From 1fbb33ee3321bf4bc8e715ed53014e61a304f1e8 Mon Sep 17 00:00:00 2001 From: mattfidler Date: Thu, 31 Oct 2024 17:57:28 +0000 Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=20nlmixr2/?= =?UTF-8?q?babelmixr2@0a17d0f343c8e8056caf36f1af79bed867faf3b8=20?= =?UTF-8?q?=F0=9F=9A=80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../figure-html/unnamed-chunk-2-1.png | Bin 64617 -> 63449 bytes articles/running-monlix.html | 2 +- articles/running-nonmem.html | 4 ++-- articles/running-pknca.html | 4 ++-- authors.html | 4 ++++ pkgdown.yml | 2 +- reference/as.nlmixr2.html | 2 +- search.json | 2 +- 8 files changed, 12 insertions(+), 8 deletions(-) diff --git a/articles/PopED_files/figure-html/unnamed-chunk-2-1.png b/articles/PopED_files/figure-html/unnamed-chunk-2-1.png index 45cc0188604238243b92628fa280868253b2c561..7ea38d451a5179c1ae3b0f266c4d20cf048e4d63 100644 GIT binary patch literal 63449 zcmdqJ2T+yS)-8%zTaY#*AQ&)#f~bIGBM3@Pl0^h0gXC<)Rzws;l&FH_AQ_1&3QEpf zK#~Xuo18OuEW6dy_y1qjd-d+ETXn5-T7=Es-%4|iImVdlj;ype-5-p9(9qD(Nt`() zPeZfJo`zUjRIO-ew%#WHU*y#>q(?)u zjYi_saYg&U-XwKcb@7H`_S?b(Knf1YCAvGOa;+iQ_NcW&?7!BY6` zpfC6B04w&dI>F_x8*BE_F5>6dw(V{a&GAL6cT_x1tEj3vQT}OeCN%HM*@kebsua7$ z30>`P^Ep2G&dh%ksn2!l$TW4=VOad*!Dwi1Zq2G)_RrTei%-5;{m<7rcdu^#=gW5+ z7IFQ2^}fYqXyngIg{9ele?{|W_)7ale}BF0+}go^-ul>+9j*Vo^VNf=|3AO+WebJy zb8?D`k6yfZk)mJ97*m}1ey}aS)by2Z;hLCY{#~!6n6!?XjaX#O-&D_`x$17`{$n7m zqwN+)6_u5DOB|xj|7sK~r>v}Rwq)sw#+0^O?Y#8qRGEz0)ijsw@BNpDTPe7H_ql@+ zoRh=FTydN$XlRQ6Bx8B@R(N4{Gft~ zi)k2H{@+cK^v?X-^nB)$DHw&s#*y=!TG{8*TU@gQY)~JqsFv2MjoTEn>UwgIuCuw#QUY%4_(|UXj_s{o_q1bA9HQ=Zn6{oO|@YX>rbCP z-JLa%8)Lg=*REY^+4hOM&pmmTTrQ%{&CP98pY&4f9>eoJA;hNr9X&ji8dZheDHC0AmU?pV_*p+rB}>ahnc_bRUblG+ z4`1Zi~iI5Jmo*dN4d)#Jmv}B?@NRZ;}I@tQQbEJR4ajZv4Z%eM0+X=lq%FE^p zArXd#hK}DJid$En-W(<2vs?1anTjXex}!ZY2|bfT?cVJ1KK(P#(xx5;Nk{OSeTg)z z4)6Zz!KJLB5$Mp2X;kNHwhz^E>f@Okt(I{uO{JxY*ti78A;NR}Np}WsT8V;*7B};- z@NmYy#&o`rnEsZN^um{p21(~1qzz3;smU0tQ#bB)8~U)BY1y)6Jat32MQ7jkF4P-@ z)T-I3srhqTw7y}aQ0Eh3V`J;eU0dHQ3f0WAt<7~C=`2-h&co8~6cEU)vuUxemMZw< zvuCb(v*^NX^1}xYWZRPxvib!=Gcq#j^qAar!XhG==4K`mQk${AwzRk{;4xDez>v zkyg@C>_-i>DwA_`bWBZP@)f%?*TLmhiIsI`T+46XEMzm#+{9ek>7iHrcpo-#eSJO8 zxcaLL*QUp)5%@gD85bNjNlD2ning03HM6?9x;93ID^4@3E<%kGryhZwdl>`g8E;I| z^H@&D_^o>7+v(4RV{#Ff<{XkO+D?$Ih*|6Z_Q{9wu>QSfhpr&CnaoKiv&pX?AMWg_ z4883jbf1}DzQtuC=%TLfoYrHd*ye%xKRSoZ8Qje>+LjBCf7a#UEN-*7Jo>%TajL`5 zC7OHB9&ur(@tWr+Z#(zU(?nkTu;?)M8smeRm0Xm(MN5`5w7Aa1$(=uc85;}>|CvNH zVX^4bVq%OWyooQ%o$1dKv$ajb$)LTRuS51FX3)xW=iHYmfgPp`^K(i)F0I#A+{4E( zAUwENcOPM1bKCOk`&GIF%{c|x4kP#Q>2(5`)nfy_$>qGt30kJiqOM=4dy{=RR2;J- zusaKq^osAz&N}CJTQk0V`LZBb*y$dVS;pJYmR)`t^6ei}dwL|!ojdZFQ?nN6cQtyrv)>EelpJH?~vws~z8R5o>ss5ZRE$WzqoRJhg7J}%$y&AV=E$D4|l zmX-zOq6@tXx91nw-Nx}7%1rArISw2cd&1lHZZ+4DKUS~K8F#~$7OqP~5(uI4PkgcR zT)#)!zHhgPOXiAaZVNLlF7wX0flReI=~v#& zOm&4g%_i!U1@em=s8;FU-tA7u^S269W0OlnuvUX=duX27{(v44YbR)0OAeRE^1P1{a{Cr86=2kxXWGb{Z0_uw zO>xa#x~ad|`;rN=;W+*IB@^W=Tefsp&xTOz;zlC3@7x($H*t@dzpA$-&&@5Q=b@tU zt8QxnVX-eFvxE6c)4k0(jRWQ9&!7L!oo*o~C+C^En~jYDTbYM5c{ozTe36VxfBS{H zE$m&Md4K%zhfvS>?gv((nG=~SvJ>*=GYfeK^*ArM@mzYt)I%vQ8e2}C)Xs5CopQu* zqH{ztur54b+uL0P622_hD>q(QM8l)I=EthnjCNPs6l^i5`E2mL;<Cs|x4AJ&QF(d7nDEKlYm~amf{c50lk1N-jhl=NaU^8Rzbu~_$TLq{7>y8}8r`CM z@!}D~Dz=fKG}A_--i6j1^z4TYB^8JuMUUD$4}be)G*jCpvPM6s|HNFjB_+jBVI-rd zq(o6dLLzQpXza^i!9iN#$<`a4+#9!Uja!(X$YT_Cw0=`eyR0pxv z%vAg7)2GTYinjUu_7{jtk`fYfAtEk|we%xb`p)&oT-dtMPkFO;r`TkQ*~GJ~-Sncf zuj+&Gwwc!5FA7=R2;3R!rq`vpx)3!b^8nHPsNU2e$#S!W7JHhBZO?MIxIagh3-HRa zp>C?_NH?aGMKB_v>xsmh-i-5TGE=#7C27ke79o4tu3#rK&jCx;Pbxm0r2$StWuHFD zA={P~k|a@|J3Cxly7snoM0Ia zpeE$S175@cvLsAQSGD+AIMmesEhoTYcvqz@KPusOJWMS4|ZmQhvu_E^$)6>)h3LW2c7lp3dhlvQh|LJ;@1+b#F4CtV`}WCEgDj)KN8vG z@;<1#dvvAI*|TSr&YzEb_wK~T&6{H~GS1`hcIG7|9U2L78dTTN&@iTZE88-Yk$Yby zraa&_tLu5YCL6z&w)g~Re!gtOn#hOsWhEswL+yn(ELP zZ-yD2R~czuxpJk%7Ms+#&kbocyZEavmay|%_fBza7`M!*&$3Hkaq8tB8G3g7 zMvC*)m8}BSXZA=3KFDu-P~H7V&hoG+b^(j9jM@<>h>)IYH_*IX zUG#d)Fu$6EB!l_vT>HsTF;D9z8;T(+2X+n)1!SPd2h_tG&9bBb#=OIjs6CNexUvVY zYZouSTD|P=N4}5w%xF^vrr*WGwr5X(m)lgw=8)ztgvoGQ1gDcY?mSClgt%VLP~MpG z1{BWbiO!Ash*xw`MJEE@*G9;GE^O;;vTNaKB;XLI?>UJ!h!X_C_u4x7%Jw6I(J5;K zN^j!Shzu~xl#u@*l|{gP-eh7@ew>t)v{)+wyF1Ia@5q&JXL9Do z8`;^}s{;A0N+^3|f}JHjX;)mmu$|O($IdVwy7X7Q>r7wzfvC2svs^rl3)pAQjxrqq zkERZpcZjpPe5+2%o9P!!-nx0SN(V=Ezun80S2{Ojic{qG_O@Gm&6P1Ic_K%R?90v; z@~Zqs_tq?(nlsT{9VX$!rgX2fw<#;VFj1Y=`KjoZd8UIGuGVWIzRL`#X4#rn)Uj^a z5=F`cl6OO526R`^yj%PCoK_u3|8g-6)1=@fmq^#)&SZA5(L8r5Tt?^VOip$@t01jYxG0W=XO%~QIp!MQ+JSq3%DMhYS`Z-94xXn*Z z?=R#CC=FN9c2TMhm-11xTn{iZZ8$eQc3e{iS*IjTR3Y+>-H+t$Iy+QooD=@Y&kr$B z^>{+4L3yzL3^3_&Z4sKAfqih^>^8q5fOZs=>VA&ky)qgQDgFyaS;f8 zKqI5VxtC?UPM>_QxwZFoS8z49R!JdhS|?wT%^DWh`1_6nZHjrZXi}xBH2|zf-z=e@ z)G$AI^PR|=J^hV6rF<>1i30{9F3McT0}@Pl13F8dhKjQ|ymvN3$)=Wg@pMI9Wt766 zxZM{c!qtTxMB^ntg7nMDK&tPH7r7r%T{k9c$=Mz zi(ZoM;lqb4Bf(}3DH2Utc9e!xfSEozLAM=^{~*h z6y?ZPIu@bxjp^om={9&H+x{<6BrDmhPbp+^I=5=os?7OmYO3Ryk)EF3)2>t<24DzE zrVX_w%g(%`qeH^`MQCVkx_Qes*Ap~Hc>;B82nlqRj})gxz8pLk->;O|sMDjpFs(8- zmf&Xkic!py@g+GH-4P*=+4@@Yv;jxrhJ^3jxkFYt&MGWZX!zq!$I(i08>Ask%MTlQ z#`{qHX*qW9N=VqVYgadNP~A&Et!RXCJV7^(MYYRBQ|pz`_9)`D)9Eezv-;8+Q!ZETaGCCo@DB52;)_PW)tV7S zt>({f6<%H(BI=ggSspT=(1M+`S3sblOK-`W?(1s_UAEE?t4f)ln=z){(9?*}b;NDn zIiyoK2Q%&K?fn90XF7W}QMa%M2}#z(#Kd!sOd9r8Eu+OKl_JyK-A!uAa-MU6N99Ns zFQ2=y4?{ot*I$2CpmMDZ757q`DGL@>=gJxN-m!Bh)3c-oDgPr9tge&Kr|iGJzcWqA zn^%g^6B8FV29u){Y$p&za)PRAy&->Ugm48W2>| zOzK}C=d<(jDr;t1%b|)KqiQdx-B>|ix5Z%TLg5!`e3rC|rj0M^!RTFQA792^mOOh_ z0U5G3L0gn$RK6x_A8=$06~jJ0KEA%bFUcvbPtsG%vW-JFMLoR2lY#pL0^Ol5%GhZj z4YEdhmo9zxkrAoH#H2cm=p&AC96BVAldRH`>%wF5`5^X1Y@NmH+*^o6D^Nl;u zEX-QvjFbsBWLSnCI&^5m=FJhPt1lxKO*aj=M&P){oQ?Zxmp2oHk}fR8@fpZPAOdo`}@o_zb`SJFtkoU8ot zW160lZfhfi{?7K8gxrRAMl`LX$Nynyd)@=Rk5-&SsKfp@+{&5Nd&nbZ@zv@jKA->o zu^S_1P(aVRp8_s#okitC)C*}dg7bT{`g{&>MLSs^^YG-}C}c8i`Z;=cYj z6K$^~FaIJ=J^crDh&=c5(W4E(bf4NR_HuK}f!CsJRMB{jvlFcpqpa+=nkHiFf3Vd* zhfYKDf1KJTgZwkR>{TZDq9#PQ@P4<9+q6%u-qQDN79Y*hi}jOLH6=e=9%a@S+_B@` zJ4tCNq#SphSEkfOg=S3*?wIROsfUj-yNwm6y40t=1rXtTex)xPL?RE!!mG_^$zodk z!NI{%jj)w~Odcf_z^m-nemMW2A~MX!k7w%poV!c3F@IpTc>|~Aq^O1ryk^(pvwkRaGO)BeMGi>XmkR#bF`(pPXC3wG2O=iiM&*QS}qrFLO*s^h> zp2QnmmK>GKjV$S4D+7Q$j9i5$s1dp|omOSwB*_nUv zQhH-;lHOTx`W+iKZ1~u19Sx3G+3!lxr{ZD-2Zz^e`$R;7Ke%&A6S>qED!j*pz4|wCt7Y(Zo<|j^E|4@6Am6Do_`dOg;p1YUiSxLzh zR7nB{ZisgOqgMR?lqmjQ$Nvr5$Nx=lyiy(Ei-c9|q}rF~ztYD1l$fp_W9Gkd3Nc>c z`0;DNhjDrHV+qQBHLVP9|9Q!_{RgyjH9(4chrK-O7yas0#n-#Pg_f(Yh=`KhXDu*R zEhDOW-jnCoQrvu*<22zYBw|1K?6mLs+drkOW1shNaw?07xffc2cuR|9r2bHN$aJq1 zL|9b{E-orcdbZB^mx#4euy$FtxED+6zrT5V&DQ8)4ZzxEzUMuDzk3vCt1#%qe}5BT zRlCF%6&RaDp%<%Pe%q^PwzI!Kmjg!|CAkk9aEW45ri~993Om#s9A}L!ekpA4igI0M z6crWOBqXJ!m5U!AD5YGnvWjCbUibSY5cT|lJf<>aLPYL@Ib}WkPu!%TIZ=aXKK5x( z$Qkcm+bkmKY&xL!B;h|^@Mz|HQ&xZGS(KRZ^uEs` zY?P*+YSb~LhWXf~v@}~fBKbHqY2A-H3-5dN6$vjANsWCh)rZb3thR@IGdwcFl+7FS zd-hkyUrk4ZNNN-gl~Q>_Z62^b_EV|c_HEwi9!_rpf`Xd1@!VScE8T1_54k#ldWue7Veht|1Nw6qYgTI8!4xYY4-&GJ_ve$+ z+iN}Qd+OuJp4QTm5|ao1Cl>vD{nZCuEQ88?r%wXw*JN1g zO2~_eiQTBx_k0v1NnfdQde6I}cOj#3gXtCDKRMljC!`{z{MWv;wnHz)$ z-z;H-t^mR`mh;1&7?p%NELy5%yI8T&%9nF0PjwhF!H^w6MICc?l`5SBZEZ?CHnTV~ zvXYLDP7Xy*`$U^_tcPslinm{al{F&3f)RQaXs`I-z2)lUp!`|%#r#QKyk-{TjBQWN zClgj7`&YS=e{5Nrz0w#Z%9W$c%6{7=px16!@Zh4&2Z8hIh!FYoda&P+Q^W79-=j7p zRD1HL7eG_;qtQp*$Z#8e=KBwX?>le&qf1xwW`oGe&heD50(#(WEx(>3#Z!y(h}4aE zCOpGRC6opFSwz4ax1qNO8_+e)zV8yY`LSIHFN9xcNHdLrM60g3=4!QUx>?hs2M?Y> zx7=xRgE$kmJ%P$sYJ2Y7IZqdC1*0^G9@HTjc-coAl#T~sPjyJDE$W%lH5mVDj9gkamJO4R^FQbkkUqXYUWOt|C@`6;-qpoho z)&fBdDTY1(+o8a#s9d@sS=8LxtQ85l#os3|tHht@EQE~;)bMc-YT1i9xVR{HH*%yv zlmg1yw{M?>pQi0ovHQ$XI4|A4Dq59b6M*xMc}+A~l?xYr{8$0{KH=-viv;vzS9l`K z*+6&y-e8=d1m-#0wg0tMm!y2^Ox!SjG?IGZ?P7w{*^vyrScH$5eK{Hgp;gNdvLjj1 zjU38E*upzQDiOp>KjdC#8^YU|?^mGG!qI49E{aOEbA0lK~)Gi!+mAOyM+b$u|ViL**_g>2^tgMje(CmiBbZ0UqT zB{Q>lLRWzVCPGAQU8mYS$eB?rxxZJf`T5;eL6-bajT_L2Yq zln}$)dSL(l8obSh_3IV%PANkyB5dUM*^zP;JR2$zB}K(BOi>Rgsyb+F5uj=)jm{tbYk#z{CulYb0-x`rM`5>F9M53(eZgg8Q4SqGJcdX)#nbc_0GJCLMGStOY!ZOB zJ^2Sy>;|+*1m>w57{+r_p^();9o+~>1{TZ(@X`@xi_vDMfo}Xr5e$e>^9bBBXS_Kx zzrRrPTfl~hf{I3bK(#MUuSN(WtgvI-t<9t3jpi}new8{0$qbWy1x?%#{A*9uOAjzX zWPiEMkAyU&n}-lq7Gqm(8gz}t%7)*4cH=#pWl5&9a(*t93Z$r|#)9)OD-jNia2lJn z?2j7+JLp%`ZT>z^J8o0d0p9M1`eU4sql`mLY&*>^6Pg6#6P^GWh>$%^AniL1G?Fge z!H9%~M@kN(;0vaVjSsYFlf#5f6YbpoUQ=25F(qqB$WhA^@~5}qU!4iA#S<-8SQH@7v0e@@!pCL)qVZ^)DN*H7>CsAH6c66BKyEJp-}42Q;lsA z6B)U8Bo(d`W3V^~n~oj6DumS_HYo3!949;CvMT1vtAP_F<|Zxid$1XTVpeiJ!*p2x zoUZ_xFb}TW^W=Qo+j5wMewdrKe`ZZryH=;0uJR@MRXFR3B~7{WKsL2tv-Hv%c?`;! zz2vkJVk_3m2(@ubi5)bumQr&q7oMEu5HwwJqdQ2uJqReLIe)H}RUN`L%#yD-GnY{?bKK zF8JI7NP7_RR7sdDoDFhta8QE+!bZqlWt87j4M?vqu>-^~QOQuGnLx&Ys7WlN(ptOK zbubL8ysGOMNE;H@91q$^_K&V0q1k0brov!!8%36k1TsIauC1A7a4x%~HJZCsGtV_g zzdTs8&luvIcNmc%af+@vpE?^P zgH{v{1)SsX;pC#h(Ug>wOqYYAFt*s#=gwQ)+6?2;XhYWv56&uN-s{S`ur!TfCcmdr zw5p*?#pvX_N2#Zq-SzUCn2E88PuP<6?qLsbeMG~j%BI)aADc)iF8@I&*!Oi>5Z7iG zb8Q8$d-oKHrx4~l&{zJ8JdNv{I~yMnjm&W9>xXHegBwX@$0iJZWKd(xd9#3HMzoh& zR4qm7PHU#MlgRFZjUzxfUE+5z-mB^NYxWvbgy0(TV@oIuqzWihQ@(42t|swLmktSf zY+C#P90^_`(`RtD2z>9FbMo^h&q465I zF5?S~SA7nDP;^SlBV&WB;K#TqtbMO-murK^n{s-K06034nPx*(wqg{E`kH%y09#B~ z%-ndW5=ELau){``ZxNT!H>f?)lP`<>n0#Lwx8&x0{rdG0)CxfVfV|IUNV?wEeW?`X ztU!1fWQo$CO^ucrQZo!a$6sR+u6EF+Yelpb9<)zCZ@+igC{L*1{d;?tIBXuGlNCz&0%EewalMMV zZye$=n3{Wq@MLA$Pga5y5i}cB;gwlL07u^R&Y~N3mSO}{bcKH4nRqc zPShq~xmkmggX1i#%jiSMTgKV(?u-a@WV*%0M?5ajXykpbH%WWdFwh1$|Cn@ykEuF6 z3e1v)2p+?#oBZ~3ieNyis^npfot#~`IO|0i z#jmBhDNf^cQjrxYP9ZsswcTblLfGgWvCYy193FSt`nEMw%c=OFGYES6cqVD$tRa<< z_CRXANoJ?N@o1g8ISKHL(7sTg;>hg!q^p>4*3mH&R;H8KC5C`*J0uyAKn1#fNvZ%E7LAKR3$4WlbF&8;y{9 zb^DG1fhA2LKM+H#%~aM8#9&Z6#SockQWz2JD4A?fUI#zuB~-RL?{2EOanp}JSu<+C zs?H|*c}Dzco@H&Mc8tCfQqWJN)D^7G7w%h2OGs2g*)h!RH+^*~qg9Y;u+=}k!bahU z8y(}3Gu|b#d&;ziACD+5oMG?7*V`?E)+`Gcg1tR<4uqLgPW z@}SMwCnc&WFSAcA%~*uelhL_(Y&_M*jk4wYd~7lIw}cCE z#Vw}oUYWguG3DZJwR(bSmn%2s;im=&qjMdaAz6=XHa^5rEv8uY-rDwq3$qrg}tyy_V$+RxM`CShTt8BFuh#wzb2p6sUwNJ zHcj^t_8h<7*+WJ_baC_SAu-SG33=3*1l{Tus^UpYO%Dyesil}8S-}aX@jxiM-eJVN z1`(V@n_5f=DMtu$22*VMsfPCLDV!$AqLl+~3r-SMz-42gX&p;uRG^Mvh^VCBQ0d|| zboya_e}SkY;mw)RdfjLy(^ZEQnkmHbabnoWGLrM+1-&u&g+R=aV$pRtmuKex)Qb67_3`P zbPjWx|3Edm4>kvW^nSeQE_0bqZ|Il%>RR1VBzo4a|BEVC&mDAG44fA=5dpEcGY9WS z$Z-)xnLH**`n~nZIpa<+0mQh>BZ+&n#i^xAdLOy;@y`T4@ovPiGQj~MvePo*rj5yV zrD)o9lejdo^D=Qg;K4|mCUpc$f(`m8FmoinTvsaxW1R|LwhP`H1(jEcgOfp7U^n-R zg!o2^irZ{E>li>fF*aoaN(yNi!#au?iUsmXX_#0puZxfhpIcX|IXFpeZHFiD?9ERW ziW(tf`X4d$E;R0INUer>HpRNT>RF3@{+M45@;XIr0981twF3CoS@XHh+7q@#DL47Y%lQFaDr- zS`r}uWEeGXVJ<;lL7_HMZYQKWX`Ik1xU?^p(W$ChTc<3iWq5{iO(J@wy0IEXbbg4Q ztVgjQmx_^5v|5@;)N0O*V~}H0QLr!m!=CI70A54zJVe1I7CGr40Yl6x64a$sJNZ~+ zVH76*!cJ)P+3_?>QZ8OLt2#IvSo`A&<3$vLZ?*Z^hzf)9LE?&F{R}GgTF$ts^`9jHugEOp{Gpl6h zvjj7pZzDTH|hr7+f8PkaapU_@hFWYCWk?AWu< zv^iUqR2nGgJFEq2>qu$n~eOgifuu;~`Dw>{}OUI(ENQ z(jujb@56`BiSBL$*NZHr&vPU7efP`fUflP)8(hFHIl?kL`f;aMO?P~*s;Vk_h#m#I zP8(|4*6SUQedYQ~x~-$~U2D|@nF=}^90rJ$IL@v|3bL}>pi8rA_Z){xi_o-7X|_%H zb4#5-EvjdbnlgAOl}nexYCCQQ5%pI7)TvW%__}V{L7O;4d&Ag3_+x1)PsDx;)(*#L zRg`ORbnlkPY;$KSvDI_ex%~0Lb^*(;H%YBLMx{_D7uNqR?>?rT+`DgICC(jbmNJGD zZP9@n{Pv4(20#;id9J^Tn8WVB-f4w}ToM1Mg$Gf%$Ti?2>!WN-(&7y~!Mh`tk< z=I)ZYi3NYN6F~t1#;!4$D4rueu&#!Jbu)|*-E$KyDzJ`C`F#ZHA>1|iIX||hE5!$R zFztJR(?LzLqhCR{nT6)^UZ;uvT9l|fXr8G4x&m#2G|zb9VBl@U^lHrz3s)Z!Z;i3k z^BvZ^{g=p|c6H4)%k17AGNbL#;hTxnLQQ<(uFK%=ke}Hz+SjOpSQei?Mh}QXjNWKE zN@X-RxwWv$uboGuhxUEcY`ydzRGi3V%HW}iqba7Ch}|cy8&5w44Pqtf+e2i5zleD! z*lpe+PAf+ZwU81-QRIPW;?gC)Ggx$}Hi`jFW)u{2TIR(oXv{$e1v~Zg#}wo3XinUP zumqD0iC=mK2F2ah$}@3V6IatgUD2t&&|7JL?axhMs9@$fuj#HQVABJ)+-_A(v5g5i z;|+$1(r&kkhZs3+mcQT(iF@vqXmK>0Jm$^ESP5u^=b>7FP^fJ_$15z{WCG_G&n1V) zI{z3*XlS-`iHJ0tutM8u5b0lJ4x+6mCtUVFq`TK%pW_a+n!<{cy3<+a^e<)VMDWbY z=D@%}_5{DjkNw{cJT(YbO_=Ckwd?mQw2&9;%Sub*h8?le_0s3g{ZHbXO5+AZMpgYR zM8*F};4@ln4?^AOKNQcKHN2QSNXw4izP-JjDTSBgw_?3IzBLUj1kA#N_NBtD zHX#WUisCyzLcA1^DOz!iKn6Un7AEMc|pKbSU|2{wo{2=(aKu%}NfBfnl zGz})Ze6QG^+{ZF_=rn1QBY$o_10fL5hr;~{@!KYs{xFk~pR7C%l*WCsIVI%?Id1EA z{?d~7(=zdvn24erg0LA@xKiu)B*~GEJZ~Q#n4i9ArW*ajK9JA3fiTbnUbnLy!D#)j zR@Dq(yVaM{RZayE&K4G;5(+Y#p_1z3-|DS05=u>?uDFCewgu8Dg*ymnl`g?e}V(guehK*Cxe_2Yx-j&YpeyqKd4L zX8)@f0nsDv-(G|RYAG-Z#ui$^M!88z_qXRsr%*pR3jL8R0d-o7x3{ng#OZ9mcM zS-eO~i3fY)nxEq8)mKO0_Dv2740Kj2d+YRfLoHc_W7{0Zd!qe4mN4|6_NCeQ``c)Yvll|A zOIqZ*zdLEj)3=5qg`o)q;`qP6jWWio(07UUv**7yrU*&3h3#fn6pjfd>7?KT;SZn$>tMO?9BKjYA3ciOKt`aKAxHTGNHu8#TrO!)9kn>H04r1gki^y_6Ico|*3EYyV4 zwKPA&_*EI&{K$2KlIN;vew%qj_HuCeRCmV?J41Y4R{NjxWgl?&+S=?_zsw`8OU!rL z;KP5%Rgp-udCfa`&8A;#vjmQV9lz#b#ktC>vM4LbOw-Uzz7hUtiY|wG{iR&7|NbuN zWfH;cZtS4({ymBeuU1pE7p0c-8@pfr=>=Fq=lN@LuFafjB7xU5{PNY`5tTJ#MK+OH zQp^AIwZ{>-N#;7zc(3)=zh4aDiKh{goARS2XMQ#4JA3>28Kl7|f);|^clI$}_!W%a ztblz>Ul0W+vYqeym0G|3PLa3Z2rnsl#0D+y1Zs2`N}q<^jbftBf0ZN@SnzXbSJjBk z3pA8>7=piqJi3;5Nq{$FihFx#Y4)21-GQ>u(CC2$ue{w47o7n0zjX2Oif!ArF>ZXc zl5@j`*libH8lCp|i|K>sjw4Ewb#W^e?c=<9djB@HTdeB!*H>?Qzw?YL?c(M4mVR6; zapugn_xlg$iC*dCJ*n?qf5_)aS37grl*K^Mj6-=B=TrR0RpjplN}WXMZ~O@Hp1s>` zm6n8n(|Bx)?qare|J-VDEG~EQ>*`SFYAHU6B$+|wpsRF)dw%_puBfPpD9}HKJgr@$7!%{`&<;(xK2xbRRWBOZ54~CIPp=pp(YcQ$M|77DYy>?1F;o-f&PnM{&zU z#e+h_-6=t`Dn_|@-A+cnpF_I39-0_z1CK9>cE*R=MZ4ZAQ|3Cqk z=@dEJN3DIR*5#Vfcc6W7arBO#gF5;U;QCQ$XlP7a9y%30hp#VNZH+#ty!XpKKlt?? zAB&3*7hzk~)=i)UyRn?kn+@VDkp3t0#6{~?{p}^Dc@qW7nn6!|m6p$^FnFOE7DU0k zXn$R7@@tNzYwB*Oc*kda{bSiOrSN)mpvnf^jZ=yUO!}0-M=Xa>V8@Ke}0JK)*I>Y9ZSR979S*s&>LU<iP5Mcu94*r`+zhtKELT`-Q~w_gJzq+(S>#NvH36>v8PY9yltCZtinW zHj#eGv^0r2wr(iUHjJBPH?Gop#_|;G4bra;-${dQQo%y1JH_tF9zTBk*-6V!SWeP} z0hfgE1@4-3-OIYEYGXADGKM`hQPEJ_ka#`?6b{(+?`UkN$1iA9fENTgPwL%05?A0Jb9@m# zWVb5{T0!2=q9K)AKU}OSZ1TBWYm9_ejXY4}G6il;!hLPoGTpnTWHlvQDxTO(d|98BHU%g2knonX|TQA(6Y0PuW12Gs+n(yF;Ir3Nqn;J4J zOfRHS6s7aK{CvV#Ho>CPgC;n2OZ7Nq!a#cyS@?D%v0K1Pkqe~o0>o)UoE2X%v_wK7 z6(o3i2@U_HdGR0&@ubtM$ymcw-5d+@)0vT!oJ@=Z41a%7H=Q{pf}8j^29MV1^6!47_R- zJv&ihVW$Wc1#PGn;?3=ZRJFJUtQNU~10`zl#l%P)HAdL&poe!IiRzUK;tgh2FfZF~ zH}xVOC)lf@Kq;6-^wR<*G%Gq-7udXdxTE+d-6JBR1!%=9a&_~@jfUXuh(#GXP7Sym zBC){vq#iee;=$K;pg9f>G^r2~P58ig)5b>q{Vshe_T$tvN|(XjsL>`a8rTxpB%H>3 zEoWv#TUV{izPuq;Ifm#jD6kvMrqD%dgIbAr9S-Z4z&;QdR#eDt1}1ygo;_4FL6X}b z^5^1Jlf`g6YQ49R@*a<1kiH*=C~fDVTO=EKZaFMQZii7Hc%CLMXwu#x;P{B*V;^0I zQ_)+e6qnzWHdX*F`2}k2eJGsI+oICjD(pyFv!&Mu1{YjTJWqPZ;PU-w<^VPm41VgB z4oO_qQo^}KshS}hmKx$F=`FaJ^kD&EokK(GO&bH5!}LPFm)WkyrN`6$_Jw{G6ti;sf68yeqmw-+v5qKRYzPr)Nl zPrP?ndj+&g8aYd$aGM`b&)dhxr{XqU9YU0!oZ%u4bVpT#evBUPYn<6>edGe0edXR* z^>k@ScFH&&XuOX^^%{dY&$MW}o@U+>4_}1}l%!g4-H15gLFQ%m{s)`RO1xHX=W+o zmWOOYd4BNNEV%W`8=thX8ng<9qT(j(Evmqtisv`5`-pjmqU98)_U>aN!$e7vkox>nny-p(7l3x#(4kF_%HcIV*Q9Xm~0y-lfur*oa({X;tfX{HOyIP#^=U_8%DN{Hi zWH+p7buUhP5L;TKXS*Q$c&LZ=g4xESXMVlQap}<0N5R4Ih+mp;-q)5qJq%mb_hf-x zc~f>02qmqCzKMe(IXxMx_(VM7p$E`Yl65V={-N&CE94isFrRPRN%#`Z7{TBa6($UM&kPBJV$aF z6O4iH-n}F8?k5voChCb3C#-Sh$H$9_mz%R4h;mLmN)qxoPI|lnsc40Otdpqrq7BzM z`LL13wg$iHw*1>F9y{6CPGP^-rWjU}(@$)3keG8_v(`8;%E-!g9BeA{eiED(w+9L} z>DxkeKs;c!!u7Z-=_S;G0o(jblRFBKWzIx9GiUY4(t?zc!`@df9KiCBo&*bvS45O} z@Ze8fO_+R%r&<_2r>Xr05V~v|pO)$qL8bw1?GWa|u@md*K0NUEXP1Da8G6U^DWs9R)l{i&3&YA5aB*& z@ZDAa76Io;aheIYMl>Y~3W2bHB|(E-rn-o!^!T zR1#N$KNHZP}>#~bV708p0-+*}^>>ea!D?ME&nzH7AD zL_SaL!#-?k5ZSx;41}O~!mYy7A_L$+L_rPLxeqcoJG;8FYW3;jT&U~qyiyZB3mCyW z;E+wfTQxtS8Kz3EnA&J~KkIj`k4_TiOhj(gRmDocD#adk_CDq4GP2H3l%4<~+vzGM!T2#hB&w$_JzGumm6g2r^Sg zF6PZN4KQ=2?{bBauJ>`HyWgwQZ)yPm+egnS(=i{FmG7|lGfh+k79Bsfv4NHrPX5d5 zH*E^XK2(?QIAxw2`76Au*t~B15#E=5bsTj!r5%aBe&a@MvVn|56=~yr6=a(D?c2Ab zUj~-Y3$>1}IEYDyb@L%xs5q`;qv^@R6yDw%cQx|_z1D0`y`v7yx}O4G;ah3!H2=!RQj=Bn3?|YV#rmJ>| z<0cn4!rB-os&O5~?c$RsPwrB>@1f=!*QBvEtGd2@o*WlqAL{+gO>iPoVL>hzOGrv| zbB>cSOoV79#vN<_-n&9~Dt+2F|3$52Dsz95D7?EXcFu(2nXFxf>rVpfx?_w*goHG_ z(LtcxZi^m_%%12N=6GY!`am%on3-eGavC0GokJeLS;D`2$AvfhdO1n@%CsE~*uN<_ zc`W{gsY^o7=Wle~!AjZ|1EXsu$}($Z2ZDmXc#zZqpi#bOY{jx=wTw6VzW8R?Iqs;K zkZu%WX|5@iD@@RWXFF^`^A*o)`$LHkF-QPMjwNOEL)y6*!~EC#-Y+;Zyt@EvifaU9iL(~?k+@wYe7(%+p{D`m*$LeDPM$Qq zb6%xA?@2QK$+G!BKReoGPK)QlSs+bGHL8`wwJV2rzep11H9dQb|DvE4kMQpA(m~!` zg*DYyAJ^wb7dNF@-sUUh`jZmMXOds4QDWP4G|Sa!=BiS)O-GDzv+`|IW=#X4F&@9M zoL|GPr)D2fRae$WiTZVBhi(-OG1*5WF8oVQ19Wg3yEj>h8F1l*`?%a+uGdighP%Ji>|*}wYrO6 z9Nk)DusiM*71fsSVCKV2L=E`|qOOAxlPfA|O`u-sF zs4S9r&?sQ!puo3^oQU0l#3k`1PU1#XFgPW1;Josz8VgLAumuwRa#Y#eC$d^&K~$(}q7$ObAIw?;~C5X(axKoN~wj*8Tc1L+5l8=^!hz=;vHXjNoMVDCW%<1&q zn>jUJ1%$=1t6zzSVaa=b;&?>(5Vz?-dibelf;N)DtO_#a+hr!BIqez?wl*{89noF0Bjl*9lpK!`1$n$TBbgdCMR--_#HA`!(1YQtEck?SI@yX7(cf?LDs zBkywC55*WTt*GMN=br^Bdz*xq`~*@kt};-8RKq;(EC{}Ut-Dg3wU*pJh%MM)GcsYD z#9)xqY~ra@pZp*Fpa7?Rk*C{5%6xP?<=ff6Mu=8tLVUy`hnrBDPU#86R1}Et<)c<|tf21ok z*Zeuz zTyyjU!4CxYv2_Er%A-lKF@e|@QFBO#h{S;rBBh@9w-icDAGex;CKY#3^x4wx_L0B^ zPmjLYYZRfAWGAO~fiCr0XYX1IGJIm%VFP;A0{v54M@-i^>aHG@n`EFHo^5O_Xwv|; z${#N&E31PAur+zo#Kh+)NRoz^eWKIfcj|u0iq4&%HvWNDJXt~k{~s&Xr=>NADM5NC z5%7m=w0X~evkRteh3mv~GdoQw@unxvVM1!m(;W7M`FH8kINw>yT_bg+5UWx>_K=+ScEME>OBuUCFxpMBhWbhbwOKN8dn?fD1r#O_w5|6g4)ETWiqv>PMg|73Jif<4PnL zQ5BS9l<+@H3vtioQykW}6PGIF8g@QDiv0q921daV)*&{2QF>00IdNFc=`mP zYZm~M!{F+v?Blh()i)_fZF5P4>IKiW0xbHziuDSk>3$|3yOGj|w0t%SAh#^{_2I8L})@%&Bh9RAgo4fLR zWtVAVW;-<5O4K;9L~p;dC-{~6X6$m(eM+wIiOwmjW;lH45CA}oI2z-Re8On z)bLC#moX)R)X|kdq!?IOUSju(k#ZV6Y{HU*{-ye)-wQ?@3Wx&4I{*v;!`3&~NT`QU z@NL8%FXRCbk2{}uVg)_Rk==^X(;qX^O{w>Z?VFTggu61V-VKT-3QtXJr6~ScOzV?A z7&6(fYlm4b4jwWp^d~vo0!8pL;4^89fSE}JuBRsdjwB>vM>LbAqTd0}yAR%3I4m+d z9;^4R8@*p(JC3&HN5nIZ4*;bTRyPztJXO&72^j*$q!Mi3k85qw5CNB}Q)(YN37gT| z=mNtg=_UfWU;{@5PQeIwa1dhY`gJ4M^Y5Gf+lsKz{bQqXE=p|Fj!L-Y>qdYnZk1@I zyy)pVD1I_P3GFX>k2JS9Z64g{Q(M{UN<))(_S`u|3=c`HtD1fZ*(am}kdUdQbs^uB z#ckFSQ`FtEFrSVp%vsq^YO&lN*^nCBQgdk1#J>vn_>Y@Ags})es(p};-H+AHhIU`d zi~buvfVBt`;aSoJcy=jOZR3UwVYudLnLwa+5L`x}lwF;pmZ0)t#|;dMQzvN@;Yu2CbD_qGG~{`$)zX1!kmu7KkrCk3~zg`rqmVGwOh)cO#(OCoQpfj zn8ATsh#fc zGBrmNH}>*88cBWrzWa{*Y_m`f85kj*;29;?5B?AK-aM-2{e2(ak?Ej}2Sph|LhsnRSyzF?p_jjl4SpCCQZI3d>w%%uZ>aPfyEF3CE!gvr|b>%l-| zY@32F*crUcEa!m~>JptisBYNJ77#Gbn*U|4d1C?D7xJ3TEnIczx$l0kv#y;>+Hglw zF89+H4co*z?Bi8&PBRm-LFEARS%2N_2XDgXULLXUKJ&(%+XRQ4_#_P|ATMl+{zG|Y z_6B?(eqK*fpOHX2Lk6sG%mrprMPcMG*CDrm^1#bW!^jpe5B3YaeiD*(?$&ge`p=8- z=$|wTr+7hvgQJS+{-Fv;RQ|u$XFglE+hKp$(L)|505tx+_(BGWJEWE`5BS_mHn74v zrXMBju1^ZQF9=j;I?g7I8_e>%@$2aQM^Jc15NPNj44Nngib}lP{IRK2ZG#pU~Ims|6(_IgR_D%9Ft-usoie z=4WjVp8q0+AgjSClh*t$Oc|U(6u@5#lJFBv^SnqboPuz1t__^?6yM;$X9izk;Q&9s zxwU|epOnMD;D3HRB`z$Ysp=k&qyko1NzvHB9it_wz>6RL$^)NGi20|}cCfSDg^)G! zm))$|?)%SWoW=IYUkjbqZdUGXbr$$4?5=wZL`1{4sT_j2$5#uCTakrD;i4yGHC_1k zMIO20=*Z73M=;3D!&$$S3CkHoVUrJ?K3V>|W+jTMfr}7XAk^phsACQZtyr~c)y-p% zZY>!t<;5&qxiUkz{@b?(=nh!B+800n=iP9mT9W}hkyEt%eO*xf8*3_8bOYlTYK~7O@%8cX8*n> zKZ8qMDA??{FOV?tcRkl(a`3N$k02OkIHS1s&x?JrjNxcmeeU&_e=hyZ-SiK@ zqQ}fg2z2}B?sGV#gDR( z!qVRrTPIO(WtZ=k(@*|>*&Hdp$6Wt1Fis6APQAZt#`y4=36xmg;12w!N}T&n&f@-G zQ!oCWqGxj7O{#2fZ)a>Dqx#QaHhf()50l{2KO+?R7Z6=PJ)$IP0f{Lc_S z#*Py|1nf{+;TFUFpHCow5Lqc5dxsENaJf!&S@3%Y+K&9%x;Y}9s@JHogdiBX$TP{M zeCb6h+tJU-U5E(7@wW-mQxs;%jnc<_Q+{7HW9qEY&tshQ{r$xXW4Dj4M+Tqp=y)6$Q?Q@m!RL`24xsvY>G=$W)uNNkh98yhAOx^3 zlqH8N4G=PO2-qDaQpc_bmPV! z$ov*NOy^=4jMn&fu>kZ1O`aoKvU>NveZ0!+84UH`@AMc#Fc3x=j>D)(6ldPf_WSkQ4X5HvuAq_hPY$1E-;-kY|5svgs^wVOk`V%P^u}ifGpJH z!2Z{GWI8DHK~HirB_?Qs2CBJ_kBB(pWGLXkQJ?x%U^xk;h?&Fj=mXPqRA7l0Zf$bG=22 z7Ih#v9Dix#jB!y06LP=n_3ODvLpj>qL$p8ejVA&Vetf5cKe9FhxM@gKQscC#VD#c> zXH;z(SxoDC?D|(-}v8u2-xSdA;Ux4`cz~medV=_qhEP*bqYVs=3bU+ z8rc>KBtYS1=QNp7B{2HVwRBWrRD{wKEuUs^nIuWxua~ZCFQD zqLo&m-x_>AMS=KHSaLujrRnq(?GH(oCWVMs8+m_UTiCCvP|qqk)LyRTzlS5#|Vk;XT6tpwBz3ao`eCc8eWSuq{{89mB91-L74nF&>CBQTYleBbq10 zH7~$SVZ^wN4%vH@pWuWn{K0PG_?=|giR_c90<)io>9}LT(Tlhhymj^4w{Ht#i~s|Y z>mO4EcqS&{fq)1uM)}U+&Cp>7PS@zhX;ZSHad{^^1R0L`jG2trrij2J5bd9O`}cHU zYrJN{WOPWp62!a;j$c{e+Zy6;D6`wVJ|4I)J$xl<7bO2fp>(R5aHpqy&Lhu8vX>z6 z5vWk{`T6EgH)*A@W^*~q`bGC+De}E_TN*FT~YLp?= zy<_Nk^kW@VR2wtk{arm_7bEZlXIDI``#A9fa;RELC!eqHLhPmns05Px z0!;3nlFEZ@7()`1K$5uO>RKc})U&S>3IfCojJ7ch2Sr@LL2x2ELHUOp{a#XDfos;& z$Ia;Emb1FJqmks&2(f1)glkZqf{Jw!B1O`heLptcA&2{`-5eBo09fs}?d_mOJt*bq zt-Vj!27ru)aQlG}AvVkrOg}qyJ#rLKfnJ3Cd6M5vWjHJW+DL3-m!TZ5jr$;pUnX~+ z%h%g?f{C0vO1~&xk^v22wBRHH&i4I+!aSnbbx|UqkwAnY2H+Y(s6#0!DLh?dzfSaw z=n4xjRO3&s3P;9`gdPlV@>B(({Hmj42wYl8P*I4ge)QL7(=7OljJz;i&dx0gVAoL_ZkzPPqpk`fGnA+gKJz@RYO2Ve-TZPW}3`+I(xv!8lM zyO`rCeFpUh6%+~EUb9Zfu8O*BK1vkt3YHCbx^ZK;c3<}8J&bAMUV)U;n0Is0Rx`*9 z$kd6o(%ug$-Ne-8`x%VE?U$mxBaVdjLMlWEXM8P8`HB%9MnZE2xN|EqVU{1^W6&C_ z!ef<3aMS6H-L`6wWbDVyME;cq0kSV{q;z#AZ2n22No{9z)0}f*58hN{6KM2cYnfH7 zt6fW{kaut=@Pc^rImOk+ebyY$d+1=mZZKXYW8D!*Gy`yA112f$(|vVl6M^86(9r7S&a2&l_Rc>P*5(bj--go8=K`7Agw3IH^U-UqN+Dt-WtG(f={0YyH) zNy7e6JCnwajt4*M=AZKrYy9DdYC1dOcE7!xb?R-4j$fNvbAHAxi)uOz4V^WL=gqBR z#9>1S1Oy%6McAa7%L4uU-X2QE@+7}9d`~_i5_Kbb@Z|jqES7Y%qz)$Q{fa&tAXunV zJi|LnmRg3SVVWHL=p+BdxZ<6?)=odZ2ApJq{@L z5zqqMg$8N_hm;Z8V9n%wS;3gNa7IFB7C!719ypel0bG!YSqF;aV+4gK=>~vMNZ?#1 zS6<}7w}mKnqI&_iY{4=s*fP;tkua%DIAZb)>z>$l&|C)q&wyvCA3lXRd}QAOr78L_ zZ4AObVt0P%zp{3UyC;S%Z)v&?2Hs8>w;6yiI%DR{LKv*z{36~6D2hiS{r-*?Z$+&d zs11P_ME3(y0faCJ&kTW6FF@}Kz8_VBL*0%LH!9pM_|d!gEMcHiu2?@S1H2^p#%M_MBr2c?33sw)QSgUlU>kynn#|!fIur8 zj%El|SAuP*u6gz<9s}kssezlw2Mk9MZp-#yKtTP2jrBL?d$5bYgiH6oaTrTHr;_Wh*oQwIAC%?*fDquw)PR?L=ogl7XEny*+AGq zXYIukC*yXqaufedeLAE;tVUg40*ruwzI)hXAPWryXF(W_x}kl|z&e+zWov>-V;WFa zfhQ)7^@y_fp_U*3XJd}xG?%vPkAc7SYGBXgbE8t*{hjtMsuTmYak$4MtpYpQ^`nyDv%6aHBpV)o-riG|NX(W9iG% z)6XVPnv}c1vV0M~2?^{eoOy9It}-0kapI0*ouIV!)~G{ho}P%$*eTj1x-!SO*aF+bB|NwS+14uVYsiXSPK9!Jw!xf>rLzS#Ou7U z`7hj_(=q@5b9?kp>nJPx;+Wgb!nXSqD1fdO4ipIr^2mo80$+fsghIm}q{GVElqWWG zlt)s+;gKwFX=t%6*Qt*Tp=V5;1G=d<8krQH0-T(IrYC?E2oNuM2kqB?(tJQmz@|bT z^PC1@kbpl!#7s54A$Xm@GS# zfca)7@NYOx1{@2*CiC)o2qD5!!mt~^J0kc%#?tj{soc?~aVC*R_`jVEZ#$jXeoEs@ zNbi-cp!_kGoVs_q*bek>m#%DVCRS=Wh&|k)od#=Aw-k|1Af{mHuI>#Rptj8ipc1@V z2mG)wh$$J{REN&AJ(E%MIrzl3;zYb9g3%K=Z}S16mlIn6}~1u=r#?ZHP~my5>!+OshH7XRYFb5+LEMbqJ?-NEDT(F>#nqN4v!|O_4(4n;Yqy)0cGW&ThjiOb36gf$%ukY zl|hm+!bP)?=<(aP$3DF9+~t75Zhy%cf`TTp+Rb|a#LO4vojTQBRZ7%AEy}{wyjk>w zTi-^g6hL<%733Ws9}}USr~53|ZwlCp$RHAZIH}3url9=%8umx2Z=yPdwZ>&uGoCC@ z7bSj;tkF)BRc&I__(L~aA)o3Ntd66-JiPoKuBVLvwiU=T@u^=Klw7r9v1>3v>f6tos&H!Ts^ z{9@p-+HI%hXW8JbqTzu%LIa!+d^%UqnnILGD)!WY3y1^LRt1I?sd;b+EyWA)z;COX zKF+haAX<0I8GtA8Er}gU!4mEjQR8934X5v2;}vXurF7zZ*^G$Rw>oNI*QM5G&&ZAv zxa6Vv^|Miszqkl&Zl3}~D&6~rESlN&A7<=4o&F5P;N|Al*0PCGj^(u>apgw*mR6?C zW6q9*Pf6_XpP$vTlekM;7i70Cs+3in4bm29_u2Ojy=c5*C~>yaZmC*SF@h=Lb`p=4 z7#~_UN)PTda?7j4#E%1SaDJGKdE*^0pGA@5$TW}B(IJrxm_*gp)k-!_sj-NLp3t^{8eG1xsUbxQ`uDP?#zlfzx9F z5@Wc>1R^Ne5SW~79`yXVGSDxZ6ARww; zT>x9WKun2fZ9^2x(Dug`SuF)-NRRZ8!GOKae9=^BcpKIRbQGhG0S81P#m)vSg2ed# zo@Jze0Q69q9N{F{_eQ4}B(x;&0^QxKq`t1Y*s#|b+b+jK5i^?-eu^AmZ2XLDP~quH z`N{4YmCwf|?FSRezT2b%y+gsER{VshX3WuzObxXtZq>s3Y@N?u#vRO^Pw;ZAu8sL24L@Xr^X}+<8&%{&?i}A7RTDoc>S& z7>JD+su(2EQG}x5?jgiN!SHq4j%FICX59|KErR6UK;}q9 zVFDP5-^BZUBG(bzQ%FKW!v0s29A4KPzK9{^@iS6cZ#i}D8U>cKUn`C_V1?lVa@LZ~ zJr)!Ly4&9*haIXqBfCzUQ{>575q75NW#$HMhvb^?QMOuzB!(HXk$08kuPK}(4k7|M z2NLaoJR(t6)dbAzWBcp=?(pb$Cj2vtZy^{yKVQeFGtTACKND@?7P zpa?gRJk&LG;l)t!hYB*51c}(V^W=*GMs;%%H^V_5PAeyfkt04o9|?Cj3c@OAG@|xo z?_W@3y*=h8fBQqh$ue$-p<7j}-p^PH-wI6M`)qe3XKqs!(A)_*MBr zu9f|)kGjKDxPsjggmurF@0NH-Q}guO+Dxm{av28J+~sAz%+-);3o)CD!+}PgjqJp3 zuMEGGFUMcK)3xb{*$@?BiFCW^x!lW_FFS!5cMC-YpPo7OAIg;D*qhY0kL8DCx2#i| z`c3&|M47K&VhVIq{8|alv9{aNa&q3454N`cP}6FSa*;`pvgmBFe&!P2(%{q=lbqeu z`yTrydV>+;QoyeJ7(FKvc5T_ZHS(_yGX`+JXt&v8W2Mm0x~nymqKuYwHKVR=WmnEK z7~C09HV}oR=6V)LAcu2gf9`R3GKPVgR+BqC+tYQi=TFgyY2=$Gswp(oN9ivaED~j-|!U6BG;wTe{Ehe=hrl zVLMG?XxY2;HVz;=K##>$H<NJP6@JUL!4D@7O$>- z{$2GCa>r&N*$`ra4^s9(X-^)ENIM&Hw^9cmDEmqcntZIjPKuZR8b(W(dX8Z-6A>~} zV-9-WfGAPwV&33nML{*dh(@r13PIR1aF)M+@7~GcCpnnqh3j%;pP>ENVM0-0LwYP) zblI{U2);saRt-sB2wg6&j~+avwum!AKvK?^+zmoXF})3p0qT%ztcCoWFg{=^J!W}M z*otpgXP9Mw?GZXu2u8TCD6(^nO_2WJsjrlfP=kK%SyU3PT4GthEMgMV zY%kOIT1kPKw>gyG>@NsO%8hpFlvAEryc|V^A|%65OLU^NR9cZkK5DRMX`(v=ua4$Z zbeGSqTgN&s!DiE;6&`p2Wmo~~s5|l$lYb+J@|Dptm%(UvNAVoOm0c`AflpvZ&x&U# zyPeq?b^>WIa2QZa6P8G{E41=41 z5>pCC_Q$xlK(4Fd%g)(cl=(%wHW+3k1|Vq+A(zjdtwO!UdNn1QF9Qyghu_DJ- z(&a#YhTIHx>ko{R2P}x*AIf_azbOX2KLj^IsV)#nxS$qNYda5L%k@z&1Hi&1K!Um! zf)qxfn@brlvTMLrQZ%P2P|org4NAK=fDr=@e(t5)60V z5c{D)38f3)m=C?ZO*vcMb}jE$oGouKNg%P>Sf=+Y2azJSHnEw6m}If^^~~{iFh|#c zxU>LiG+ghi3a^|y7*fd2UmDfvir!8HRbZdjjXK3}*rzsNten5ba)VlWrJ&@Qg&(v4 z#P2zH;>$|L>+5*L7!T6kC^@cce@jN9BvC?0UIY}b`HUAGd>Dydko-cysq`t-x~)(! zi2?pbA(kfrmjGvN5`2)SvtgK@hlaUk=#107=1m;R4v%29Kp~th-=u5+!U%XFdD#UI zkUz{Mlcf>BUpJltb?ib0bK7QBh_3s)RVKg#6A~`bpci8u#C+x&PJulHINZL7yNe`K zios{=^hoaC&RP#^O~-w^)Cig3-zqQhDtUP;td`t`#!Z-DBfELc1Cdp$?i3o-Rvw;! z(xD5_IMBqnT>YyfDrC_8Y|M|bMWiW25Jqk4_%>aL+1Ur8D8o2H&fEOMQjb6&ikm7d z0T|5!>IVo`x+Ql!!{s9Oaj?aJmFw*}J%Qm6uDXD)0pV4o@eoLAe0n;v563auSED!< zECta3F4`YEa*g_HaCae%*@-ZYrP{OGesAhLtHu9=7+8FeL9){X;F?8FW6pvCT)qM*Jovn)UBNNK!u-%&I)^h2Vatn_OtV=7UAuqTaKFdko$g-8QoA;c{CsKJ<5zZnN8 zvb;J<&G8J3)h;J*;bz33L|V1Y$|?rhX8wjXWBf7AU?_6IH#JZn*ssfDCCgEg8}0>y z?&M8T+~Gn9IF696DYYe-r@AV0(p@e_QtI4c4}|q=ksga@0LMQUr&Y*p0$i$T{W6Bb ziIH;nr}vuVNzeoGqf&H;Xa7=EKs#rS))VLLn~|WVGnNr3|NG^yj4M79SxB-%6r|{C zJhjdVzhACy)Bbrz0^)Zh9OeRZ7_V(d>(ze+FSB~sSrvI`6(;y?A*_WDf359Vx`E+f z^ZTQLArRcnYECZ=rPC0s#G9)3VJ1+p@At=GW6pKzf+z?a%kU_d(5^5dxHPUCt0NP)2FV7kB zNtEGrew@@NIata)hI_`u&DmGhO5_vLZCHee7tcq<|EA)maPPq`SfBepjR5_A{_xPjpL zwjqUo!2&?7Gv2>oFa$H$vhPsXArucatQG_{2cZ0wpo7YJ<{rnVWOYHL%{lwo{BLI5Q5$B!S=y(W;=ti zZ`9(3sRCRiL$(NYnuV%k7-!~;*uC5o!VK`w(#6royJabZVL56`!&Ctzrv~VMts563 zSaie>W+4;8ylY{}gVv7e4_q=QGXkYY3{e~&0ewsBK-K23|4uQ6uR+lC<}sXWQz-mS z&9;$TgVsQ1e3!I%&kf`k6%N|0PQ9R^VXG-yTiRe*m__iKe~%jrs^ul(GlpskEpFDXd; zLc(kaEk-YpW+5h$d)ivW<=3rScL-Zjx$=?{$T;xhP<0;dGUun2+JcHQYsQbEffUH5 z7C2*DGJZU|i9pJJmt+Y{gOcZuDy^GVlQRQ+Xdg!Gv9Pdso-r23@$ij07*I(ytpjXc z(i$^W&$N9dXR^^^^vAO}ql!1YKzuk={cWG>703?O!Qtc{Vz+1Y=cTUyR#!LP59faU?@uD>6dN2W zkNY~(ReJZvGXmp&|831E5(*#Gg4R_+8)O4j>>6t2#e;9?|vVyzgeaH z1v;tG-w+p0J~CB}-K_=X4jX!3aDehJX(_tTe!oGrGRphH4<@|~r7(AG$NSxV1GTME zqW9XGj=s8j^z^zzeUgXDK+IM{CQM$eU;67dlCv|Xd`FZ!rKdNLAcwX9v=T#-%YI<2BkN2N-O2)EGGx!{*5{?CF=Zw-dQ=9R z^@mZv1IozJk9(rU3blg^%aqTT;qO5&T7>WQ4-B5`nm;G2%Y4n}Eo-9YJ+qzf+@cML z81)er~Ej~AFB6)bWd))T%r67hY%1zsJ_hdgSGks zB$e-Hb1}jW(st7hT}m*K04Tx6AYR6Cuc`s;l} zf)oZ+T=n_xvo~Ri49#v6+z}Q{VaTegeJO6&TH|U2$pu*w5MYFK5b9ueFoZCJ3(&P| zpv!(JT8K@m?oy{NypsfsXjmcmJdTTTMjwt3X@?R)n8`)5a#?da!}pcpiwP$1o57Y5 z0L6j`Km~$Bh-2{5XJ8LVm}%!wq9Dc^)EoX(=)gCc$3Y_bi%q?iXf(i_pXcUr=MIsp z0P|820G|(fS`bOYpuY!d;Ih*ky)aw^?pr}UE0dYdCp3JuiY{!K6* z&;Vc!yXZzxZYWH)w+FXGdX+p@4AEV{VX)wv3xvTC-Ima6L>2@=}a3l#`AZwm#aw={O51iYEL z;6SGK1Y#F+*sCKwAe2cf?2GNWITD(rDrCqUX) z$5O{ru8x%e7ci>25P=Md_vg^!uRQz8F3JCT8QqfOGsLSH9f<)k==o6#-kJ&uutZ_T zC-C)2!5JFUu@EFVGbqH&;RFH+@pCl$ZZNCJb~S=`-k+kHB=Kg8+4t3J6 z6TojO{( zc>sCFqk==uBg)BI+v_FO+YHfq0$W#nmf?qtj69-fLH_{~R>3KS`V}UhFNOC>-E8~# zqofQ4in0#i-qQ1Z0K{R;4O+`%5$6<;Mip~kcMy98gk78HO0mU=y)-_Nj(XNb^7R5V z9{_auRLS#WJmDrNQh`}oj5oGSYrPw0j`c*f-_(SVvil-WJxtBBd0=jY8-xiH!lU|V z!Tnr}_B4Q2VNk7-!4Rr~y|rTYK|t6hV7@qnML2YU>6(wK5V@<7qXKq?;KMKdfmr)d zJr8xcPk3~%&=4LWjRuRP&@^#CzP03Q2WCVtDwKXJ@^1&>?NqTM&upMZRM`WJM!OxI zKCm|EY%JW3CpGbXQ`akR+!}Qqutu4S3g7VCzGKr9ys@vCAFbla--}>hBFBo0i@zv6 zO<^XOE>G89yc8~h%98phyA0bS?nVSoL6G-i-z&iUsm?t!fr!NBKfWXbSlp%#LMsTyspkUDqqb!S_Q^d3n~FBkNs}h^!qngqJgq|TKE1Sd?0b&@jN@FVDo6-0Mu48=w@3UO zEDGvh2J`iUvuk^{J_ zG{R&>Ry}4P)wiiWNSV4_SNxYC=of8DFmZZ1FJMJvgwiCnMz`1sX#pJTM1Vw`U`Ql7 zQWi(JQPBl1Cn3Oysm87qrVC~mq3^I*4#3b@74U$6x{7Qk_k)w6DOwD>HQ2^e<4C=* zaxTWgn{cJ(l*M5o0}QeVMPs#i3nOwKM5~W{Bt3%7Gq`*|$LWu$#4;fhH|hijOPg7L z{;8h@o>H;z=F13^s3|CT+b2KyZf5yW?Ff_zT05Y+sn$6);Ch!ZiExl{;Vi%S@oo2| z$)*s_J+$6%V+XKJLul33=FxN~y0au*Wo4UFP;_7M0orMH?NSEng6?ZApEQC{&g(7v zGFZ1}&oy}i*>m6i_7m|azbkO#5!hcqRy!NIi8qA<9uWWm=Nuhu6GULd4qNVR_4rL^ zIhtPZWPZ?s#_zJo%fS!IZ*MES5z(vKh!V*U)z(g*TgMA%;^1r!OwRC`qB;z zN~)jFUp8>w)frGQ?w(XR=nHn2y*eJ-D3gfvnGZ6{rP5)$}yrU9D{-Ax#7ms34;0n)o#ed z1GO76-3v8B#h{7mX+DD;%gM)zHZq))PpP7)1~3?PP8H*LHWWB`j^ECf!D6leW4bix zjp>zv2~OlAS?xZ$qRcSaf(p7ZfT$2cPIv-*3UqJzHkr76?hK4))3HX z`ff2~{c-`5?CJ7%@5f3*mt9G$q2ACPHf~8MLVBc{SsfVysdD}st$36hIS^~kYIQqA zp)$4!*c502qs8|9^S>4?3ro6FavzqZQgWy0p%Hrn+Z2G^lg;Tk=9sMBUoB13T0d{! zw(TT#ATX9(C~yaR#wVKH^1b8f)bklSWk1Zmbl;AzA8+#6Pd>^J_Kh7t{tU?14PhZ> zqFxqwp-0e*ScnbzczdAh4G;j-^W4vd$}Lc@;2$)e+}d2PkhrlzPtYmp6-o)D62}2e z)?p8d$^fg94Hm*O;|U2YGA||l7DeP(_#``K=1mq$sOpf_{=Ah}?%VeX3dlr=KN3k{X68)l+^OH)Gd*apA}br`laV>aVLkyK(X_quS#`*5Qv$ z1ETATz}_ShG6@z^YcPk33%-9XKO9>U?d~+tlnA!8uW@ljb>)Lt{W01$Vp%w@=5F+i z^OU6}w6t1v2v`BJ9G?y%sraRFeQ^(6j7}AURTm1k zPAaDBz|-(w9;{VrvFYWFMtOo|t3%sSjxmX)Ok{a!BD+><;s<#9t=iI1{PFZU7cdwkJ=9p z`Q6?Bgr^FyB;+s+s8*$UyhoJ6WFg6@F_R;69I`HTi&DG4@}+>zukhu~9>?70sC;(I zDNG0z2PM2M;ioHkKOl281enDN)BI(Fwx*>15{+gD+u1bKG6$YgA;LBx;dSYtliPqw z`HwprD|L}pf&Ni3|^a0Rn7zVaMY)Wm392G>dm_*TN`e4KF&n8){QfUU+Vo( zVWDPOB)CC9Mpb3z+BB(Oo%&|kC1o$=jeYBQ{@F)k;~o@L7HLLG>4-kX`9X=9iBkRqCH23spJj5|c<=Z5T4WdLfxl%UqG?y@C}J)DQcP!i|Oc=|&BAks*1 z?~p!(mk%AN925pmfd^SYP*x4jw+)3ubJd%DOeHN3x)KFtgO`ixiKQ`c{`_9rcf?9; zyyrmqyy?@cf5e;Uy|BFZ<6Gam!m#@e#dfAXU`{FlAo$)8i;#lC5z1T;6Hi?rt%~gu zwmNsdCDa#z*wqPk4ta`Fi5kRyUoc8mAi%@hkSXzaOzU$B5zBPfqQ&FYf@gC#gpupP z2VZU~lx`+pXr%{|j}V`b-7jrMa1RTDTT7hzhw#K}xa9-!iquOn1qN6&EchLC2aPFL^*FxlxC>m-O9g@yGc{+aM=R!<5!4^fI(_P zv~mDxAqj<`xxm4a-4_1jv7l)G$NZ=&H(DF|YImXLLSn~fpA6i^8cZ#bDYMI`E@-Yc z26luDp4tkBmYqf8+XScG!r20kVkS-M8rp1c@>c$D9R%Oh#e z=Op94LkZa>=}*kfW1|E9W1;OYt-q#V5{+!i4hs`+~vgsO<{=JcC*Oo5vCa) z_w8Z3T(foiLW5Vuy2+=b7yruCCE}f#J*xG58{5Bc>~pb3NTpT%f`TFO9r~vbBDixL zYG_qto*}Hhf!QZ;IT)TSKgy;yxrnuja-b1Y-%2mFTfiBJ3G)5xcJ!i8 zRsjwu0Ui>T#6C)(fW$Za?1hsh@uI0Cm{>ylrF1#}=6bWfsXx&lXFN`B4%yuXs-}(^ z+zu+rwoz`&up~uvf zl?^&fnnw+GF6eCDHcV;`;u<@C7mI*durrwINejWrrmpLvHotmR+y=7SN#~f^*{3@c zw}kk0(1H?66Z|?nyAS#=@y`lKqwqiGJ(?zl{b{p0U(w6QD`vHdD)!+)kQGyWBUtKbhwgN^GaBG*Y&KP-;jD);$^ z&}CvC*JlW#c7lsPFPzA{JIb1iLcPek(Zk5vM%MP!R_`ChQL+^qy#t?_s;!B(LD6a# zivT7-^cCRp*ScPA&X&q{$Clqn>_)SvEodX1I#&>k9BLBW3&pdGZoSAqPcRpO6P&Gt z2)V+=-8IvDRxb~)GL|2DprV%+>9JEi5ICU8?XVnfZ&{3s(0 zYD$!{dLpB$%Cq-_5v;K2{wNfJkyNavF%lFd!DR6GgqWaEvHZ}!XD4{nd;Q2|;e4A{08-R`c?5q7o&dNe;BpYGRE1|eFgimvt3-@jtURs#Ui)?0732XaC62rbb*dJlMb>;#O;E*81ByMe>O4c? zD8YyCj^|>r5yD6cTQU`c>jLPo2Gmch5eXUPEI)S0;ygPD931`WgxQKEM=GNRF~Te1 zPLCy=k5MXoer#(6HV=|313pgtLkv;$tCP8l$vzF^Ll}D@NFS7pkm(+x*naJOK&>fV z28F!=P(j+%Ct*o{71QR3XsU@6gM`Eb<%ZA0Rv<%kZb^V#kG_VbwWf(d--80YRlX$I zlJWq6p$Hm;Ai*Hej|Lpi%G#`l00|Q4HDC)-NA?d5BJ~1@Z;i`bF$Bg`o_QA8H@)JCFKLxCchS=UJrm>$rr!& z%=pS)0I{7=B^goyLa+Jh01W4;`Q9eDWFndGGcOC{*7b_{g>ZZ?CU!7@Aa&!MU9Yn+ z#~u?hfocd4wTcAoeW)kkVf%xd+yGeXhwfGP5ZnPWIKZN2=eN(+lmV>{U>!*Wk=-8k zF#Y&}V_jRD?xA=S&YR)wI2;;d%u7p$LaWFEjCbDIT+npbSXshgxqQ1>uVH zh^mNap-)i)GSOvIq4o{{pb!(0jP{prbHfnBZypX}-y=(so`oW(@cY7hZUg0|z?XcT z#q4^1=#hwpMi{mb)rHbAVPhuxVy9n$dJeRo%BXzyCk=xBOIE@FWliHP7a!e#BZ7J% z2ocn?Ic$93eEb4`TxWe_EMwX#(2h4=dJ=4Hj>?%OcY=e@LwS2`di~mU>s)xZ03srI z0RH-`nB?Q|)LCOT^nB14)5NDNsjv6qQDCpRP^ggj9_6mf+NWalsarBmCeJm;#*pWn zoqC+53ES(bv?-zq-8k&2?gwFdVN+Z%Hz81(G6bE$A0EbH^5l*W6HNC*`>RS$Z>Z5% z$}Y#5Lb%yVqdt`S0tVq>2@rLAZDs$&p`2AMg{#aDUc+fC2VOjno0jc?_@g1&=@SM~ zEzVKK`F&qq)L5Xx`qm}D94>>gF5pQW*R0l;n>fham(I1D?b`_C2-7yv; zL6n3F;4gV#=BflGMqnn8oxIv-+%(Ws;ttylH0i91niX~!u?xz8ycYBDMTgkcUon8F zC3o~^%+&|YQD%&`>J79m#h$yNM2~wfHm6RU8x!K4*8(w7T^+?<@$!}W%Ywu6;F%oU zZy1hyU@-3OMz#lP)Lx*tYIEDY;RkyU-Qv&cd5mYYkI@}&f~4$%95s-*dt|17SH@~? zKv%?J3dHr%B754V@=GY?V9saO!tP@UTTwV`sxg2qzCx$bD_n+?;oc^|82NgNT~#hOZUlFDG8mPMh8r6B zdR~VIF932OHo!q3@p^YZz>LfvREcTNKa<_d=<6a5t%C0Vsn80zVW^KekY z+u6AXe^rhY-!|N_vhWa|WE}+X00QT^0XCnTkJNMOj>8Pb#!+4%1xreTCRpo7Cv!2Z zt_6uu@aZYjHACVK8!S zn}fOW>oxM6<<5=HtLgIx(Z~h8LFnaKZk+YRMYT!CWQeATqo_*6*rUdrg*-73MH0tk zG3|svkOa7c`0%IK<(!>znlh9?qCZREF?UIM61~N4_$_mhOOt`f`ToXZ{P;a1#&P&t zZhLu&gcKgBJIlG4*D}K|zemoU`Jk((t>>(js^Q+N+Ylhn~a_?a~ zY=rSGq&n*dm0)DIgNZ*n1aUS4d^&ILCnO6b((Ud86>`eI|2eERr3@wtHEmez+)sq? z&esLQ!?6ZLydB%OlVwTT7btmc*C-Mpc~2y|CEz_cL<5m^)BDtb-` zE?51z`(_B;Mb>4I`+!HxwRv6UyLl|i)`6&F(B>{g*8Iq&rszXa$Z%i}55__eo727xx%v4|%E6LKgf8s@!u!$b15k^fmK=ydPsti|tTImA5&2T4S>;uYvu&*4Hs zj3qGgdeA@8%-9am*p~^;5?Uc4Q8+h^o`J%<;hUX?n}xpW)3Hj_TrK}Bo~6S;ljqF$UasH-1O)k!A%z=kAgSIVCZu?DLXg_i&YUw=)x{2C~zp`JjDeg zmjH|ammqGnbOkWXk?SrWecJ+R0{HfrHfn~bYl<>SwSRhphJqoTD_J*&XWK1z_oYmD zG$a`m2G_310oaAZh2;nSjCA=;n&J+IoCz3chNBl$1`pM!uCRx4RqlQW2^8*d25@*l zR%#)&5U(JUlT37v%eaSG6HZ|XK7xyLy(_hVvxrxKBoL5!hsO%ux(A6N$zT+Z;mmc_ zIK(@1^I^h=-}MhK2lrM~l+w)L81_JPCO^N3)4RpjIS=0%Sxzi!c-5`E@^ggD(NbUh<5<3}o1vqWD-1UC*<$ zfH~33GIAzDDetJqvFArZ)q=f$Ai_tTom*~wkU{zKcWr* z$yi#ZtXB2=ThYDg&6P>?*a=kiFdwf0&wAXs5HUt-MQMfV_G0lqD~Q7=3_m4>Jojl$ z&O?YBFz5Ja z#$Q>BYlns5dF;@+F-~%fuI@fLBNQIrFLC2EluEWw2HM$J+&SS0@DuR`{QMfNFOY_+ zf%IpF3U0JkU5L(~J=gm$|8LlQtD{eNBk_SM6y0FLQH()PwqP>rmptQ35Yk%JU*_?| zz$&{H-yiW1D5hJNQ-FlEY1ogadwYNOAA;|KPXE_AU?b_r);Hqb3;`z6yS<_Qb;~|m zn;6hSWPaj2qLc^m`$3rb#aXt6L3YW%@lTPDcCOm+n`4iy%-gahBiZ2@?dwP#Nu#Tk zU`6h6q=207+t^P{7HKfSagw21lrU(&MF*+7<32Bi70BG-zT*v4v!%To=Q%ZpAS4Kh zKjHmCK?vsKpirgRfE#T>qtpXkeE7&}=K3Q*EE?EI) zgh1l88)*)&+?KMNSvn?-D-sh!Y!15-N79>NG|Obz~Ihn4bUJcC8UL ziZc^uYPl32#*>rpujb{kbd4?^0`wAy;=pXz4{^Z7X#eL)Ujpy~!HQ^^Dh_?3TY~X5CHQtxltv_>55E@M)KtS3nCYEC&f9d{aqTxbxi`sDNUu(uxA`2&vEs%$m zk*ClA!z{x0O+Ao;p*%EMJyFvJw0j_DsDIzp6mpWULU`hP2UC9!d0qi}1lPn5b-h49 zkdRojK;82v?iBWAvMy2uKZ#>v1&;&AI zLdH%gk!f94F7;Fd2?{-@7cUI|l5hPv+9VvMi6NLIrYA;{rj$x=za%qu{FA- zHxFp&PVe?~iv%eF?;o8U0$f+m+5blZ+==>_9~V%u>ur9SqxxjW)PYvZ3Jpb4^a0aqYm;QMkzdA@oFMA8sey8&jjz+7X_?9m>Hyq zhHL@dBMw}*=#87b}u?Hwk784_7m{8@wydbIp2EYV&K{si zWlTxE$zm;LlAq+&CKI4bm^jAs+klBs-3jcHT--$vEQN}X$>yPJ0hKIRXiYI6k<v8cXC?HbN{7deeya&1IU>*0mPX0T(BwW2S?h2zug}l6y&tSaTo~! z?6YR)Ep^kmEMH=HMjiM}2MoQ)Re~Om^SpJ`Zc+liRd_NW>8$wq07n#2!Ig@mVGuL` z%oQJ_dVeS%GXnxMo?~GmSZEgAHh>aAhg3$#lKXn7SD164!)rq&2x?GfpZTN+gL^PW zPN&4?fySyoJ&e%1wdB}^znC=UXc0_Jq!mMRH1M$ipW>k4`CbC9+(YkmD{l`Ncpgm* z2-ABSemBO9J)>(}`%T*+t)vTwvG;EdOG~h_SLf*~RBF~360y~r*H>)%H0PnWopI-c zzIr~LRym-${pr~20R)qN-$U*K`Ln>{kiv)^3#qxtFahq0(t|srT!8;VhVw?fO*_wB z?CQlotTJnM%hA*$c>-%au<8JhwWuW=fkO>?A9WhK{1UJQ&q>sg-R?JtB_nku-U8XE zGFt3Wf5UUdKVj-o&aOe|r3%IcK<13Bm%L)R@HGCTY5qpz2l-+e@N_Gt$%JqY1!um# zvxrJreyH3?SHQzL}6c^AMg`po~2qn)irP|`4KbT9y0xoZYj(h6=XzYd7ci6ucm0*%JS78 z53kYmCJHl}O??7xk%C&v{(J1ElDrua4~RI^#9ZJfybX#0aSr(k zJu=OZs=RS1E_@mZ`s8gPN9}zUzijKX=lX<+c7=nq4jxJ}7AuBah{s9)I%j zHFkXe@kYN->>ARvL{Mr)>m>k`Vs-E&?r2Ud%ZM*8w#yRj*Rin_5ZLbCdbYoKlGz4} zH%Z%_7mj%$?DS&do9*L}Go0BxF6{CR%UKJKZ9B0)rRByA>C2T(fBf-xs{fkT3w@hI z{`h0=oo#m+7pE<}nHb+H7^{`PsN$iL_3m43_2;d;6%hkPH$Is4p)4R<%5<4<{Zv1> zvFx3-)1Upk6D_xL@uw48xA*=f?7b6t4TbbX&;k<&5YRzZR;=sgh=mnBa8K=Dv8cbc z_x>uw^_vleQbSv}KKNlE*IADoruRb96;|{+e%tf#n?kKX`ezeXK?w2%uzZx^6Cd;g zn}G1ot#5F~uY|m^*(Qv4GL}zP{WY25$99$5pu@w1f9fyQjgl4ZD{kg-$3qW>dIBIy z-@fj0%0PW;S^p~@N8~LFR(8Imnkj;05x?(hS46x)Pa|SQT^Fj^^L`~g^zc}oy5_>B z%lpN#r|SFc*|qEFjce!5Nk1ebPvi;3L-y4Vo4Q^?H{fgEFxmUzqi=)F&IN5Y2kWdY z($dp+{-teS?BJo`T_$98aj5=C)d}+ZMbH@Q8i{NYo_^%@P2CJ(LVWkwTng2Ma+XAc zY5xqx^i1RECTJKDrM6I9gl9-Bmg+NitgcEPN?5vW%e;LHyP_7?pB*?}cRiZ>xUV=8 zGac}BVg$~An@)~5=!;hf^;QS+g_964QlO9GW3zgHl6KH{cgEB!6~^kY*4KIR5r)>g zSN~o6RZ%}?xv6IIQ7dbg(s$+`lUNUHrA!vK%-bb@12?6Q?=iz6?wS&@s;jS29UcEHOqzNH94yRdZHQ`IiS8m6>5glvk|kPb}2O` zwonK~^+jJqovY1I33rt5uDZPR&%^xaA4j+^_>BgrBO{X}vmzM(9aLp83HUBbte{<2XAzgSWDk}x7 zJ1&1<&I{B^e|8ljVxN&vi!t;@;?V@u7+WId`yL)&p;(q2`OG%u{)s@01 z|MJ7s-r{aoSJBZk13Giwx2@w$i{4ZsNzGPlCsTX^w5gnfjJF8^6WUg&;Ckd-HDT*2 zh{z?)c|W45-7Q`2Q+_(8R=0Pv-3<90&ZNz3NbFQcnCbV`@VnVm)+?j34Q^j}4?0`K zYvI8h#1{|>94)AZ)1r=h``0=iVis$Poy4S`l0Kb4nD>LCVEsWa6{rn}K7?W>Lwd5pqRrPq(5nv$3^u|2{7oPpHM@AgtR>yAx>!kcJ}lf?)>i zfp@VcLdFvzbP9=Rt+$g_ujTQQ;+(XrCn3Yrzf=#J-U^A0@rL3lKTBIIkV9@m9VlF5MYFG#qDUz?Fkh}E`aK38m%tSERKT$$x`mG7X4)|vm{Ovsc zyW>ZFk?F~5R98a9`-ERAw(`JPE9*@>6}ZtfVmn8fxd5O}d3tfEhB~t63;={shjKcE z{*cbd>YU^S-)qhADIWN&1E}^dJ@1{l0VP*wk$c}`cJNx;OqKm)(-aQaLXkxo-`V}H zSj!{;d?p@u9R2}k?(1opwn3awQj;hPunj_l0N;0Yt?>PFSCw5Hyb74ZkM4|C5U@m; zFyPj@KK%*!EG*RvCUS{k4`3h*mJV~J_TMY#j_kR&B=?;!dy=r_`?DM0q>MIrfy1yU zs9WIU{dGH8S#KXM*ORNczy^vfM3%#|J$1`F*RhWr3JZqj>~gG}ReROnZ{gL;X1 z+KJu8O6yyOdzRMoqZcs(|gejH4tH#*CbD*^0t`5bsapl5{LzudB|Y1 zq`0Q0qU(ew>9WIr-Wor{%}}o!jL*-y11T}lgMk2^0rM|)Oy%UKs3)(Tvh~_A@;68L z^uzk}LUFsno_Z2CARsxWoqTLAIc1YOIJ2_vVmzqGBpSxPNeCY-?hVq05QADj)LA!I zv!%`GN_0Fft|@t_FZp8+FN&aqJv0K0zbOPBP}H^_Fz-CC--6O-3r^cQ^97qOlr(!_ ziy)7cOXn&TKCioJrz(&lJ^Va6>3LwsUCpF@mK^m(!V~i9I~LLdVIuzl;cazL%#caR z^OOg=nQXI_V1A&;vs&CIP^LKN+toW&s|{Pf0sA$bWr+aiV=OE5+YOO0jyer7u=oV( z{c&Hf_$U#W*RBhXpl|=wW}KsuPuhWOMLCZ^a^LpPPh_`n9)T{T&9Dl=xuM2N5WB+unzJt316R~E zLOCUWZ8yDapG&XbNw7L!WQvOXlyyIh5&hT#S#kHOcyt0VtxiWcx3+gSMqGI8B<?Oka+m3zC^uy-mAw40EoWml9m zk_x*Ls{x5nh-|4832l<79VJRuhDgYeaYwskj8G}m%D5Uxl5uV0GLPrF*Xo?T-!JF) zp7Y^+I{VApD?MvH|L6bQ|NFk~>$>i@&cU2bGx3P^5x-M#X29NPAmp|yBzgpqqo|Gn z5pg-XYk-8(+p>6+3B0XagVTqoW5lcnOPX*1Bu_*nC+m9fG5vy`uV{Cg&}?_HJF-jo zrISplRJ~}trCt=9tWW3(LN~dZr#!&(mgSzK@o7{;5*wL%w@BCG0WY+aMtc#GO_*J| zEi$!1OvJ{Wo%B>oI&V7`Zicl`E*gcyXv5j%bF!)RBe#BIeoYR<*P;qwIFpY7@u6Kb zEwx1n%|%nGr+mLwP*QT{P<8u-81yNRQGM>?GovNl#k1VW*6a{QiO^tNvXemQc8<#L zJcd0Tj9KYaqs2l;KKnr<2sWhlGQl7*W7v-V;{DDQ<^q7eQ$sXk@VhOYd&L65Ff@jQ zA&$`u*e%jJqxt6~oBU^)|CN~#$XHrybQ-L{&k6|nupo# z$^@F3ed<3AOg@Pu6mH!2s8zuNVK4S(F>^ljwt(izapof&4c>7l$xOgWfYWdVz7|Ts zq{mAoKyr*hXdpYCxy4N>c%L+6G))#N*}t+%N8!kP!HzP`iNnVV8C|{o`&`v%`-bBA z)bqIzm;=5tT>WRb%SUv`i{u-=>h3&rL^P)XZuh6#kE z88vhR1QWc>YQk|i{a_V(@D$(U}2yAh1Ez3lFMiR0p% zf+HaK6QZS)0j8+o0yp>zmluK8u;DDoh3R|wMtiY}f_3&nrvz{vQ)ZJ|J^+y7Q~V#R z4)BKus=kM#lRkfD)26$c0K1TXsjmyL>Fc9avBwoIbTf}_roYBFAfgYUD zVxAZeC?vF-KeNy+IZi(*H2p5DCGp^STratd}QWXydSD zeW&oZ>`KACO7u-^N6HVN&y4R<1y9g}UtB5DDrvC5kL7}S5!`IoEL zg?<@9we+SH=aTvoN&LHKU3~Y%MJG`<*jU5&@%ASKYp)%!F~uDV9`Jiaef53&qFsN0 zHtzVu2>QN>g3)z(Kg-C(KVNP#cPF1HI{W)lRse|CsWJE6fZq&1BKU+v&;oS7a`8^Z zBjkx*qbHxdQE*>$P6qZX;XPP;5kD51D;O9ooWP4QKZcb0@{)gkW;{qR(%Ee|LjLvV zEv(&g`*2DzmlfNGYs@FW8uiaVL4mkJh0Fh|r(A5I(9 zP_FwB(|k^>k5jStvdEQbMGX{`Z`T1VRi z^(ThNN9Ct~L5B#3IeuY>lHXKKbfAz-Fwopl&MPtSDQkuiZz%K`vuD3>e9#4?%9ngf zp%%dzZR7~9leDL$rEtezB7363?&5F8aekpl^JP2`3v$RHzCRczUiOMWgR~=tC8u9O zEZWkKufQ0z6jr>e$s66Nre^CS>t<>BB0cmj$o3PR=z(g~{_W4&^Cv|jg z%&(w5CS){|^crXu#UsE$_@x$=KW=y<*f3;4&Z5F=t=Kd(vT;COL;k4;vm9;dcg{i< zwHyxHEYfNzhsRzzW=q$@fB)cx;p;* zH@>8vmtg8_VZ&)fAY4+!35K1jF3KHfrtU-BXn;^qv4LZ>G_$&`ryotp;{fwWh79PF zQuwicpRsrN`^cuICV7*|a_c?Jnh?#2^k;cvRCac>m|B1>@Gb{aeW~x4l?z_`~v&v6<%%oL4SQ z{ljw;KM9zc_MMAE)f^r((ktY-#C0M4AOb46w1HkCXIll$eEn%fG9RPdA8$ae5W{ir#WRc;HhT?R5%1{tPJ+aC2S=Z8|2=1(10Qu!CE zpA(16K9T!=A*2Aok!3VZ?ZOx~0x|O#>t5St^BwKcaFG1E{32_7b6EGO{`^mm_C<`{ zJL9E07G-ltKShHwFC1qg`4~Q5&s-O1RfJm~QIJKs~S0 zJ#K=v;8Hr3|Gd;(ex~qj8yp(D@%j|Wi`Ze-;+s2MGN^<6$tdD4((Q~4`K-}GDav4? zGQu7(84I6qX%#Kf%Yj1PNxvr7*s3KJxYf!RKk*rdFBB!Gue-RI2};vP6anZS?Dn&B zSukr>U5Z?vN^uo;;x_(2_gFTnOgm2|Rij61@QK43em<3a6ENp0lPm)r+qFwkM^Kjr zb})aCEDQBMJ%oN%{D# zq~dlCz{$CTkii$-o`Pjhv2{^xQCuA+MeB6sRZ6FLI$n>U0`IJt#W z3{ZkD3ZyEcn1Hn8rk5;w6Ac4;my6F}JWy1@K0h5(ETeJC z4})}b!V6?o7Gjrg6ZmUmXRW*@h)ww8|7B7Ye-kL!E9+ErpRCZ}_np4%EBGUeGpiq9 zo*kp~o`y`O%lPVcoB2B`;UKsSKZh^Dx+aGt^nbAFK+<8E&HF{WLE;uNTxpsSkid}_ zq5VTnQ%H}0?R^LUU*TAz>bH(6AsGdJ%u z2qVH88Rx^{QcX#cX_2ZGJ~XoG_g809w9tor$3;n&N{0!~x~7Dr>?sGI;+Wvj5F$uU zWZ}UZ#c}J%d>1OA8MB{gg47kmwc^kXCTH|9HguFp+IIf%vB5|~zq|P-hrbfoTeHX8f zke5z&|5vX?YLhO12GEWXuU+94fhdsBh9cJ#>1=YngXqo~E)rj_pTzw^sN zZFg{{Wnz;o4pks#b^~kJ#X{R{UhT2bRg$gS7pkHRAPyVu*$OQf@hy(0RePwlCZCGN zMhq4QCKW9gJtiA$Lo!KDPWa8Cl?s?|jeZM>&CTMb>P_8`arB@_8ra)E7Rm1zv_U{T z`T>XjxmBr-onM7b)arQ)OFwBP2w zHrB<*R+N!vCJc47kX90E4JuFUl!_j3aX;*N1s3{_n)AHtNKAol(Y#n3JHZy}nAeR{ z&^=mCca@;Fm}=*mqV$fFm~agd!tQM~u^~lo{CL$HR&IM)G`_X<{llf<88j`2iBH(~ z%ywLD2N12}Sk6J>2pZeIn2n4o0R7&v-E9C0$x)iYL_{Jk0S&LjVjv_De`k!RG8PGN zo5=zZtQ%Kx&yv&KI{c$B7D#uo#UGbPf#wGd8Ao1418OcQEl*Tuq!5Gyf(eZQN0KCc zW2?*HV-wdA2b?Z@VJw#c0yOR56;Ktn_oGqT-5e7}6Px#=p8>u>47|W}$#N1|oA~<| zV>#H(v7`?q`b3yTzKcrUL!GB8ji8W}qn}A`Yh(xnf;%-VX@(5(oPoky1T8JNR1ttM zMV>$4KaeW`Wfw@SDU0X^#1l9mV+Rl)^LGp>Rl?VcKa~0ah`SQR>aO&hT%@>hBQwy8 z1`LpZqYd0P8 z`={ckHS%U&SQ;>grY*vnq0 zVF03cNJvOm&2j~t2kRK~L0^}7bTX}7-LQ35(*@T;@YW?@`quklI?EeXk<(L~+!IB> zH~kHL7HKstFHFz8Sgk=W|+V)h(V z2X5$%VYCIv%E1IBIkKFJ~HQH9CXVs zu~D9e=7(8&RUiz0%zBBB&$%%l^FBN;)D^*XvG`BOmlyvrRd2>peyZ(Ad!ZSRu`|OJ z-QU5Xnz0AuMHQd$KnIm;!5Ivx$iUj259NaW{BEy8HFeg*>dEMq=)dL1K6KqiON!y| zY!-L=vyTRLA=$ygpG($AJ^5o7$de@mVjtc58HBu-MjGrpI7_N+3l`vIFbV3RK4eDc zONpm#Y|uCmInliTmQswdxA#D5bF3c2n@zsBCPSL?9}B9{*I5!8Z3}?BEbVOz@`M%bv+xd5;QRB zFzYdlvU9{#LSZy~klcu(fRtwGU=jH`koM23m@V)(1l&gxaDXN9&VdL(V|so1RvVlO z0%;w<+?~TUI58+@f>bFRQx=DS3@0>d*NDTAF|JMlnG!q%Ukwks&ozNxoyUhc0KKG5 zTyjul6^88k7GxPp4$kXQ1t5~_!kLULad32~ou>F`ut4Xd^UkLLIdrAbU-CF%pnuQ? zDvA}Mcnn@<0*)Xukb(B5>ER94PmSks!L_MHn`hVnynmx6Y3%tGQ;k!~$NK=OZCG>f zSRhr4punPevA#XVY~DEO#0jUkro)nCgPxT{v)9;;xU*GE9e{BTFJ|leeRM?@d>lh- zD61b>_YDWs-MfF#GldHS>zazG0eo+BQYW*}hqh#rV@^@Xq#BoB*Z`|~P= zor!XO``14&D3E}UL5BgG26BDv`ONQhr{jDLw)pJSxWLDvEobb)b{0ki8!yGoTC2aN zU&)^b&ydBhkKxKeoR-%JjD_AGj1fYjVvfB>d7s{n?op3p)@no!v^ekB?&14RrQKJ& z`Rht>4$SArlpXh8u({IGIW0A(+FAhsddtHHAmaqpOJNM3j|I+n^8n-wUIl9b2EEkN zH?|1a?0U_ER+LyzE;MP+o!eZI5Xe~}UF9BO%$Psa*Pe9xuh15e$AA3ZW0#EFU~N(- z=9wK0!V(YKMu*m&?%8Z}YF^?BaWME!k^cXS>*tTlX4c=00N3W#N>}oKH@twmIywN? zh|9>W_ImRHvM(h#1EUa7ovRv^3)_(CRHkrDxDSnhc&@ zWn$_e!zFunf7u&EVS-a3iz@r1GBZf3o+3yzXrvPv+4AY?r`;?huitDBqZ{FXfw4?h z_SmSi&l(sJ9AMc)|3u!YQe4j)H!8=?0$|Y?4)n4%|~3hB-R?+VkAT^nPqB zJApkEX!zMW_0Ffp!`tP#+bU>o5Kb_43{UIGU~esHR@VPEfI1RT%A(git0_q13Q6zKygJY-V-Y+GHW_>_?FeVALA6$^`vUk7W$GSAJWWSEIQaB8E8f{ zEk`8?EkZMoRu9Ec`UutZFd^ZJSNSXi;g6JcW>;+dSpOTHQlVoALp7XmywMu#I<~e$d=Dn z&>}I^>AbQS#J>)49-?wx;3~Hi> zIt4!@tg7Z_BjYPtFov)F<`n0ATy`4EfrX&SO$m_|ZMiPHf_qx={7Z~R{Psu+(m%V= zs%uZQqTJ(+an#N+FqN8ETA6#{e)^ofMQ`8AZ8$MRy->`@WWbV$X;S9`@(Y>r&~S3V zr4P|)^$!fxKe8jyS<+j$M++rsvF~FdIzAevPEIRS`5yGD2@-0@Z|$#Mcil*~Z(Q+< z*e;}~8&X}QuWy-LFcP~GSZEAoKv)2@i7HsqyJ=HmT9p>rF_D2ui+`tSN;DvYvx;pN zhF2ip5+nL5Hxa1=A4?JvtyAtImBR{-Y{sbf z_-+Sftt9=(C|G@hZ*?4GiD3BW>(4Bh3a;tMB)>BtpjNX{8!KD6D>#Z zTKy<%XiI$k-6#2nA$pNdbscI|2(v(A5Ak(4K6OiL9TR2JrZbNceP3LR_ih!Nf~SDO zU@KSwtSHb?gOb`MJj^uN@+hF-x!|W%*=!$d$9)Md2BZKwpfpjl{e|0B~4z1i6Z}b*9hG zsRaWvtSyNxhzsj6Z<5D_?P2hz+!MR|^xIH3y*cA^!9nFS=aL0~fC_QNSY%k=7hBSG z=I}dqicp{e-gBPSbWN@EDL%{Ec&fi4JJ{CPt#5HMP+R!Lx>mGScR%ykzF8N9`l z8rKnytJ9V?xOag#8K4^?nT}sUpmB=jZ;T*QHb_FlR zq=;0LpjZbm$mgTZPlXLb z3@IDXpqG|96 z_t(cDRe(p#wXSj)5gCVq*?G&`t)eZbYL%IMr*h}k6t}wW0{6$Sf*Hp6KMlQbZ8tt* zZS)(kfsLt{U+Q_2;~p<)f4f~hBfXMA*uptP_a0<*vMMSy!`b<@?{Gjq22}nOD8V@C zrADvw(f!oh(loUk0k<492dWe)y(dE3&$HlDl7h(#7?$vErR!)c+cc#fyXcs;3f}k( zm^l&^XPueFJP+FRKp?Cz-JYOT&pOm;C#?IB@}WNB;6sbek!yxSl|)wMfx1~!Mj+cQ z{OwU8NtqkYLVM8-s&Nyl4%W5y*P&aT%aabPgiX$+Gyrq6{dCWd%r&;(=brTDu>V8j zZHs?mKSolzOGfH=CSu{b+|AgVNxYevTGP&u`znCtoZ(2vpj#>!(TpiegYvS-I~orf806M1 zy}qW8eKN7Z@Es;eIX<&Y|Fau7NO72L;OSJaoJ$`nJw%5sKf*k#_)i=^>r(gc-|r%& z03)Kc>{F?qu0w5I)yTf3!k!~*2!7E%U}<7wRqaf$csyNz*z_IbfEIool#8K?w*XCh z=j&4rkS9zW4Ixn^Dt*U6%0?Y;{fbQ!?NBH)OYsf1Ur?4so#sPAFbLto>cKsa#Y_Fx zJRMsK)46G!3(bk%R=^I~RAyvacn3k1_&L%IiqmF;#D=JI0u*SIBBQe(EWk+%;tVbH z?zP}>mgCq zZK-e>kZu~YvQdyeSLQ4~s71O%Og0Ee?`Q-KC;~Ew{*|+eGqh6#EhFs}Fve9y!AYY$ zNk&hKMA$r{+D75wk5LX0Gz5%%=mieyk2=5+%~a(QoejxZ zQo*DI7Y23rSW(FTf_a%9+bK*>?p@b9{Jb9=timln1Kz-czDr#TYDHXqD%Y&BN1860is0D)}YdBPMF`YTd^LK6vWRS0dkk@8F zR_X7-ZyZ8rQL^@`TZb(ud!ZN(g&J+qj6<|knl_a zQlQI3G^~)Y$y&@LH^(KC(`HJ6_fv*cltOk3SmU1dib8ISO!*)weSL$U1pgF&{J+G^ j>7)PI*8d+-C&ngO8EcQWZ`!$T6=r&2gzybve5fD%jbqu;BMWj=4C@Bk-5XC^HR8TsU7#b7>m69BA z2ocGVkZ#^}xwp9Y?|r}T&+k3Hd5-;nz|4K$SDfcM*IMUwOJ4RU>q5?jEG#Unlw*e# zSy<-Sv#`w4n?Dyn(VkhZgD-Q>o;Z3KU-24|PItwZ1=hz-+pw^#xk3J$;nV!nfQ4l( z3+3=ZW&401bq@AQ29r}$Y8o18(iRGTeOkZv>C8`CmaO0M-0iDd_|;j0AvaT&gye)T z&TVtzJ|d%)r=8y%^>yyznXd)XxA4#6=T~37=tbg>#21(D^^JvObKl!*a&ObA3qShK zYird~WQ!eIC0j!~1?;-?cQ4NU>(_m;u;g5L{#N^+?^&L@%(tKM_djx;|KI)guM0yB zuGwex^;dCmMX5xoUQZ8*XWwrhUo)@4c}P2@U_|AqoOFqYvt`l{Z&c+Xmid{R8U@L% zd;8Q`s>#S)=li(BYlqqjo>i+>8S<{(*H->H;cT#G=S05gz)Y4yKFc!xdFV3=v;Y3K zfJ59iT9o?6DA;***98`q`CK!}EMBX%DUsNmyW)HTO$_-{ zqcxH~wT`+@ju;gMh$?M3;`O;bEI9V%DSv(m32inV`)B0ywri!x=igq_$4nB^Og`y4 zS{_^xqbag+(D^hHYs77pot!ehB$7CjMGI(zncv1lu|_Zo3kPc|V{ z6O#xz=EA(xM$b>5j(c3> z_6$sHRvf&vc3|$<{<>_}Y#xX2%e1ZE%sGDYq_U%9+7U1IXKFeQG9x1+zLE8twTBLB z_iYg8USd%le-K-$>^hrHctwSBUxrk7M&HF;R*|+!HQh=%sfq7&tpb+M;p|v9#(U;2 zW9a@+v|(G zQ&LjIXwFUEBAhZZGA`Yob?J6o?d|PJv14h>*xkoZoM39va4}ig+ofT0!D38{y0oO9 zowSBg73MSP8(y*6=~Zd( zuwpnDu?a@o1YQo{stjfpeZ051?sh(pgd;nKidxmuJ}~C!b!=C$7&Cf$ke!*VEe@tM zhIfQgSXEkVTbr?d>2sMQ6v{2eMDb+LIjo=~SUt}5!8R?05ecd+#$N1i7O}8=KIYu} zadSu63(rAJJoAE&)MQG%eG_X!=%Qf1V2s(Y$3ZQM`SB|FacQukqcX}ll8R+W_NwiV zY6a#6e-SIysmTdv&UMv)jis#Xxc}C*zA#uyS3l3|r{j+_et!P4o69!zS7};Rrbge( zZgm%0vPLYlt1jI?zv=Tc>45>duE55P@4KnjPoxZ`XJ2s`=W>5m8gkTZv>$U=U`aC@ z?rC&vf8pm8`LnGwq@y(C#$YF-wY-J+aKyS%3m2)*%sWQ7v9A)I^F9mMP4T)c>7zXMO58&iFI0Y zH`%Bph;u-)CudgP?KR?FzP`(I8?D)G)9dy*^oG8&X_eI<{85vab9ok%yRLnlo@!&H z-~8#p5f_&%x`JVWpOD=pS!Pv?_r?>EEEqlmOA6w0?=bnO*)bCHi z95s?m4YuiIR9nhXs(WUyB5W|=U4<6(NNnG-h8gE=`S+HF@t->tk zS#|!K@ZE~VT)8{EL~U9gZBW7!TAaf!dhy~#ixD^` zX4tgM*z{s8mmk%yBr6@6F|;bqGH*`dPg=Is@p?LbW^K;g(PA5#Gj=Y{RZlW~@XEIH zi-xpXZul2k?2NXliH50>@lhvHk^K@h$DA=FrZng39X2f|gR9yF=yxtoj1EXj#bsSK z;mg)_j1*zdcRhXj@!fy`b3qZqLiRXjBBL{J_-B*z5_|EBZD$1g>g*aEVpg!oi;%?Z zcI2Hfi*e0sgTQsN10x$mENj)7y_z&jVZT!+PV}V(2wlF;OylFgPh!UARJ|QY%76E6 zc+XVp6JfJjQ!J^NK2hl#ON9Np%fo!+298DUuD-aB+j4_t*eQQi{l}dQ+SmlAr>AFK z%WA=`TO*RH`ui`2WMpJiQB`>#Ft>j-`QXf5NFM#Cnx5V>&AR%E*4mzaExOxK5?#lJ zIZL-?=Ahb^S@p+Uv!iva;%`qq5Kw#Kvw71dM<-Wn6&)SR&=)U8F6yMZ*sIbVXRsvc zFZuOMroG=knqs}g6fEZ6_t{*;s$R=^v_FD6QXDAm--$gG`TF(i`hm>8VsXPuJzw95 z4b|E(Gh;jQyt!J-$Ah~whk|Od+F$xtyf!Eh3+@As@b47L8a{;0NR>4DnsZsK$!oW% zcAjB^NuC!+ODCddYTmBytg+ad$pYQUZ{=oQK0b5|dJ%(+9Cc(Kuao@Foja2R%VV|0 zX$&M79zU~R&#o;q>d?f$b?e9dvAw;$%BrfWtu{^)B3KL3OC^V&I-EolYImI)3wB@1 ztJ6(AGE<<_ZmWI%6h855axrq22AyNYiV)ZFCSJ)(`x*nk$G`4Ir%!wqOXOcYGg(;n zC}v(7inV#HBqvd6KE$NU?e&kTYHn(30>V>Pu87;GqRscA{kC{l_>kj%4uC_s0*l&| zQga}h0ZED09|DqLe$9yJ*k9JDNjJ;hXw1;u@Hn#%FJxyfaF|Y&%OaguT84Z(F z=5(sUCf(cq;o)0egoXLjGUs@*rutkx9hS{xeea;8s`_%3fU5Dgcegqkva@ByJNE#T zD8G93%5n-ZQD3qZ`;!_=r`zP-!#tT*ge$bnTNW1N7`bcr?#d{&ILEl%iibbB45`H5 zcBK#Htdn*hPPCvuP2XGo^3-m|ip{dhfM^5N`aULoEk%;fJLa+s%XIP{#5jCaEAUfr zR8YwFnrF>AlkC?6PE81qFqUFG*1x_mN@L zi~^~uF(v2Md{px{EP9e!C+XN9{w2{wzkaeKDz0$Qz)#!o_CB)kr%qhp+HM56G+3W` zX(I9s7rpd$(2*A5e#s@OftA? zCkNt2nYvT_5vD8q!xcjIIQAQBJNg|QL=g*%HEEpRdHQ(#6*%`Z`3$ayGUQHpR0eZ<+b0Gmu_CTXi=`tRiT41fT?@$&t z!0Fz&`bX;At&L0WkFQ!VR&8qARU6S>j#W4|)E)QYgr8|cma`ag@DJN`Je^mhr%Az% z#nkS4iE{(a<3IB`cb>f#8fo1XolsncP$8Y{JX>wRCUNxsj{4!TMrkZDY8{nsf)o=O z9{zc9Vl3(0$K`d1=pMIkKjd4ra;5S24|g3WM_MN|L|FE${#DJ~+_%@isPl^E2C>0s-T67k`bP*5}$*rN0p0bMYqKf8JP$wvx-yfVtBn%->C=p?;E!iK5l7RvFYTn ziudAqZfDkgodZO4m~+P&w;GqeB9UCh-1ls{=E+lI4O6520Uc~x`wZ$oEtMMo97aW; zpc{${3n%5Nz0{67p6xQ>7+l36dpF)E<{dRy_VKP4#R?3}t2Ngto`tw!$JRi*Juc(+ z6qOLs67lZ+G=18-;a#q~#_2RG-Gs~w&Bud-5*!CQo?1w4+H{1J3RW(CTzy&Yk`GyO zJbqQwf8Djr8Zk_m_?gd}?(M<)04u@2ldkJp7ZzVQ@Tmt8Z?OIBQ32Ixd0SiCm;!e9 zXIqtCKB9F4%5xUfV#W+FeY(HZ(x(5&^JCTds^7#el=?Zy24Ct*b>EgR;9vBT6fU^3 zuQGL6HukbvT^jXZoh*u`nn9z5tA;HRm<-2W; zDU0gS9N=%an-vL0ax|%qc}uv%0|El>RDF)qmBK!HOgrkuuD~4k1?Z_7AL%P%#OhgT zqh?ZI&M6LZ9z1PPpHY+H%&?$Us<#ka6(+Io!uOjb4YbOX2LR2-&Ev7pX?4814(sDV z%3vikE?bBB8nqwg>THXw*EQx(L~UM@nyfGDgixk(!Ncu~zU=)%v$9I2nb#~&9QelO zr)~RK)W;sFw=0b)Nb}s4wrjC8Vrc(Xx9tJ~oOYRZay0!Euuk*S3L4z+`EXL;l zOaQ_JBXPcigM%p}Ifm4$1_lNZ&!4|_ijIyxd1b6KD4JO4ZtFiM}A$6-{Vext~+{JD&0p+ISzR;pr*MpBulP*ylTlV+Wk zk)b6jDyorae0T0ro@i7c0JjCxqjmeG6Urjt^WP4mDEGgt3918S$TNmz`l+jPSr@83 zw&Zac)}x6k-&nFH5{P%UK^>cx_2n9uZ?`u013{&(**#wi@t{u7d=Mo`JL=*}pltm? zDn2RMg7K0hChD?81RnuV2q8zj%RW62p!R${*q5<)u{3~$MuMStF&2N|zVnBCSF*8% zp>&kG8TS{9%bF+G(h04S<1ZU1W-`*-O!$M)$Q=XSu2boglSA1zqTLZIO#pBlJ0imP zD3=C*l%kk0bC$(`C7Rc~7Hd65b^Lz!Xn$LXk10kqZM?U@Ogs66pP(bkuuRFIyLX=v zJj15z^a?rT;Hw0{hUzalM{cZGug9zT9yL{E<|Vtn=LRMF`@Di2S~%hg8mM$_ zjck`pm*K`6@(b#{@C{#>YS8{$Se7sxOu1hPpMPiMx>3v`Y z>Zh$?!x@uzHyrI4>&{j?e*Ab$0V~(ulLU|L{@PZTPP=g7!k)A5<_DXxi&zF^jdlnG z`z?J*g27*F*2EwHifi(B5czaP(jjo8J{Q*p6#fL@h_H|a$@D&a}3)ia<`VHF^T$Xg@sibocg>aC;F8# z2dXqdCS1K>WAot2lSouW9b~icPWH@8GOgsVeNokOLu&jW3cIw9$S6JvIK5!mp#AN1 z}IRoOrmc6u0K|guZI>*6rI>K(j9vtk~3sI-&gAd(VN~rBYkgtviTf zs2t=V6>JjF1M=?O*uEobc7ruW%S4u6HecKG}pUXPmM2$}!WIC@LG0ruu@XuBEPL86MF0AS5*l%U?SX#%}Ps zRA)GYSG@6xlf`)dKcw%01B-J%OD~lO<#iobP^?T`;-__i@+fMiTi#`g`L8#-sVRlY zs%U6DnTzZIIs&1VQd(LHW|Du)me0s%F{E<9niUT21E$@ImzT<{15IzZ|7KfrqLBEQ zSC>lmIE;7-rX2%o(Dc@r<*)T8OMiVn%aw1>Zt#?W7hGUBW9_wHj1Yd20V1IM;dae_ z6q6l@J8EDYkx+!v>ta=7G$Q1JB}v2(2Az;(T)MutdFFu-X_Cx-y;i?p$@AnI0Zzw`s+L9UD!)+>;<>ZJRE!-es`%Lc#V9 zdYo=%&Bvd(#HOG<`1;pT65lUHc$ioqQ5y1Eh;s1gQR2yEsl3RU1MxK3iT<5d_hCiXvF?Z{NPX)w(;da^=boI~IeL*;e~P zT^2L~p!P<}f&~kXA3HYRMkZSl*F_lSc4n{)*YL7qxisbbfOXqwKEnZjts1AZ=Cc?( z*V2EM$7-2Bi{}3a-{xFMHm`Y(U>Ba4xTp5THI;kTppgE4O;#tG-0R95(xH@*U5T`Q zBlUTY-fW?NeqXRfP_Sxu$$tHM9mG+vb-C&a_RC25OwU0TtF*mcR6_5!|w6{!!6omkFUpUTzyx=BApMm|z)2op;rrjtn`=Jv=-L zZLyqu@7>#&>hr_mUmy1tBzKW5@H0ox!v_z56W41QTwgUk1ShQ5yz#$t=V@ptUthA> zFyqm$a*tds{u;{9guq9Sj^zmal9I>|S2uF@`j;y-`7 z*nY+G<$Sl?)TY&?ZErcq+fQ6rYOgJnQ}yR!H|>|)kOq$R4NXbeM^RzZusJ(D#fP^T zr3g&yx zqOFnFP5GdiI+WuS%3V^1e%Rr4hrY^k`gzv}(cEaK6AXPrJv|kJd|Ie$f?nyQ-J#_2VE#FWq0fI!c8?TM&3#3Kok08O1mf>rJnv`&NMZOUSfp$huRxa>1WXt8x6e$MO{` zHd0U^&bJYMHXSH)$`G|RXqMY4zyk6pn;!`*Tr>T-59zNBP9y~S`*YF+CI8x)aM~cQd?xj}ptGKfz3WrOofiDOHt_tnze(8lJO!M{)lx8?Uc6 zq3Y?=Thr@!x_0rYuuk~g*KpEfcmH8~Nj>-Ku`jNbF}m40pq1z-1peF%e*KQ;XqLx+ zE(}<~i5dg^W{6Ps_^wa}L5|U}`uVpFFM;zj97B0sxt&(~oIEVRZsV4iHyFzU!U#rj z*^_CqQWF;g&*yP83eG#Qq2k2}HV~^!si_ekgR8_3v6j1df8WW32|(auz9@YNW}~Rg zQYJNdb8G937g_Ge9c#^B$_2;Dc&^?mEG%66BBdt7ApuZwvGslWT2incjs@vr z+FthJ%WDJA+q_b-_^U{)c7p0rB$^Atkr4^BDgd#Z5bTK560zOT(JD9i?0*in&-m@N z1uYZcnjvGBgNaYFVC-EZ;iwGE+fkDc<}#Gt5P?+aQfa~N9-G?2Nht#<@%{W`gMx?c zqzaG*&|V~z@ws!SE3+omCXC1a>x@2u3|j34Pt7g+s2$3I(D#K+dy-sdLC{We8o97t zH%kkf<y1U%^4$R^AwOE)mNLNbUFG9YDlPGOtn09x1ZgWBv62?N;%N z$kgrGV_o8zBQ5?sx%U9cw!;qquIcletMeY9v_2u2dKhFDSVMW0UK``F|Xl{I!8-%Psy{QA*(3f<(xHP+N;xciJ zE=rvQ>?Q^X>ZKo1aam<*y3Q{E;yNHO#Qv*okp|n|Iuv%X@ zdCiBt%u9uHE-W4qU}r?iH4KMjjma*Y7*XfF%&lMl0|Ex(-orXe`aLdgmF`z_m`COAwe#qCNDxm+u&9pYjk9o*5DFFN*z#~@h*M- z4fa~DhYnp4qjrQA7M@(SdiC7STq#WQS9;eIv)IzwR2u_!cknoC`X)i4n<4KwHeP4r zqY&c(p9!`C=WfH3>ZYu7XX;eM+y6YfT|NE)Trf{|=v@)J*!C1WAz`Bt@(EQXj3Fo0 zKcorYP{MiPCN(*g=x;AKyC!qFYPd+G!5AongbfmNpwDO{J4Z%GE9>Y4Nn+x&Uy2V8WlzNf1aOo0 zE4@6afWjQZLgWYpt;DL)z@STAqS_A44=@^DsK7|&2$R7{{1;6IkNgTYnv0i;Vc-&? zq}d#~D#WR>8opGVh@30Ab~uf0;~)8IOli9dTmLqsv6F_6$KhEZBBhGIG?Q%{C8r*!+XXC zu4}+BGa}WubgU$)BsxAjCU4~OQfDrwSl*YXnv1HA&c(OdwY#@ zT`o$y?G#Q)Cwp(HvxL$Qxs5^7wr{-d2$qyA;28B+hcF4Og9QWOSp$bi%3RFg#sp27tBi)Ba_2?$thwH8mj&QzqpDMC@vJ#9PTVFT;Qu3fvvzqkTzlkB(( zBqdly{&nl#I7K7uKG~`74iZ0Bj9#AY>Pl;d=Z^|C*aQm?7&0)~szFDe(;YjOqKGKO z)N;lD3uA%X1n1}Fb$AHG-a&kTOb`n7NV+^sPQ*_NHYOmjt-na-`v3rvRnMg7NdVZT zK^pIG_S5ADDG1Vip}qe=UGQaY>B)sv$>W_|uCrfvzdbKGcRl+Au+b5TpYK*2my@Go zpTr>J&9k_4x!-6o;9#hmOmEbJLp2Z6>H5gz_Qkr4M3SZ3PsEd|sX@l(c4MiR=%McP1(?H^Xz-LkO z`bC~ifK8*0ZH<9TWm87)voNZ=^wWU~nkzG54JEDjlYoTF3!3xTpiXwNb^~W%$isx! zuRFA9W~_>pAFS!tbfV8C6>GMC?<^NFI_B%|ui3P-RgETxfF?$Fg(7!paX+x?mIza^ z`K??@$^#F~DFkI`9%v(+e8WT+ype+atGz6$>|E!P+@Q|rrCE(H`;fILe}w(!fzi@N z>o&3pSIF&uhIr9I{CL8(F(rdD+e^-5$)#Q>9_!-A6G3pk&1V9^i$^!areTewQ#CV9 z4#GV}Wm{l3-`ktyQ`kw^5qY7&l;5T3v6Rxy?M)rc~G-_K7?-+mM->#R$9E~ep* zGuw4t_vLyb%GqnTTv;Ue8Byels(!GfGeKEQVjd(}696azp$KTHZ%m-1IF3U+cbOd2 z#217zPYY6#6@A|{L7`CK9Q_$xTw|A2NNw*l>j0e%bvP5okc;EP=XXCnAIo4k`TG*_ zf*3zR-YogFmzgd#OVoXQRkXzjLu4T0mE@QpHv;C&_Iy}08b)y&R6;fLP7U9n%ZuIV zoKqJ3#wqwhvxPqt8gfk{s`mexH{&*MQT< zy#-t%g!k%d^*+YV9&K=IBUxvzPgn@UoP|UaYQ^Q2VD%z0ct*Wo?c(kL-kYPneQaG? zVRIk~-FmWT9sh#l*ROZU96d_-#*u^u!vu%S+l6trq_Sy5DTDdTF5Rj!QiVh~65a@u zwPdo=OYBF<^Q_>|G20j2)o|rqry^%31xkqd0=$+qi&_xjEVIvPH{g6gPID2!itBUr)5Rf zMyUzKvH6Lke*HD{@84IBW^rzX9!7>9F?@1<^bIhteLnjIepp4J47pop8D_MLaF&;qUBsLb5K&4j3;@wFSQA9$fDAqGNg&QX(5Os_6(8n8 z=MBdigpqS>&(JCC8E?--QPHcSbUFDzscivz&o&p+e{9d_wfjFL0IFn_+S-ZX@Au?P z;gKRbEa(i_AvIft&J0MrWM0ac&2;8h4(OIin$rmU$Wn`#kTahC6crHSZ?Lkmawxnl zwHYaCe8kH_DkNbz7Ofv}u5{{VwG8N6EhgmA5{E}P;mgs{)sU^5@54*4F$(TH|J}bY zG_5n4K@3T%SaeF@XgKCqtbRHCmT^tqHk?|Vr1Xme6t;i%z>n4EvE;rf_UKZc_k#C*}CozkS}f#-N2kISmUVcE6Opre<0 zgf3^vUH9noQ?%+{K*qxq=FSH-Sx7b&a18V%_u2gbpk`u$(_!(+SR(dn;Y^(rQFy2M zC@@B;0(>@<$elW+5+)mXvF-D^HT$e{_CL$;cBHyAS1{Ceux(TSYR3ve0*uFl?+&t` zz^l?$NT?6&YglYLw$T3R1TzW**NMo{j)TD4-uVd$3E1%*uQ>w;U>F)s%&_UI18R2o zQ36eFq9=Fh!H5{K*7pqfaFttM!^SYkt|Qty+zwHY@gWz=?D5DB&DIZx)xgYXz@pDb zJorohM-U~0BlBfM-S5EIPA~(cN+6{LuG0>NLoQ}rTsFfjT)(_wC_?^x7MrM0FBzPt zwn*$PZm(8v*9b>Bl=jU-p!VS_I3D0XDp4ws5yQw(PM)klAU;YdyS89OKjt&FXiN<| z8#xFSZ3%NuY=B-D(0C}e3g^t*KUhP9@chRjNAG`Nj9x9x&N_DDgc9mj0~%u>XV%g- z==zUi3mQWn zk>0WG*$retqpED!A0&pqUUeC|6&HZoN1;BJkVyppNlED{R^H4VVA7d*#0PxdTq4C5 zBX9y~!|Ut|fqv6gkXugL@Ith_szJyjcykLQx_fdl0sisH%~lP=8}2x>-3C?mrY#@a zod7nn+mcY7We$EZ(~;J3rLi4zo!j^Ke*MKEx>E zk6nKCwy3>At}tCI1Q~;5dxF%`YD0V^JAO_s5==SZOH6E(G7v8rIvaATu08@Loc9fWPsx778x>VmACfi?T0U7J7&9|C;19+SKjsrD*DMmmodLO9 zGBQyFP`OIkjHgCBN?a!sT+r76E23ukcrX7* zmk;*2JYQ8R@7=pL*on$3B9w@!jKlpITooaCqKMp;G^wi-lB*WNHkQga5$Q5A8#{or z412XG+2-kC#5NuK#yQSCZ|0#)dx5O{#rgq#?RbG1?O^qpT z8udU7c4r4zqFt@-(Sm}4vcRE)P}!(*JtoP589X%}rW&pO3`#k_xVR?iK_S~i8v-|q z-f*=XE_4$CYN_C1D^WmG``Z*qD1{5H0=5xQYrRkD_dtY!v@wP=ViYl}Xc3DFZ_;W^ z2|uofz@G6?P&=}@z)t{01cp)p-v3{XISAJ}WLCP-af4MYN1ht!loSR6s1r!!gx~U| z%f#piRk$DOJ#l9cPJrBTZLJo3ac#aQ1kW#OlPK!sz6mLh)Zh%7l^P_g7&U92J;rwk z8celmHH9S#)q#Q!kHjzc3Kk(x@CrQJ=3pm9|GgGvz0Czl&SSO^jH&HqGH^I3!rQiT zRr5r_s(~6qXP?vqx7WGO(bpVk|5fyWSDU;3(F8mExDW`LR<2|Icu1t}iy$DQ|UlNv)%uL2&i(%wvvoR(qwzy3Ax*X53K` zL8u>s)Z)rHD(Ik$`r}+DPAuKu<0hCj{EgG6;e7s@PenzQunRoBcJ10zONPI-`3mn7 z;XXTWx2MHur>l|zoS4A~D^<@F1?nO03OH+z6Ycmf0dsaZOFos9r~wFU;J2oEpk#%V z^1(1+>-O#2Yn#VAAh3m}q&&*9U?Ei%Kff;@zE+N|6W=eeWy>8scBd`sOM%u}WFA6q z8?FnT!7Xlk5}BmmViu{+iIWP&5oyM1$$8tcNjAV(r@U!ms3C4Z^CoCtWN+AXJm^-z zB8dR61Dy;ts88iEWppGhY56+mj3~;T&sYjXI49PTHfA?$JV4TW%&MX_gqF-N&#j9t%T@SI*?dJX9;5hKwYM^~v-rs>vn`BV1`c%;V)4mr` zQqjc%4?2@#yf)=L8BOexD_Jfq;RQ&%)ZrdI1XsZ}8*egM*{Y}j5!_K@J*-nd;@y`i zvi1t3w>G5oeuoi(xC}+RI;z?%)slb zY-Cdv)rnCwavp!8tEqpM-Cf7AeIr@M*q{sTL~$`U11R%QrImd-zjI~Zh3I+UuxWip zDj|^wcGFBYSFp5<1s8J>Jzd7c(GFO^c~Mytb%oBQ&ZN1TQY`lR2{rwQT<+>9&q#Z-O%hCkhrcmoV4 z*I)U5o|fy*IAEILh{(%BH->OwCDDog{(P1b5YEe?k?^T_*L!=I?tf*}{Qru0=t8*12P9k2Xq_Sf3EChTF`4Vr>$kUmi)RPkdHVQ> zFhB-b`gGQ&mYduCsNQ+*zmllv6}3o?9z#RJC!JUmH==P&(0 zit*(`^%aFgv@S-Vc3O%%yx(er{h~#SsK93wMMQv=2&KJW-ro9i4}Gh93}H#^1gf}} z_}`D8n-QzDha0fY9Bo%(|3j=5{tCQQ~FN5&h>ld zAX#t!x_#TW`y^s)<&XUNKG3^+71>63#dFKeh(n9hta=2P_=^9?)?se6(9px~ddG;R zAXiZ>d?%lgP^nYo!hcnQ22P4igUy#dusXl*{e7E^6 z-0zu-?H^zD`#qCxGW%K^w+jkBd^ca`&;7~lJKFQ`*CMpOD?EJ%@i$R!CTMGEo&2|P z;b4yDir*v1a>6>$$j^zVdG6ECzA+T7ADv(u69S(+;r#F4quc03jUc)R z$ck&+vU&ZqY#e_LxE0z%MamRB0<6-)cmL%%frFj&mI`+f-hw^y*5W^p(}d;~d1w+0 zXAp(+m4qArJkPG;eNBt&!0)$o!guTtko@eG@$#;JY&k4ByaccUNdj!YKtvX)ZNBy| zPYNMZkfzwXcCDYOw1*4?UT!ke)AoPuTS;w91>bA+bDA1`(%nsIsE zkGttk&$=XH%wg}bN6%+89+|$Cr+DQ#BNNR3_XoI%p-nGvzk{OheP7?R^}%pI#QL&O ze&5l7Kk5lJeUmxgL_x z-5OamLIX|}!Db<|Z|9YN{8Qo518#oHGH$M%zV9`W^D?$;B<`@{m2}$o-|L|9#=jHM z;YiQ=v(rOHwPv-Ck}J)buXAlWLdegfyNLiGBvxPR;D{$TU?PlTE6Tc#| zXOFV4^A$hNj2^b-|D02BU}N-x`kb5tXfu%aUAbnBMfB!3i(J?JMli~6HJ-1-+cw>! zkasR;{r5WswbS_8X}yc_n;{To!aOV1-u-<&EjVD5U@hVsn{9ROTT^0my~EpHA#i-}aExhjs1mtEZd^Jag(b?;C*$)tAqpW^_3m zI5mB1DQCp5&`;ypPtF>@`uo|xUZGwQ{+M<6Dxa`P**kT^=fcCY7W|XPa^_%M09cbX zkqU>o{`^>NgFR{+({1;@UYGjU{RQs#UX~GVF+I|P)tl_KPrbGU(ZK1sAb$OyyVQK% z*gIQjBj;BuzTZfW&3r|Mo}Qj96JNdl`?;rjbabxL&Nt;PT(aaLUm&E%BTX~;eou(o z2{s{}Jr9L=-YQ7b1@1bM|?vQ6B9YT=1)(&{YI<_!piY! zr&u3sEe`CnIUFuGJrg=a(DZJGF=}|{@(PjZ+Y%xjF?qdUN5K?3?6*qFaM5$*%T%-#`f4ryfWxhTHl?*@bV-7Lxb8|L`HC ziNXdw_CiCO7>}@Bt372<4!WGWS&{5)jH%Uae|KvI(fOw@1%uV;nEkhF6}XD?(>|Ts zWdgF~_^DIGTQRX_39bL*z8O-suODc3QmN_?c z(9TWFT-cS9OWwz$U!wW@cTjIUz8}x^+U;YB%sIEK9seZ!6SygH*Bd~tZv31Qj)}i~ zNQ#gj$&m^;7U2*%ZUrQF6c|#eqc;TQ6Gg{T*Ws&Iu3YJdA&!5?j?ayQ!2hAJY?8*@&lorF{DM!Ph_DBmf8*xOO!zE3 zZr&uQhq#jAm8yzK*}mHIh&Sg(3TY9{Z8iUrNP%x7naKDsCD+8*hy;>%p$+g>Pc4s6k5_{0_KYFGmkL(^Bvu$Yf!pzltzYm?vT% zI`w1^1%iCSr8?5iK~9MPiN(Kd8#x~$$*x<6c%?>}adgamXNN?M#;dc}`BbWnij$G~ zqtFe^gzG^8K4s#2BIFy~pG$PjUa2Qy4#}0#rF^%>oE&5#Z2WocKzn(3P#wILh)3~Qk z*OG@OHV?jbkez#~pr^~B4~jcqt&RkHK-sGfki zA3RfVVGW?!&lEuT2^x!VKFmhSn>TOLnjdPSgbGEVAXcd*G;&q|wq*37T}@z9OJRaA z98$+3mV_h88C|O6m>P0s0A}c zr16e46u>6`ZO@x_6S5)*MFRlc$s;Exrv}e&RrKi)3`&k5M|0#{E6h!h>!br9+v`K) zsJE3+xbnw*Oas48)E>)VzRjBjL`7>FT?&hfRq=K*lUT!|UIn&e;OywW8&CYGBHS(J zupDjz(y^6J1yCa%7St-8JW`++NE;Jj0!KTdmM{hrPkroj?r7ineSvObGo>LV*MCB- zyftKVbbd8vE)iC-bQ{uWgBLle=J4Tbq`M89kduiMA40)V5i>>_B+)gNc0kL-PF5$z zuqpSq(eXVbExn4DH}3xZm1yu7=`YxYX;p>g7n7|`f~0uehL&q`q2m{%@+YWkWivK<#Ih%3G`R%`*6&9%SW25uo4wf zjY0kEu+F(cD>O{Nff&)?Ld|e=gd~^K%#yobWiop}{hOde70|yNv7!=9zXajHSNO}` z_dKxIU9BgPMj&%VuQ}toYP9zA&RG46q5~~JIIe7^mrs#IjuapKy(>GV7-sz$#PD z>o<)MFB4w`u_=0-N241tlM<5~<|7j1g1B8*1u;(%RuJ(cDXJ+Qx(7n606+h67-8t> zdE9*OBZ=WfK@yt8euLO644@I5wACR?PC~+cf1_ve{&p@q`-CaeQK$YqO{>fo$nz!u z)9^_uVG|H%3$R!Rk{~hF!5SF>>krpN23nyBAf?9AgCNgNZcmy+{0J287_%ca=yA%08HgTYxa1R^Fi*iA zt#h54AO^M7+tlUtme&)L7h3(~Q6>%)2xS{%uYW2myw67{$P$U6081REGH3Q|d+paH zXJ3Uwv4@NEGnNKARAHEe3X%CGR;tpyBq+<+Z?;gZJ#K_#aOA)NM0t?NAJo}6Ie#5! zg~&qs7YXl*hQJfP2s`13ot^cWn&?lCzGEC;>@I3v9lwz6BchMm%?&{9#Gp$0KykWp%WXbZOUca33G|E z;z(z8@(Xr&BS}`H4K|GQwZfAaiXB@ND6aPG*|WSfWM|{z08vMHRRro}!7+CRv8`Mg z3LA49*4-D_kFmPV)jZaLRQ2_GtR;ZZZFVYHvaY)I;K76NX{F<+kC)dMv2~EhjBT-= zl4jj_B^lw7@2B`8RBkhN(e^Hofekl50DEExYmal8*Y& z!AR4uSA+DSL%%wI5t|`YDxy5?hq_oj&#7_?6&MoG+hG!Z~VK`$LfH_}#}YZe+; z>|i#Lj5YDm;EEkW6-h3+R2ZUsKNZLapcl7nubC%IjjlK3!%lB|D3+AXraeP zxCHt?ZVqP8o;Q1TI57Y~w%b49!}iubFb9N;TP%WuEZcFAM>pf(Yt*4GHIaZYsa=)rK2BnUHnWA3+-Df zKy{#HId8_T{PL`UYgAtnO2jixr6N==v1%XObc7k+4E4zBkjS??pN_gSHaSMaR2OxT z>DB2Kog95D7$@R@)Ael2K% zaiP7p>YYkoejkw>=x6V}7Wd@>&8oJsXtFPVq>YuKSrt8V)~rg>r&0!8q&q%1<`vFZ z60Pl;wRHd2{zW@<1N$(2e#$|l?mSReh66zeo=3@HioJNf_eSx47=mqQtrR5tD0{qF zNYZ1u+RP(8-#m=gTu(Kx*`@zFqTuw6TwW=c+OD7JU1=vEZIr>k#7hCKr61-k{@uIFhd1SYsX|*u#wC5r$lLZA?V5I{??oeCQbz-@XW9^+Bt2iD z$CwyB9&S7R9Ezd|&h{XdDmeY?vXb6wgjGC|zYt24Ug+KhoqJHSHa4MZ^!>zq{^j+c zriz)JSbAMHY`z3-pjy7XM5r)4BWD1%X~w@FZz{$H7#Qmsr_oLjSxWvkef4iZsftM zU#3b)*h13AiqUB}qR#u^;X}>>s-7!d80*h4;c-^X+^Vbx??$@UG57yfyy>R9O>(|2 z0?GN!8=S&W)9DO$5?8tzU66FPliu#gfUnMDoiy#(Z2O&?ev|2O3jzgn@8eHqpjp<+Xm?4V5BaG>a-@go!`Z5eRAYa z-Qt>gwnnb=qeH}_M(2s}@x5zL+jQy)>6U(U^ONHuPvIHOH>s|G-lL&^fK*bmVfzD4}NCy^qS92ko?#As! zgq;7gruFe?C>W7p^OfA(KYF^j>|2RdDb@-HSv-fxe9RY1)sWUj1axBdK=e>TB7F$# zkLkH`^{T-ASMwJvC`HnWM#qUZuT#%u9oGpeCHq0bLRQvFa^4K^BQTILyh=x$YvDpC z9+4CqIvXlq%*u;*?+#-*L_+bVj&`4gArCYKffr$R9I7JB1KX`6$9f|;)b=y4DdA*= z=fuNRWp--QJ;b{gz<5UiHc1r1r(vgVpxA~u#t>}dBRP5u34wUHNQ#S(-x2bZw9+2A z&D}o1mF|^r?ie+%=~RQ`mxsb8-)@HRJ*VwFa`Y&ZSiXs&7Rj&;{ZqtMhwj@L^bi>1 zbebh*He#N+DiWe<6nQzIK;X-2%fo&G_}2DbV$JJKt(?N9AhX^*h|-X$8e;M--Iou@c=@eTydk89$K(h%jt`>jeQLWEjjjz-lXf?lL!Ctf|RMlrqrIRGbLe78w=$nyaJRqS4YQq&}K9i+*l@{a}nZ zpwEVO+ECNY5V5!{kDXh#93xhC_r)B~k?M)V$`6u>z{PG4VHdMgT1T@c1~uc(UaYiK z8A?cS%tiH)xBXicqb`P#Ty=r^PUA+!>4+J2jQjf=!h(3DgL+gny9T|*GiqEXC3%@M z5qqlm;-hTHaRNyN*^>i8Ai?q)({Or0wf8yHmq~M3IYWUEF)qh(+F>ava{&LyP_6oO z3$T$K23SroDrsdwwtb~(wd-+3l9?L(5pf#b?l*7UDvhDlYs4Kfpd0eppMReYgEk*U z*Jb1dOgeQg&QO7W9U@xD0sF*pU8kSd`9#+Ym}Yuc)qEa>&p36>)sN>Cag~jtu_qn} z2V1>sF0hZ@@HwDvW|u`nTJGpkGsKZ4MmiEM<0%l_O}8B}+Yfmux~wmTx(|Kv-SJYp zckd?do#Abg0!%*HVo5EnF61x~)2ClhmBsN<;J)2+#A_2D-!cAfO{TG;CfbfiQTuMB z;GoSR%KyIn;*tY>lI{YNFjJWYj1G*Ryfg4%*n3YluIv&3G59gc0H}W*kw8v~O!J0$ z37rQF1JNB7LS!7wYExd3VvJITs)(E+ldjA-1QNxito@FGop8!DA@7{CH={}|X5gy3`B7dDJV zHyA@Q4XP?|6ihZsY5I;4*tRgA}Lf$||+b{|B(lz27NKDL-J%1BUsZ>GK~1qD~*~2%rU} z{~{PW+)>YQ7^IEj;4Bg{$tgv|c0ybX>9<)XYwSU@vrg{iTDT4C7i;0v7=%uBua9C-6|cz#~i=+xGQ-Y zk^j38jP`UoU)&9AyDvH1R@p2XOSne|b0shLF#f0pWJs(S!J0JAwY8N|cC|;R+jw6#_?_pl@bphqR91zJ2NdS1M?;5A8h{6_q?WO3x-vCgKt% zeV0h)#Dr1-;|z`!YE`%*g7`*`PeDUtBI%eDx0C%}?7e4DRcW_1x)F20tSAcF0xGDO z08s=?=r$q}R8lKOvg9B^ZL@%G1q=wZfMgI6HW?!*C`ishksOsI`5SW!bo-ur&$)lT zx>fgnR=sak13Rp>o@YLDjycAd0LRENNF0(!vYD#+{uUlKsArDCyuhyS`%dkxLd9yy z2~BDiqsNVFfZ!>I5*xw=Pn__FHj2Fa$le#o%%&#p1IN!JG0O!@wFy^?1{75tyLMfp zhlyMpQ7R+k$SSHzV3l+hk%T0oP_vNh5fH&otG2ZGM@9G{PXwYR3W334RE92je*z}C zjXV4lA4thN$)JI@5!>&0G!&O2qaP*(rK90fg=cdy`o98kzTDnR*r|}(^ zKYaC%7GQWKqj=N6rBQyH#v0q)gNjugtU=o05G)40sgWM|SxK5O;2i4OJAU+M3M)KP zGEZ+Q2;YXWBc|q?{3WIS=u;Cp-p5{4T2a^ z@W`3icq5j2jsZCS60RXLXL5^QK*uWQHcCeVc!3$L7uFX=9s!mAdaEBNm{}+vY&A_~ z(FTD7or1jl6~s2%Sbavil}Wk)N^-J&8Rm^bsD;zd4Eeoc_n?Sxm~xA4i)Mdb;YC)gtg$W+t4;)-bEG3=utN{UkJr zU1Xh(d+~BJ@&gsLG*)TG*BT|5wTB9V;2Zn&^(euEmxlVyM$1teuuSW+gtUD^L+~i) zr9n0zK4tae@K_?0XcT;WlrBPjgx#=qK(Xs`kAR6gwa;KA7y>@%59*15K!SbPMRF zjd|F>k2l+>a4a>(jJ|EK?bFw5xK8?~{%mEYk=4a65a-dUxGP9}RPp)$z6xWPib$t2 zm(-oDqr@FYzWvrSbC$jOG|UA!;~(yh42Rte6fb%01kK+%rlVV`mRRb~j0ff+K4<2i zJBC~KMz5l_zrnATXEN-UjC4X3a%W5dnja8;pCLNZ(43^auT}90*MQtVuT|zWjQ{J0 z8A3u|x^egVm*xN5Nsu>r(}1QtS3AA0^~%+&K*jujZv<$+ z2|p}Yc;K1*^|3!E4fi*_X5>we#|FVa7VoQ@MYNqc{}d*G1I7ev)a`^PxuW!4{^vKv zqr=Reh|9M=kr-)oQh$9*kNy+!8S(y8My}b#X#GV=kkl<3Ib!Gg473x6-hMfH*lzh1 zhMgRU#W*(aTZ%6C;i~sX^&lbM2!4|^x z4tEFDfA8}-)24YL(drRh;V^%gwQ%zF>({rc&{KSjs6Qk9#1cxHSQ^1=RT6I#(e{&fv79~8>AZGtZiM{bJpXQWgUoe^=0rru#BjgS?Tw&12V+D!%c4Ggk> z))WP&k$XP|5T#H&p=8hgaRs=4nFz!gS?rIJ}ebBo}&|&1mlqE}yE|KiGWi^9aX#UX4 zyqrd!3&U*I@aHr9y+ zx{LmP_~i?rXq6Tbg~90S)>5Y&WoIAnJcd``&_hxw19DYg`3TBLZvRhd+_v??nr~Z~7d@ z%PAw@2SY;~tDyK>wtBS!(q{?&J^S``>^#h1NDeJhFAu6I!xzk*yEH&LQ}^T}##lzq z{^8eO1?ofcb|yT@ucLt|#Dr>kt%nIpiKMq<8U7oGZ_c)w3{O=M#{>hAvn5v&*8AZE zOAtx-Paz40T<*~7^WuNLudGPek`HY}go@oPWbR9DzEi9W=VAm7@3fBF`rVg-W5t;f z&79L>1Wga;C31$5F9vwPu_|q5JV^8FNPw)fLYU#uJA4PHBHR=m2KzUrbffO|u}l=J zdXDc#GmM5~OZ{R5QV`dksqI84tLGWHj??MxYg{!PD>kD({f}4ZUcXXN@#p2MSBpb# zg0||)L`Kyl|0@zYSVP6Q9XG*EN#^f|A5J`1JMi24u8T4=0|8a#Sb{$Bx)=&^!*|33 zHOE@lg+i8()}KATVjW(kBRTwS)rd(lmo+O&06bW&Ga{Ba@@z23J*?e@EH!+A% zcC$P_P;KyYPJlMZ(7Vg~E3F zx}BpKI>W@XoOL*lkr`jjIGY#zAbc-)!*z=c}QDa#sgkZBCPH{&W%T(rBfO}=XjG8m^vevabKv5I;{ z&UcW|4!_d^8t4%YpX0`qT?|J0a17@mi%l-Bs(?#_a6tvLq|;k+D7m+?Qpj5dt=YV#b~@;aXIBwx;xoK z(dI7y`wZwU^QDY^R;fo)$8tn|BT91!}?I9Xio-}i@JP>CE$p$iKmtPs%p zey!7JI-!gqXaliKoEIs+Q%7UrAz&sdNab#&z((PSpDg#Nmo9&e7L!&1#{<}q{nf?H zwU0hU3cChAJG=rJZCitEbL82ezRG^{eu~b9BV5CdcRmfybf3zq8qYN{zQ{ zPo{u=PeW{_v$=_h3AKt5DGp!=$7cjWWp#D@=?YsQG321qk)Je>drTcE0995xI)N}T zQ}aHDfc?>H%cXX>yqtaMz{Tjqa1Ye}&fe1yhPyrGl%M!S3 z0C_rracv4G^yO>UDuOzP=a~mEmcS^YYQXI@od105tBGOw(*$%f13BhGxj}Awh3^WK zAv>F0YMQ-|)bc`1F=XIOlHVbIyY!wgJ?np zJkeQFCubMoA7C<&qizE{Z88V@G6oTwKQ~+9I!R?o;69q27Vxd~5BSW*Fsl2JbcDee zT=Gh756rW40q<-ys&F9K_{ok*gaIY>mbT2|=QofJL*Lr|=NkbuB7DHy_*^GI;|;KF zkI+@G2UofBJDwg91i&EyB7nNj{f|HXxCi$_P{>trj+luW+-^H(qxc+t@XgK_P*iVUl$I0c-&@a|8q> zVs$`@{5QY@Vz;bedox6cZCe zJVZ#a&j2z0^ujy6F@jy0JJ zRbJq|Tco}hauLV}b^OMyTfwah8p_{ee8sP@RxxWr+0o?x?aVAQo{$dXg}HIObL9D3gHn(fUl^9l zP=^8|)BIbDK=`BiX?Uf%GoTm+h>>V4nI;b3iA^@yPyJHdB~xz44FPcnx*jXF)p$!v z*X=Y6s}#3Vk2}6O1A*EXw1^@NEvVUu&4$Jskfz!Pr#*=27d`-iDIV`_3i?>`F|VT* zYP3)T1zm&&{BFoAL8ohg)RM5jYrreR_cRwJ#{&B#1+PJAmHk!sa9fWET}?1{agMow z&WPg5)BXV^1Qn@r7ul}e0E%BgWrdwWHc{kpPlOQge9sh}`qv;d%|UGnxzp8R zAdUCV#`y*|w;=i@dx7ejY?z;$dUv4X?Zq*ZV8RoMjE;~w-WP(}Y50SL*R>il) zyh&(N5qJtZ0NJM#F%|?abS=@qKLqlfNzk`f)VU)81zw+pNotUvi6T^i7FrKKAM<%h zx&J#jRUrx~7TA45##}@RqoyB1HOke8n{A>9gC#jGfHxW4>}>7yN80Z-63?XF<+LF~ zpJwbdw9IlY(@fF*5gmGi?BU2y9|07cy{8cAV|VW*$3C*h!V;vT7rw-jUo~J=?<%Gh ziX}w8bIP%05B3TZ6K=3KX^`W@0i1k&&#)Aqpk`1a(&{A0n-18d4p3%nd((!EHs9V) zXDWh>H*3a>ukBVK`I^<-@Maat0c1Y|N|?U038T!0P|bdE~Z0;#vfk4 z{ylczfi6EzWXN@m<3RL}Wz$CBzY=#`}f3?wilcc1ZbtC^XCmyr~&5naC zGUw*y#1P>q3xjVpH9@d_an8)ym)Djgk^&3W&;vyuGcuC?NFP^RNtp(!XS-X~sV5{J z)fMZ!F1IU>22rG3#%9jkxqC#C+CCrRN}M>h?BC2L`}_JJGVf%)06)4ioLHpu)^2B+ zg8VfxEIjY^LOd?D@9OY~+|RC?T9#aScF;P@Nf5In3%)eH0Hl1*y7;-@nxi{c`uX}= zEnw?UR_uOnRQxSe8QwM78{MMpU}UuEe91$v_wV07vne^M`Spj|-;d{kD*WbV3(#&M z7q?5#GzEQss6`6}XeHU1PuLoS1yA!MSgA{G+IBqhXg*-*wAi9MaHU0W^1`ylP4@22 zWvyniDpj#Z4yBkWA8Cj##==tq$yA6*g!wm?A^N9a^OLKIABkgR2v$)Y>h}Cw^VSJ6 zVH{y(IpqbpX+-xg{;VgV3;tJXdU_rR(Q0)aNR+BssWan4)#EKQm)$wn?c8ClyYh}w zv3>BMPmYVFZn5)9`xIlLfy2%R>{HGO4HUSBe(CFKOz9c3UD>l`Qu7PdNA?>A0tc@q zhLj~%876hv*0xk;Tkq?bwZu|)dc(4h)Cj%1Pa{1x!dXDppz9|XF1|>C&Z1~emW@&D zKJyW@B9fncxwgF_3e8{|aG`?44NzRTZUeS=S;87gWmW02_OMOCOr(Ty8*TTK=>YV_ zp6?3KrlACkT68#3*Mq4}Ei6FL21^Bk&31=;n6(q3=~Rhk<6b0b3uO4`%n@eJPK=2p z%GBQfLYdMR+!o<`)pgno&)O^rMU@l=y6ETol+*7h^_yM4a zhRpcsZ@gLl@S53DJ2kMkcmyn5jiQ{U>3#OnaT8J%7X>Qt1YEv2=6&RSg8i;5&b^Jz za4&scZSa4yT73i(5)ATk&S@y;u{OCyXl`=X*cKo27}5*{Qar2)_i6e|H&m&^TF9kK z6#>n>rHtdLG1#9psOoz^L|@4~=m&WlVE9P+msphax?o+S%VM-)Vqz5dUBfNEdhL6g z><8kJ*P%E<2ef}-`fNWhFFByd?fT-&Fa-iNn4pV2Psa}Z-X3AV{c~>m?)EGVwrvVQ zPd5L0(n=Rl#@5Q1T)A>Z#d_}45{rR8t1;uoWwUb~lKRUKBo85nLxJL6_V?k^VD1W} zF}2?%e&(p(JiOGMN<^|YVQUgWSSM#0&BP;eI4T2l0$w0(WIpH1)incufY@g^)5!QH zH(YxsYWC%5*x?w*cBLA-%dl~6`oNd4X3zt*4itq8yDm8AD%UQY4B&%IB*|9K40Y{7 zDdX)R^yHGr1MiQVEPA)u@{l#rJ)diW9%@8E27@X_z3Sn*5x$a_-sz`o;ouN~k_F+3 zr%!*7vnXyiwYN5z9FwN1A5$#Z+P-EzFNR0=7k7m==UT)FOsK zaL{@#wOpU);xTbJD>3s+0|%1H!}p2q9S>_orcb36Yv(=IaOLq3?z4|)ebPV?>L$1( zzy{wKGlEFU&$Ba;hPJSL(3o#-Jb=na0z5XUITGgho_oc}yq}}DW?>mQ17g^mYTPOU(GIFjA3N8(7K7G35${vgz zRePXs*b!w=el#O@x^9BXX?Ad2@l(IvY)*bOOw&p)ukns6Y1*l!E~$&7G6XdnXkaUt z)Nag0pO(yAwFZ0K4n!F~DoFq{Xg#>=eij=x9Pc0Xy3mZ{HMOfX?+=>6Bv=QEoD$&K z;xOkMHx!Y(16uUPrPsmW=eD4qH#?YZvoWs!9K;3&DSv{61X?rGNms18riSla_gemj zYim{64mQO}%2YCNhWfyAL+e;SZW+>SMeQ|5E5pmI)#vFpm7-wV)R=*qkG!O0u77eh zd-C_X)9Ffe-2~^q8TC!ew!=j*0OS#KZyvJncj*c-Q+g5tsDg-~EE@jW>+@ey2MzsA z0-}`$vOQlnYT~%jCrU8LDWvNm3tMV3kl_c2r$-ad$H3QVZWvqEuuK}34~K{g4I@pp z4;poB-6v+RrKuo@UVu8!2MR_q^b}&EXPbjhZqjLnqNxE)Ga^l7PY4bEPy*^FnU%AA zkVR*HF$o!5kqFi=6m8-IuQJlovN2Bb)}5ElvDjlmOppSS^{UZTgAAVdf7y2u%HC!} zy1~rCT&6Fky@|Euf!CazRNy8>*2EZKR*T#v`4)u_O7OGut>;%FTYG|1p%$_u^9V7V zqGwUSvD^j5x#_~{hm3BJ5tGCM=aGCLyoe(JqwHY!`iKPgmKTn1N%MXBR0mljXgqh6 zWQtmmL({?$3V8i($A6^}Q5dK!_=iUHU)G`k>N(C@Orf>tNTevLs3}c@X_+9&lzK2i zX9bgP^Fe_Ujl6Hoz^8rDZzDN@!BPv~^3dyqPe)#sjEKh!%W>kCx(|S*kQ#<~aPCqj z?lLV@t^Bma87jyG!}pG0Dg1Bt-`ozzj)UY7M3NgYX+mH3EcLM5ihRwyYvmd2JyOPj z4@JZ0;>=+4fLM56BV?P_+jZb=ha8EJ7gY_-0o};4=Pp7vn=i9Qe^4biB10w!WV#(m z13f`B$2-mp6-N%3(V^t}$V1vVK&m+i^)2l&Xm1(hig`d3Llh<4-FmQ6Bh==?R)|D@ zc&|1cMej7T`Wz;!9Z8Pr@;of9I#_~>3u7o7vK*4J&E~iN53*4n@Q$LBuQTpSY~EZQ zFLkA)YM5)(LZXu^oXBUnk3T>11AM{SQQ6?|*d>?zz(Voq75=UHe(GNM89gn3~qlO+5Yys;f*?fs4?U|EqVJUg&ndRmo4ylqhgQ_)R zH^IrL)Uwi%<}P9cjr8u;v17(?hEc)#j^?p}ZX19=I)8(a7qK$|bM{JmK$P+6!MdIB zyvsxVduFe{X*WBqtkX~#ANN0Sm25+n*68hpNdMQ2fHfFUl})CpHYlw z9d9Y~i!SLr|6AUY`u_9Zn2$X8O@>s89_>UYwfF}Uix+loD0%{_Iz3-OPI>dje*YjQ zX`O*MrwAjAiGZB@modb;b)`ai9Zsl4NJE5S^I=)HYN1*4!}ZcAA)Lf6N}7Mv?|xu3 zn^C{dOnDH8&LGC&Y3GGsi@d#vN%d?(`>{{!ENSSw7$!-o(1YIS-) zfxQ=WCnC1X?*j=zS-VHXGkDXQHJMdyhZGgtaf(11_101kgT9mJJ~bl?hBZ?9XU;71 z><)T`bw7K-g1zM`+yk99ezmdjV)A!q53)ktU;F9LNa)KpQs}-7f$I!2SqIL=9s}q}`H`knWrf&0513~Cw)3q=V6MvrMRL%|Ikr+;WqkAc^R0_hX6(c?|`2K{rypjnzk86%SUIK88TC#z@O~C0J zK-0~Euu^@0*=U;~M$y;Zc82~(e&aw@wiq!O9VIBx!r&Ci%%TAmaQ#lsn9jvmh31*u z7%=;-D;*_?uL!eAa&afQ8)`VxY&F1lJ1}bcNdR8fkU#{T#6`_TPhj@36%;kM3k-|i^kDONHAU*1S9m1=Q6Z(J1LBjt(S%hp{GRyuZ~o%7hh z4PI3*w6(QGBhj0!8FC;QzZggau&H~3IPOyG%(0l|HwgjTPaR?kT<-#REW8sZ4 zH);m9U~2}e1PKOUKiUONVpI$dNZR@_Qogn5Df~4Mv3q@Smrf7gr8NMp6+p+P#-?9M z&vPy@`1r`>4*AT;jmN&eBS`u+sx%rfZu8`e2T#yMml$$`+Nw}!Wf>6>(Uj-1kaIoC z!=6@){b8*s-|LpW{@%h@X_$zo5IN)vH0`3^q4m3}cDBwym$`MwZf4PF|zivk?jLtuaR9i>kFkVVX2UeCD?jEl2I47C2w8~?v^E8swjP8KEob8ZTK_65Im7Juz8XE3=6EUbFMa+Wg5j|2$w zw2B)HM(CeI%(Bllp7sVWR6%#fVZu=1W*80IhAnOe1(r0_-v@DOqV8GdFq?~^KC;91 zFqzBQjd(eaoDhRyF&s0#E*KLJ-qh6Je)t;Ujs`avSix|R5y-`e9vbxJFf9s_eV`J1 zSg2smtjPHAVS*A9bfn?P{2CL*96JG!Vj^$Q!7Uiw<+XbhBYGqlg9JhOY~>*OcB|#T zdq5SW5+ttRi*LI9mj{>JwT&=VAhe7L@9AWlSBKj0!iuSkis3nNTa|#bWDewp z*$I8H<0tM)Zvvv5&B$&C9w;A&B$L(1T?Qbb`}f~+ydKe&LFK%krIci6tKE;IXp|yi zpV>(4L(>g@B)e%!<*ez`7c)sx7Cr?wl3w*ohBowQOp3}mvs6%!w+TW1_g}e8u~d6; z=Gcn|AVgoBG#p22QMt=w@_;{Ynl$2IQDqlu}?BS&Q|N+$U@xU$G^ z`s0tSNPk#9oC{v{uM6ISNY3&BwpF|@ZBhSe{xY0t01A2&Bp412!*^v38V$+qlRzFm zc=*1pv_hx6?#DOyq>d}~adL&Q?&t64;z@4Jol7i1^uA?Yn3>3M2)z2s?ag7D zcHFCnCuGY*_Z3=Mff4qf@7PwqaiS%%5ywurg7p8##~oU~VWOq?lP5};@WfQ*)(pz6 zXBROT&75KKh>&j!I3uMs=P;gg>e^{ENbh6Dbo`v^RFAUSS2X;{v|e!`iYZF z7EF1YlET-7>^LcB?7uRXZ^Q&AA6iI;KGzY8VRaD%hyjhy84UiR2)yX80{NERPBrC` zFvy6^gzJgtepQ8KO4>UL*TFqk|sV2UBdtPLtNOIt5>h8M(cyCb*mE=vm%rZ z3@3ID+W@VtD}qJAaQ8PsE*zya?lDb7)VEv!goFeFCDbFC$Jtc9EM42*l_9)Np7Mn$;uV`FEz1;hUc zKpV}K;#S$FY&~fAK_{{vhj#&aN|}k!Rxt4nDoB@HG42Qbaz_E=@Zxd~3*X3#y5hvkT?UoI5H!LvCTETq8NR(P(RM zf%a z9peE{{8O31cyz0ftMr(RMFC#i2&@S{#f?PZsSRT#cN?jRt(EAFB z`jDh?%+X1#kN)BTq@5AP>A#$=ve4KeF(-N242UdbI7}o9&7)|i3lhbFyd!9mHYx)! zn)Y*r@-Vh$o52?M#0k!b4~*1;3B+vJU{kdT0$LnrTF?f=CbLf(nv%>SlxAM?^G(T>Z$SFl4b{vPkfFwHU~j z3ZcaWYLfKR;Bj2X0!#~niOrf-Q|WOuZG8ntusT?Wtx~-dl#3oQo;40LCUOZVaAwHo(Ab;@nmaTu{zY6tbv4;q+?txv3a+qd9DeMlN5bs1+<@xMgb1&CF_$Yy$6JGL7~tn4+uNSS;T*oTICTcF2KUW_30nW)_$&ADmb6fJ3*l(-??qf=IEKzdnmK1Fi! zjK>m0fFNHn6e_0|)PU4^2Fr^?>VyY@;!}t5TsS0}7Ra!@yUcOtjz0VI0tw*Bv!W_p zi--f%<;R#{njHYN(fnuw;l%J=!d}=N7XRbMo@09W)BZ+p(#p@9Vpp z696A8%M)zwEk8vg#&`#c4H5_n(VUDtAm(4&Yt}HleyFQ*h2`qx5`+#Mr(mTlyse0f zf@{zL69VMH+k#X_KzVr(gdLs*8@y1>Y&c)$BB~mQqeA^~8zy(2MN~FVL#Ffs!>-8Y z4cwD_a6v2cbffbYT!@MhT==P`j=A|{7kHwLdHu!$&HP#i=xbprp9(Ov@D-akYk&#^ zUw)0tTz*T%kFJ;l#6GfQiXlXj_u!)snot9PB-Jvh`H zj-dSzBt%H>nq09l)xb?rfxjH-OI?fElD+LOGOV(%0(Z8xGS-7;I41RxOkI$P52!8h zyrH=gpmgNdtwB8TE7Z;2*ZT{gS zW@+Qz-(47dEhJ38G*+4P*QLF8VRi?MlAvDHZ`3@*oXRu7swksz1_RCyF< z+H4{nEBO0lGlaNCcKbyMi8L}2om4Wu*N9e&%0s-@C3ahbU15g%^0>zlCgxy7!>A3; zg-@|Ye>OV%GZ>aIi`+^?O04|SAIRZ}14srnTkCbU2IYzbm2US4aRGV8uaep4{-L14BGRa0Y~! z^+RgfeV$nM`bHAR79Ldy$pGP$&!343XgSYS<2BffYn;3*ewf z5h7_&0a4qaaUv`gqGQ)mZ98a+nTlvDgmw3k$`XN-NZ0|8DuO($0xdIUFzVAp4p1~r zVx-PvSdAC1nqm$X>>)IMa3a%Iph4y>}-X%&)Ni?^Ao8vXm9BL%&E z-!1=mIB9cxz;GpMEUb`f;o@l0EfNU~kjUZ`5%)=!`oQ%pzfQoGtKw+rBL_Aj3#2eq z)2;`X8x3l04eSsaJq4&606G}YAAVk-=>Yg0Z8-2zM7nmgBWJrShG;4QOKfK@e%}NP``hwVwUi;M$vzWan!jKHP7tKr`1C{T z4o9prmT3p8`1zqVSTP#dT?PsEpf*!5#=3fnLJSP_tiw14HWBX>eDSy9o;B#qhZm=V zCj|c?YK4GEio|okID55^uP>7Y!Y5WCdOX-055Idn`gDq#hh#_v3X>lUS>79@ry?z$ z-=;^K4gq$39!&to{MyWIaN=d^SWR)!)e4~dvwpDdyhzMDVTLu4*W^)j*Qo9YwL=e!4H0v;^Nyk#)3 z^|gg_T)nTGW|rqb`_hEIA8B;DLh6GdFuh3tjdL(RTclAEaXJ&#V@b0bQefIpf*NfL zj`d!8hXp7)*^=m_w*4LXJo%TeYW177AexaynOfx7i~6P}Tefg5c_{rSU%$bl0q(?; zaT9N9H`tsEd$DZA3MD)!M$m-)2qmm_*7FFJHZl$<>QWos1UExTuUO2`gzqEo?&=SyjmOR&SK6S?FRBf1VVhei?>)k z8X{!lAP@BE4`$^`E$*M)s`9qjT6%M$N8?V`m8k;Ft%^DLy35eZL9~$x%9ME00-n-- zzd9rK0E}+3SCvGPln3v5`c{c?b<#jX(t=eqkA!H;E-uOjkM(a{*V5eG_#QaJLc6p4 zP^Lp{u^H!bAQ}xDoN!jT2u|T${SxnV9gb8KuV8XP-V%sinl0JcUD=OhzOB+~N-Nc_ z+!N}-CtY_pu^`M5LUJM&5n&dgkvu%Z({&NPOh1iVQ}b$5M!HJXzaPuNa?Ej~K@i zq(#J&QF(H6>q~Bfakr6cqVE!mBwyV4+k4&gLj~w6px`gNK_g$l!Z(W!Md{88)!fME zuXS`YOhjkZ&bX+#I-&g46>YT`?D<>IW;C1l+V_0@NmG}iQ&WzI^Kywv!wAZ#)Kg6_ zTmA1J>MgeZYsdVUdNZorM&ohwr#lV86_Jjw`*6%T^6l$qt#pw^*SYiu1|@BSx8-Ln z(56vkV35?}L?Wr(o9Wl1YRKD#+PegnY%Ay_0#b|S{YN4}{Dw|po1FMJi!ezRkgX3> z#=6z3+g>E2O|LW1*G*Xx4eVXK?Fz?*7@$+JR*8PwGnIC?d?e~x>ndPVlK7?yd(rjV zPapkF=Aj=mYa5LRarOt{_}d7tb=)GyKVNm0I8{fzz-;uJbXQ^JQn<9Yy}iHv=~VES zdX2jZEMFI!Tv}?IHMe&qHOz^FNSRA*+?oj(!khUI=XAOkge6z3Ki|VRzlMYz}ha);* zC=eiip6zNb#Tx0{1l}2$oYl!Z&NZBrP!f<7jE1_Dy5_+Oz22M7O^PKql-8GPqa{X@ zrsBF^=($-HTpYVoucZ)&lI`;Y2g(l^_CQw}LY3dTw1qgLY5)&~ZZDGmJ%jz_gH4K`PWq|(zXvO%x!w6DmEh|#q`k4( zxQ0P5bY{W$%dji^B1?D;n!8T=LeYqo=R$8nYI0B1Ez|-4 z11nc2q&4o^z8$iI8ppt70#S*Y9@t~%vPs3T@Wgp5x?;tOGf#^^3tE$S07a<=(k6@8 zkfx_PI4m>aC`*zbZ4Kn~MD?RT5WX8w(!lPY9*^%uNi#T&+QBqPUZKyuGY2>Iq{O3y zSgolGDo-a_;G4pfBn+3L{If7@Zxq*|=JQEEeQ7tAmegQstO6Q6rPjd+Ohk<(OEV}y zh|>Z58vOfJAHKg2L9J6y>jOkbG7?7uLrGNf)NpjF;`Us#eOKsKl^UAzsIuXMo!+Yl zOVUSeH`D=t^8(E{B9HQo=t@xqMlv6KQQ~ePS*pkqN{X5f_Ru%;{xYcP00$b^83)1t z)h%wZtK-V}<(pn5HMzaLQQa1vaO(6izqsEwUi1=$n!wZ|itUjo-zbSE&m=mX@o?cO zwiA>v8T*C#J#Q|D#T3syf6l$XqcAuX%jpG1P*o0Ms`S&c^KGAW?UtK$>lp3e4WS#gc7DMphI+F;gO@&d;SMY3>)zz17>us;|Mx^n_ zXC*)S()2bjU4BR5Q%0-B<}!XQRq_MRve$H8f|bbQ*87BnYN$`nzP&b`JWcs+8rQ&Z zp0q>0DaG~dPgfhR>F+A;0cgQQpRfF`4@25It?8Ru6;ms*(oby{SUN zul4zIT-i08(wC;n_*1!sSY*=Ml?nKc^hM`-J!BcJd-C_aEDlkVWU3BY-+%nLsk5>n zH^>kC26!owhi0_W>%#X-i>tP!XW14Dy=g{C06LD_H_Niu=y1gC*>GOjS%1CC4mSrI zoAVmvXUEJU;tl9R#topJhvEw-<(>&jF)wV6ELi2Nfd~Gk@&Tsul)OyI*ORP=TVg^r zIzzqXj?)!S5?P6rgIqmsx(!mjJEk24Me^+~%CK4lez7~&dYyBckn*uY?Y2nca7G|%G(8a}*-_9u zc@TScgRPy`0xeZA@Z~`M1&$m*!YvR~wVk|i?`hEBeu0Lwj>Oc*K?n-p^2!di5EVG% z)S=$K^DSPWPZ9VixnyH0*S?#pr3CKv!9jR?2hY{CeAT4=RYXm|NFp_kW8sZcnAD0s zK}$Mdss02tI#V%*ZxCf<#`w_ohnR^&z#I4) zl@=IX=jIb+vU#vO!`_vX{Ux72-~9Y?9LSajfhM)nl;HKA!ASu)ZlZ$l!T zYBwhX_9%2rZtwgV%NedffHhLvI=myB!^Wgp1VjN?Ad`1@K4yjmpaamdpAB|GfSSo= zc;vVByb&1iLQ>QKGq!}%Sls;p)R9E@%@AAK0=bl69aOyurxH-xlI@pX$*yO?4NyCm?UM~P+3_iI(hx(LO(y-bWUd9n08mYGB9-`?K!{*IS^#u>MnCH z9iN??3w(mwHv}ufBb+cO9`?MArvANRZ@GRwGg;Y3%1pGU%3$w~ay+3uYma%&H@H;5 z8Rr~LX`ozz6i!VwvRFZDCiaj6I{}lX#iXG_mBy1#QVJGjhcOx;IwWGYwil8IzCi&{ z72$(VyVXvAj*d_TMFu_+kEgmP1=j*_(w1qhWA0KNT?-)uU63&<$hc9I+jY;V$p zB9{U{7KEDO3}pAS%Yt9s{T;yy4GcC>_Uh_w&>B2S#M;^O=2?Bx{#Gv)`Xv7cHU|0r z)19P}2BnTa$}Du19s#ebj@ZN>_bWpIlq!N0o%oDQ)Dunans}Umxsv5pTzdG&Z2K61gG%Z>_u%)0=v2&kB=qHzGnNihYf-tO_)dH z?fm5>OD_Bw1#vsYVuJDE&({3C4FjfYF(Ez&G2N zp?L>BrFagbG}6s#aJzBvR}`pRM7q}iZ4k=`MUCo)!(2`zZ$zWjS9YQuFc{(=67A^> z>?&s)B$?JJ_Gb+CIhU?g<)6EP@~9GbiM4B4Eh}9raEb*Ui4abSn$a3jz6q@&QktU& zl^yg`v>3-GncC1!qY8Ov#<@}qzZ`uDCj_l3Y3X>y=2gW20Wtbf7lPMS&ihASq*?)% zd&10;bG-R0fgY28>5_M7d(z-(-X^SR)T&GfXihLhM=|b!De|)bwG~aQhBIGrv>+R; z8uUs?cO%3^b0N%VBliKUK++|{DIg1CQQLt&IWH)Eb`O@Z$fuS<>)>JBV*0DUlwJHeTM0gb>`@K(R37!LklJ{hPD&+ zVVI!u*S#s02Oe*%GL6F^3GdPvlZG#<&x|%i^vrZ}bnMteH*4fGQuk+8)XM7;Sv3yFvt0nF7W0Ow79S%= zm7zH_#8CZ>f~t2$P2!{3i(oz8$qaw%5faTw?y~3EYYOf?c4IeADP%B|rw)^QV4J%! z5RGoZ6s{H&Vz(rS5u`JG%ie;9Tv>gVieqf)#=PQDny6I#{g)->k7F1u9Db8!5)yf5 zPMb431?M=3|8pacra=lhZ!}n zIX(S?q@yqNy)A?SaDv*=qaUHKDnMqHDT918a}{4J=+EVzLR<{#p^1%h<8`oBGi9(} zGiiwK=()r9O96si%<)*v#HbawTpbq<6S&}t)NiyX66pcMWSK$~$l;5jeAL$4hNSHu zR#QEg+G~rz(_-BxhSZed>nD&wzL&85w+rGK)AWY#`$5=5+#_Eq0KA}Otj1OtJ@OQl zu!D0sMNm!IKDd*>L0ND)81m5*_!wUGBcvRl>L1U9q1q<*dqM27WeT|Hl97w%16=z0 zb!aj-DrZSoN#y^({~mHYr_xV(ddH6+AEf{;x5hB%DH^;9Hw;oZ62VXct9*nu8Vb&6 zl`AUL*MpZCjFMpn@HMi^265&rI~afzSK!!x@krI-IYaa2D+iH&U~u}`iizOR4NsD- zB?SbY95kC9ezN^c;k(N};}X=T{X-%|ORXMyI>CZ54vXFl&7)n!{40(z?XRJG`F~iW z|6d@`zHC9Vg|cZCoJ%E$Ny=bsSt%L=`c&=TLa-vNmLgK_cK;_ahD-F%Sx-awPNm8RfV(|(PVu8@SPkB;)v zwMZ=ByR=3QWK$p)zhaLax>)?)`Vx;E%`s4MQ7ZlFB0pz)_f?~>C~t$wX7ar#mM<)W z{V`*_HD?#KKB6PaHf5m&ZxhmuaZeWDcZ+H{$H_}W4q`rW>Qq&ff!H|9nE>XtGlKs8 zg}*{Uhw4!xZOTioGO#|N5r#Ta^nAXVIQ5AEE%{MqYj&mUn z7DGQg$*r#TjBn=5N~6vATSh+TKzb>b>O^{na6x&_bkQ=-r#NVRBbGk$>G^|KE^G9= z+~b7tA-P1~>p87?00rTD$Wj6>yrl6Ht}|C>%3)i)uiR?RTgdrRiwc;eV4bYDg}z?) zdYk~Fva;nZ5bNOK{E8qh&0yBa$02>dsI|85yF1(3=^jS^k{&3LWSp-_%c;Wg`t>zM zzb@w&saBzmzBiCj%p{vNg_=!OY|c$<;Nsj<5sgBJF1KGpK3_n)(62%_$Y?W%;4bI; z>)a9#&a>H%-#AI+dqK|Q3LcH0kLUk&FJ>6Tf0y@u?CLt__dkcea0@mG%LkZKnoi`{=s)lx z4zu~BB-VHWJ<6>tqv(UGt$q>K7j5^0D2>%JnjJTJXQe@ZX z*quMyyMvyiJTE+hj++Krn#Yda0*9W=3@gSgfcwd3^u)=A6n9@#SXds9Cm%!)T{O$* z6@!dd4+sQvLh`5(qIT8!L@*xeT~swW&icq$w5@<3qRr|8A``eQwM!36yL^d8848*bViW-dD#?<)^uOaZTV? zh61$PLWX_lS)OKxq8)_0n+xqGabWOAG{}gMH^gTj)cl;eGXPQ=`Mbb~f@k3#nw>x{ zahNUD1o0=8kB*XM{>U2DfahBD#G>uBf>~9zZYOB2eO6@EL=!^s5(2nMMhuYVTu&cp zMd%ukJ{wJ*MM$_&A(2}Gb@Q_o3XA)R=hL(^^v63pAe^N#tD5s_KX4dmX>HmNdeR(A z;$yxx>43k=dhO)Nj=*kI7munKx`m@
  • 6QHazceG}%!*l+oFty7yZvZ^3NG;XSebBG`H5y8qAU9#lOVOJ<*ag38q z(`$$9Y$CSOEDz=8eq6QJR6^@Cjp{_JjYf5<_wz~UDWEwUCbhHy zNLB}snSulXR0t5mm=*aU4RHY#3pG{%RA)TS&Q0B*xA>54TSzfR-%-~b({0WmyQtz< z13ea8J@9#R4Z6`br!g#uG#t*<8(W)LEyEsn$^Q2X6APS|wp@*q-mGesz0}sqZvCTA z^HBtVknV?PuyJ!JxX3g+5#$VmuK9W*8&j0mk0bgfq1(+J0Fa1#Owbs0m$8D2LY!~i zQUZYy0Icbk*JC{Ur?23hyl`XA2CWsE@rMy|iR#?}Dl&N1G^>nA>EL#MYqJH9Llxv* z@*GFxd5Soo12~5SF+}Ynwlw)l!xzsCtAM&YAm;$VI0ObJaX85QOWwX4{+8lc^TzhI zl<}Ou>@atkHrmC+wV``Lox~Q{H%H{qm@I0APMtC5kzrm zHjE7LY3Oe(!`uig6eFwDOJp`iZ3@yzpx&kr#*X3m^nJX4@L0bD8Rp^1g%UcBAV`j( z*x-avll2iWc2#`8Mt(wBcC*05u=-S^1^xxK>HIvbcZ1zabDP1fX-6Zc0e_`}4tv_C zFL9WeLzEhD5{S+P3hhcndR!1a0~E&jCA1NTz0=tnpQ`&dLwHUsf4rD0{Rs zwLAq#FoL%~Tfb@9&S5S9m>N9XB;29lyfiD*Ao|N#X7l4pksmOg(7{7Q0-R6%eN>wu z=55940Z4>4YPQLCB=RoUeE_Om;_AOzn}dx?d=eV`N^Ks5K;8x%p%j?OPKLa0pCIU< zvjh4hhx)PN$nh1uXx!2aR?=Rp4lX0#qsv#XLg4OGV)7PTO*9k@jv(q&R~1gNTTlt$ zmJ=3C=&tGdm?a28H2Rdd<3P*6j#mRnZPr}_3EFks{HMgP3{>7j(@SvQntlufT|{?{ zOh-X^s?YXw&KM{_MntLgg+VRE2bsRsZ8-hT!Y%}@_T8 z`UFU1T_)Yole{)hTt_KJ>==9oSE=qq5GAj61O;6LQ+)JeG!`lv+!}8Cbre*~ zn|2Dp1F#_!w;uq*a&S^1lz7h9guHtx^+C)Jo%k7;3|NpEYb|A6IQ=Mg5Rq8ZTFMb# zX+$pKCQ5bJbqh-jm33N29?WB_RlM#DaK0kl{ z)RnPERSyjARD3zoe42nwP&x{?It1qDkDS|qQU8)c0E35c;P3EIC=+N_CdbtdhadoO zzy$&My&cuy_z1+jQSCj~)!+ITyGGJGqw_R1({$b`=CT~j2#y(>Y~xqz$)MSFnadDk$7x>8)dsyUCvXCS zDBReo+d4*VN+_tqSu{}__oHYfCf4VY1RSj$d>CQPfhr*n$ptD`I)UiEX=$SD5}nA6 z!KDYIML(=6Nq|uU9dG1@0l%q1ElA(Xm0Skx`yQ{V8@ual;a^+d-B|0jV#%Y+B3~Vz zI5_;_e`(^aCu`TGZuojFB4%yRY?pIfNe1J`^9)Rz#(QFxg2I;Dxm=?!^Z5GS3prW$ z@Y}#Z?Kita55YZL|5u`i^G*SEV-VU+TBKt1Wu&@F5I`O_xF7n8+0ec1zNf6LVhDDv ziln9$i<%Dw1qC8&_^vFz`R;gUA^0PYF}^gllNf1Wt`e8}{(Y0)B^QqIQs#;~(_I8> zmN4{}RCRUEdNy>h;Kp2| zp^76|ylnbH5!?&q;{lE)y}M)+G_m9!vx9n}?_J&=>S-Y;k%9V0{~vfHa<_IA;)1ICCJPSv@l-EkNTa+A^eLr*0j&S85p!C z9mKKdeWo2P%hT>I(5f zP~Ck}V?z2Lt7-%;_^?7**iyGB1HMtU<{HS!eNqK!fA%>o=EaKZFRKRJPca=3ve4Bx zf1`=ffJjBdlKE}h50V*^;>oR*z$UghcBv-yxZp*R$KOqafTj+)avuCwp5i!DCq)Ns zdjwqMBg-M$fhd481QTZ%dJn@!HN;g!qXQ$8qf0XinfXNYq+}Y0Yr%C#oB1B2&lSqG5v@fy|e+{$@0Tl?w^uPQjZISr# zdtIIRAehF~?!()ord~bsPf)_nLrNMr~X2oasz+k=xK~s_!@5NXA_kN8y&6%UR&P z-EVW+w6k5R#+*m!(Wj?3ZcN04MIG7nseYkp>4STeclKY=oi!e5Qavp5U)rO*+Rf_z zlKCj&>L)aRj)LP;6mc4sWH&zg*@Qv!#iBv*?2eD1vLj__)7AQWR-ol5DfYkyd=J4E z!o)SF3MNzCND>9w|FmvVtFlB4w+UbpCyi9ko>~6*`=+~^18-YWv)!G_SfA1jC)>vC zgN4hhO3qZ&O`GCU*`9#9Sb|{(?pQ<>BEP=${I_l5?X((z6$~TM6kiAjnLY-Dkt*sxj-|!_o zkhQmS88;rJJ1xj$5Wx<*v}Jm2;YQnR85orC^a(4DeO#q4q2?lH19eHMNELO)aSAD0 zH311R@O$U)@Bf*`kfR3E1z#18`lX<@!{yx=~#;L1}Z$HYj&P-u@&3+c5;!$P09v~dFC9La7Q6|=KGkCPj>Ft*I7sl zP2+Z4gG8E0vhbwGtX4-~MNTdOO#4Uj=@!|~cPp7hh5_LdOcRvit*>ae`6otg%Q5fv>V8a`o zqp-(krk_v10wL`y?$@WQ!AN4$!mKV$dGi^W8cEmi;s7*Bzzx@q6x&U1IalA%5xYNQ zv9gn}Cd8j0=mQ}Tq`YAB=F*ye&q)-NA+qU*7_uHYhD&9T=#^vs&?o4%__jSb##`Mh zA|c^wqCN@d=-SDbOjIY~#iuSx3JYH`;T?r^*S&ZehFeG^O{?nb$uM9C0dfe|YThvD zG3ncb8G^_=jqO&KMW53SnQ?L!PIAdRtK!H3J zrqhrh^yd6Ab}`7mmF{D`R#i@DFX>6`-qL|MU|}Q2Y0AB~fffc`j7J!G6-GfxjWF@+!;GRl?ynP8K%P3nsDB-$j5YvSLg$NHwm zSoXp&ENXwv`4=8f`C2W0Uk&xT${H;ZKnB6Ren31XC{K0KD`WY99hX^8VXI~BpXg_J5nO8iupE!kQo|-Vb%2#lwN;` z=SEAb1_MJPY7f4}QGEgQ&p&?rczb}KN4$o6zHXZ)0P78&xiaFD-A}_iUxY~%VVCJ! z6Upft00UUGq&}i!nkDVD)}&0c{;bm7HrHeJkVA3ChcB+#V@%x zvlC+>HV{%S+ug8tax_gB1p&Q&z5MCDaJUk8HA_#3JMm!#yJoGdtQHYuF3ouql(s5v zFH5>?ccTv4n+AsetGz1?t9gCbrmYZ}_Rf%^7GaY}RGPEZGSyCzMkGTi)oMj)FeElo zR%j#<8ny;BQxu|MMblEND5=!aK!xUWKP%_|-(Sv$b3UB&<#b&xF0ESY_xru?^FGi0 z+|PYCkTK{&Cgnt{_CRu*%UN1-kF`EF9=i`}qqv!;VooNH1o4>{2Y>LTh9*8wSsKy* z&~zCMcSjen$eW0Hg}L6qF7t(Z!{!%Wpwy*=+0^u2chxXW$o_%4u(;70UIKkpCEj;b zSWTaZD@wz7n3qtEd1bi+W2)pj3x=vPwul)j^S-vjKJhfaMsqxbYNF$82~P`JdhMA4 z`C2!=0~Jr;2svi9EGmY@EeLBBG;;(#h+mubE}V}s(h#&7Mhp_lZF9QA-w$j6T`7K5 zzBS?#`XQ8)xqH66q8Qqb4rMs2$9>MhhJmCZ0A%>(Ui(fqq3=M`J}Qq-<-o98l}SSI zMRGQC{i3j{DTGQj%T8uUH3!ve4iB)>dPEv(`eYCQnmT|VEd6A(zA89z-ri+ zLrRobbiZC69AA3KagxnN*qbWvY!De ziP0DgiFkT>93|{}s_2~qqb$UvH7xMaDbio}8FCM3yzbGuBrOryl4a?zzZwCV`6?D< zO@VTzmH6DK7o+S1a$wbAxBWOKN(+Gm_0Xt?bFs5YCCUMzY9-ts4I5ferxH&N$ChwP zPsN}K{eYrrp(n1JTNCZJ0@L;+#|$(esvr}ZHHJJ3mcK^t#~Eq-$~7-&@-I{|-=-H~ zu20SS6Ur|kyB45T8A)l|T7tHFIW#mDn|=J?_OJMq8#$mOSlj$f%Md`3jmG-d6(oDP zNj@XGS^!3oG1~iB0}XC*=ct1h^12&guRBAl?~>-pJ~?yj-NAVTCm-w4XLvWd68KfK#)Q zW;TJhk@5q{uOPa)VsHPxeEGfY6U1;H-~h10l(3Mkh*@wHWmHBX<0!=-#YrgxRNeG= zNesCfSZD9+#XSBC3R~cE^YB!U5Dk^aksy0W>bN-YNPM6Yh_1S$Ie(TkG7b{Vw)PoJ zp~4Jzfu_tLo<}Y_G@ubFtw3fnC>pmQUoE2#OdKwhHkg2CU-|eb%l+EeODJ=JjRLK= z#OPv=s>(26`En31XkH%Eo!gPDC8U3tNI`{CBEAD4C#$3NzhGv`ias7t@{dl+u1MtV zaB9Hx^|1epg8r=cT74Gt2VY7*eQV%QohKcYx&8F9NRkGo*!Jw%vpFR}D|qQ)7x$x^ zLw+zqJ#NfwL%e7QsyrM6ad?1MD7z9;F|nCUlfPKT<;rWn_3p*xG~Hh@!4;44M)reQ z+1{$7NBP!b6_YP9)zDUV6=VD{;UNI|tpae1-MifOR+NQhxS14^fw+%nzklazpQD^} z4`l$&YJjPD%iJ+zVNHEH$w}jg-Qv?*XpMW3X2xT|6POf63Atng?iE?7^PcEvO_u@9 zc10=yNeQs+NN<=za}u1T%`tF0bHDcNXtpwnHOVRD+=e--+<9F0yXcLKBrFiHe1lcO zU?L5vV|J!EK}c=3-=B%uGsUjL8K`SP0(vq7#k0rIJ@NEZYhE+%N3Ha%tl9+Z7^vWG zkm`6cB?Yg9s8K6|<}H*WX7Q>-zL8mv4MnR0HXqbd`PB3M(;HoyS_bIkB^4^VUM5e0 zW^|^w}+oaA@Rm7=F^lR+(@_-;q2U zD6Atk3MwQYY7YIl<=!^w#Y|beo0iuT(1f@`5f`xGTUfnNkky8Kq>rd<++4riA>uAB z&ESofL!v?_b(lP=U`Ru>UYnI3QJe=d(7HSQaW@CA zdeVC~Cju{@YD8yJu(-6w=hV$O3Nvc(V}@s_UmLMjsCGF&8t3~y`D1gQ9>a0K$^g8* zAb_q#*;?paq@|BaGNkbO^hd<+FKUF8MPR=0=U4pvdBh6v0hcksuXQqC1XDN*N6cJZ zdq%zc2=S2@eMEuOzbVWHyBJ2@m&;uQX` zIvjv9$VCt2nGq9I--xtvhcU2}QG>aA)$_uul^(g;mm@ZFI;Z~qc7TSeGPJ$0K>i}a zCI{FA_Le*oDH3)2GT!Y;_Fb-l@6Vque1ENLPK%B%Y{8gsQ_hq#y3@OaSDK0Jl@^FNiILe;>!@%;lH{dec& zpC1ONdsrd&SptlCg{$iLu<&u+*qY8w4yT7eU76l6`!SGq9(DZi&u?Owt-u1f@{Q=Y zZL5X1?O$&sBQMYO3^YSvI-l^&y38!iy~&vFVj_g_~aL=l5{2#Jo4LdSJQ?y@YKcGW4S|b4R`{G>9q~26s9R zocuFO=Fq3P|MSz(i~xaa`Xnm{t*2)Sdb9=BCxkfB2Rq*P9ApZ@jz|FaynLoU`s!jw z^zkjNXCuo~RpQrQZ7&M>EQZH6zrlBeU(^0=47>i{*0EEggm;==q2(+5`Tu{h@DBXX z@5a(aHMAJ!ca!ZrR>A3Y7v`u)P9ei9WPX4CdEt-F9ms;j@&ZxC!i+0DPo?ASp*7$f zIXUVA@r`}-$bti$<<}W`dL}*qmZ1&t;X|$@^J6lt^Io=<0ac9*=aKjKrzJ)NT-6cmI; zto`H1&z)c-imz#HkY0U_e3^1XbhZ4}Nj}++Wby=}&myL9R0%o21nW(71M&4ge+UJ8X8BEPpHIJJoFkIa z`&EY!360Q}&YL%-?g2TAArt0AQ)&)`V)sSS9wsn;B0hB7r zL0C({coiB*I!M6#p0DtZLd8ln9kwe&S$ei#6p3yl=^?>-n#BZyfMmkNr>`v1GwxRE zV34=JleKiqsm?_aynL~Y#ZOXZ5A&i{IS#L3kUJm^Qc6-fEB){b2gxrlRyHGBeU<~3VseIhR*X(Fz-B(bG*NagOPKRCC!t#0hBK2a)3C!JmpBs$6>2FG+M(_w zFaoe^%pLg1sa^v2L)IxA2y1A-Ye`-V6(J$deaP304GpLI$>#Om_=%a5BP{@53uM_E zht2sz%pAXMxGCM*KV&3-K7=*Gqfg71rcQv4z-AP7f%mxzV$_Y{tcGZX9e)udUa_SD z87L&yJi9c;q$1U)G_gM3&{8_-d@k~Wxhc%`+Qu!XL{pr)4f@z0M=W*M#{fHL?FGTj z>ci)Qvq*mIc2SdODQ|MEyEgt89{-8^O=}<3$BHA zRn!#GRN-Q(i7gb!dQFY`?ILd9MbD9awq7)=)^X7aMl7%p0OH$$W~AKb@(>i&Dx3T2 zi&CuSmycXnbizL`Y^5-VIXemvsoDL#e?=Fp-+*j^WUr8^8sPpDLLKk<DxMkn!E1w4PW63PgUB}XhUEJz~l$|^$78B0K@ zwNWRHnWdU`K+Pu(!V zc!zK`CF-!fqQzy(bKl@0UJ4MVjZ>eh6`$e(iAI}&X@tw;ucH1NBNqC3Q4^+g-k?}8 z*~j-opT6|^nA5kglw?P2vghl}_S?-}SB19rQUMMK(C?Pfq^@>~!={ZJAEL@*8bxY& z=}0ayG!b4x_QZ;R-VEWhexlM!I+q|BAaWwlbj-#9M|j;J%pXdzEh(%cyxH;Jqa}yS zCHw+?o&A;0kjwyo@Q*d&TgWLA@YJ9C^T#O*7yE+K-~aJ(!=EdN$<}Do5{+rc|?f zC~C`4G;qb#DdA!STD;HEG|1hcg+)R-G;<0GymZw2b7M+JWVgLD=3I1OV?gdH()-ODF$;RBH$j8w28R=A1XC%^;D*3!`KeBq z)Bt2N96ZqxT8)5>PL~S6VXoL<8 z0}5wbF{Sn{?@w4NEsvqwG2+U0p;(JP}`r0=$A ze4;y}I`v&Ad4oFuz1#YgIxUp2Jt;0C5&uDBN|!)+K-p7Ga@Hcf{+*ozkYnR`(o2@ z`5*nSMVyM2c#F~a1j0;QJ($bRH1rzo7#`GQxr60L6k2pK$4;JcRutifx)nIe1SvIu zc5v1~i#SO&c2dtII1S*dISKA(v;FG7><3L^Ulr1vW6RR^zTzlIJi-Dvd9Hp3Z4l%3f z!N6_R=9l1?8PNzl#zK)?Q1b!f6i?pK`||3tVha_X+=Gity+g4B&$tGq$+#*NQ?TTAgLz?ULRz|2*?K@wl|&dK8{n!^ z3E@B`kOGKP3C$KF%5+1O@BkvqiFpG)q+~{?AOm!3wpC$ePjAe5xr+Y#;1o{f*jLc$dY)?@*RyscAkROd~lQQJEazuk)=Hj0P?M7ignCTwD&f%V{oHwMgUOO83j z3P$X1iHlD#LgI*4B58;ajRw7)-PKpkNdLgSjqIr?RHro(8lF}d6*s9TP*{%bc?`te zkD<#+#*LU$xPSlt@J13gb{GUvdVd16>Q_Qs)n;h;b0nhN98~rSVtH>dl|D^cx+B#B zAo8W);2k!Hh(LWQB;=E`59b2NNppcJ3`H)MfYyQnV>Wj1z2rldkpW@Va<6-`FTVmz zL=F3td5qcED^exqN!2dk>?&2tz5*r0HN~OLE$d7F`{8#2H_#VekUmBG@aJ#V&??Q( zB(;2uBY3D^2P$g=a+8$c6BYoN5O&=HUP<6y^mZPZ&3k%4`YzP=xPg03?`_X>Oyilp zs{lG+Z6n4h`tG|eiQkB!su$!OUg7-a4~bs3+EYjSJkifyoq#<MFG0MSND~2+ zz4-T_mf1v|f&3mkn#`s!`@IjU)UIY=j=Kl@_?ShGZQ*ZN!_REqPuS3(PE8qx$-CBT z*RIXGtZF~~vs)-g7U+8>*d>a%_=|2Z7wOYT2lcS15i}L|dp6j(P1ipFqM0jYx&-)mq<<={s%OYuPNB0gu;8sJRB70U_Tg1}F^4GKg%CQz4Yc9%?O<(q* zsfSjXV~I6ZCJr?^gL{WbP6P205bCA8`ap0wAQ(@=QY5BX{SCvfJcv^69~vgU2RNZs zzoDLsbrJLfVBgtbEJO1RgiKRU5a&AHh^`3G8~fG5V0{kBSmBqY@j;oY^nAM;@?+$J zj5fqebUYD^Q8buTo*>L?3!_!OS*iRlRpV3Td7L8qeN@E|vmjnX#a(?U5Z(R1)7i#YqN1`>6CiW^J=nn~ zQEw9(l_4DqK=^~wCuxbJ@st={uws@*|7S&OgBc8AA0`@g^TQYJotDy{cB1&hlNCR0 z|49Mo#qlSnUC#MI>7xM&s-wUiO~tsHcl}WH&sv@aUgU!!`$T9U6+ukfL4Osn7=K*s z(F?VXX>-Sp7en9(w(>aWpWn(waJ0KnWr7IeWbh{GNQV`x7IfT?ZpRLvthkDv-0Fr9 zu(IAeE8kTP+`9~tRSvVy8Uvtc1n2CwUuo2mmbuN6<4dnTuZiBv9KuM zuR>7!1g753_RmY^jB$u0nep%*^)Jt6q$J3JxZQAhdVY0I2cigCS(p^O&m2TOrTF{PQP7NbLss)aAXtu~QOG)jMc#s}zNgjf3O=U7T@~kTU%vJ9o;?P36^9+(4N{P$cf$9ewwb8>7lPvC5=x^Tq zck&}V!r&4Ozg?B5`~LbFP>DJ3e9iu589U+S14xw#O&a_8`r`9c_j)JMWELf|IJ&#! z0Gfbf)l}97kBlM*?4j20D^>394_q2#J`{7lYnmgzOQRT!gE-MiB*x0U@mB{#F(6}I znGQ+St6-^R0Upj703HLX<9Ti10o0`&0*gNMKn>JTdG6*SVzcT}5>T3Mj8hYTwBZ0F;nGI5+@9Vo3a4qnkSmNkFJ1tm(FcM8azje@4wwyRxI@P5Il# zWXoq*g?onnUZXT^F23$(8fuQzQgz$0fz=aO*Q6CN-D6KGJ)9cN?w3m1Mkr1Jm7!=1 z(?nYmiPu^^hm6W;1q|PHY-bJbFGMEBlpYzy*&}Mu(joO^l2JkCO<57_6fGRGcBS05 zuMM_z3w{4ZTE*p=g&Q}x)n9R^921$hRH4d@sO!o>_kkwZZwB~aB}g`1X4PDB&!OMf zJf`|K_|hkSY1%N~Vcuu^u&$_s=my5Ev7^aM%AFa+MCqEO4aku51TdfW<+CRLXzh_N!{xwc!x z64$hzIi5M5lAaPBl>=MZ7+Q?_kJt0~2uj6s*ngrPZB8}%bzUi@@c_VEw zBkS=T6NuLQj@*!#4#@h+>6Pp-IMFo9*Z;j^ZQ=O%<(5Iea_<)O=ds82kV@*Z4%L9T zoHDl`q%q#~6^lkX(H4wC1Xs^EUgS4tG|it$X6!6``9bU2`L^j3pJv~|wo->KH76RR zsgSe|G={g9C_w)K57dNTS??6W61)J777bS7`G9V>Fsl|!#NF-3003xfO0-*mGG%hd zFQ%;|W7WpTaR1^gozb6A7MR|JLo%$e{Z5(aKFb)Nkvc~-9ML7bFyYpl);&HcOsUEv z2ydj(-aCveBed|T-6o!SyEXP^j$TftV@KNwTaGcF4@7!4apZFbTq5=S`wDwbcymRZ z-0$O%{U0^112UN^uH4@)fM{nTqPu;ZG_d}Q)+(lZE=U!%z*EeTx_6l@* zW2DqQ(rdX&(^^}edU0r`6xPNo>y*8mHPrWR;gA);;OX?@zA{wDI`H39V>}e zX(8-2Dz_jsDKHwaz4}0B9uXOh!OPEfOyk1~AO=NA>tUaMbFEjR`@cF-q!;f~qlF6w zi2*V>@{1?MSjA9N_$NbLj>y@Hg^}v5ilz>|NK--!k-q1&gZ_bnvv9L1@P5)MB@bj! zNnLSZE#+%sKDx;T(nVyO4PJT}UkWpuG!}HnAW}Z|>ymE3>0dhb_O?I>a%C`Z(*&vs zDD5up1QxU5)!OJftKQF9%eAl|ThU-@N;5FAY1pNG%@E zcpn(W8fn@%68wQR7{_bk9z%t$&my0!8XPn+ZxNXm2?Ucr%hSOpPvHVi^4XjV;EknQ z0yew=+EDfjQz$=O0Fs{BCW~ro2Xre}GG>#}FwaHlRZ-F3U)0-;d2Ju!ccrH69yc83 z1|Io?L-;sFjhJjbR_mDSJ3*HcFmg9f&hWaU(jW`?+7$Y zN%K^UC1uJ7*e* z-UOOTMt%>X0A@$*ue6MA9h;}{h%2y9)xgtcRg96OzjIPz*i~c?+~9Z;sTkBtc^6fZjpk6Sfldw*kq-k$fZQ|LTjIxe|zFWXM6K z&17T=ghF9tk49mr(50FNQ@{Cbrz$i!55X2^8X@Kt^;!yYGv3NaP5*3PUG>)&uvY(m z;eg!dA*xhrQ((E1*$omQEI}F>rN#hJh#s)!FJbQFWwWN1`BMP$^}`6J>cF~MXw0$TJY4r#gp>MQrBsaNdQ%&!H%lVT{~ z$5C2S6@*Rj`P>T}$ diff --git a/articles/running-monlix.html b/articles/running-monlix.html index 8e4cd43..bdca245 100644 --- a/articles/running-monlix.html +++ b/articles/running-monlix.html @@ -201,7 +201,7 @@

    Step 1: Run a nlmixr2 in M #> ── Time (sec fit$time): ── #> #> setup table compress other -#> elapsed 0.002866 0.124 0.006 4.419134 +#> elapsed 0.002762 0.134 0.006 4.450238 #> #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #> diff --git a/articles/running-nonmem.html b/articles/running-nonmem.html index 1f2ac91..8ba9316 100644 --- a/articles/running-nonmem.html +++ b/articles/running-nonmem.html @@ -268,7 +268,7 @@

    Optional Step 2: Recover a #> ── Time (sec $time): ── #> #> setup table compress NONMEM -#> elapsed 0.032119 0.093 0.01 320.27 +#> elapsed 0.033752 0.094 0.01 320.27 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> @@ -459,7 +459,7 @@

    Optiona #> ── Time (sec f2$time): ── #> #> setup table compress NONMEM -#> elapsed 0.003334 0.102 0.009 505.59 +#> elapsed 0.003488 0.103 0.01 505.59 #> #> ── Population Parameters (f2$parFixed or f2$parFixedDf): ── #> diff --git a/articles/running-pknca.html b/articles/running-pknca.html index 478af19..f09feb9 100644 --- a/articles/running-pknca.html +++ b/articles/running-pknca.html @@ -276,8 +276,8 @@

    Initial example#> #> ── Time (sec fit$time): ── #> -#> setup optimize covariance table compress other -#> elapsed 0.097378 0.490291 0.490293 0.115 0.01 7.438038 +#> setup optimize covariance table compress other +#> elapsed 0.093825 0.490602 0.490603 0.112 0.009 7.52497 #> #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #> diff --git a/authors.html b/authors.html index ffc7bfc..154e32b 100644 --- a/authors.html +++ b/authors.html @@ -49,6 +49,10 @@

    Authors

    Bill Denney. Author.

    • +
  • +

    Theodoros Papathanasiou. Contributor. +

    +

  • Nook Fulloption. Contributor.
    goldfish art

    diff --git a/pkgdown.yml b/pkgdown.yml index 03e76ed..0cf75cd 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -7,4 +7,4 @@ articles: articles/running-monlix: running-monlix.html articles/running-nonmem: running-nonmem.html articles/running-pknca: running-pknca.html -last_built: 2024-10-31T17:03Z +last_built: 2024-10-31T17:50Z diff --git a/reference/as.nlmixr2.html b/reference/as.nlmixr2.html index 0afc668..b0b2954 100644 --- a/reference/as.nlmixr2.html +++ b/reference/as.nlmixr2.html @@ -255,7 +255,7 @@

    Examples#> ── Time (sec $time): ── #> #> setup table compress NONMEM as.nlmixr2 -#> elapsed 0.038503 0.131 0.018 100.95 2.899 +#> elapsed 0.038921 0.138 0.019 100.95 2.886 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> diff --git a/search.json b/search.json index 54f7346..4120358 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"/articles/PopED.html","id":"introduction-using-babelmixr2-with-poped","dir":"Articles","previous_headings":"","what":"Introduction – using babelmixr2 with PopED","title":"PopED","text":"babelmixr2 now introduces new method takes rxode2/nlmixr2 models converts PopED database help optimal design. PopED vignette comparing ODE solvers (speeds), section : take model described adapt two different rxode2 model functions, solved ode cases (done nlmixr() call creates PopED database) compare examples pharmacometric solvers PopED vignette (mrgsolve PKPDsim)","code":""},{"path":"/articles/PopED.html","id":"babelmixr2-ode-solution","dir":"Articles","previous_headings":"","what":"babelmixr2 ODE solution","title":"PopED","text":"","code":"library(babelmixr2) library(PopED) e <- et(amt=1, ii=24, until=250) %>% et(list(c(0, 10), c(0, 10), c(0, 10), c(240, 248), c(240, 248))) %>% dplyr::mutate(time =c(0, 1,2,8,240,245)) # model f <- function() { ini({ tKA <- 0.25 tCL <- 3.75 tV <- 72.8 Favail <- fix(0.9) eta.ka ~ 0.09 eta.cl ~ 0.25 ^ 2 eta.v ~ 0.09 prop.sd <- sqrt(0.04) add.sd <- sqrt(0.0025) }) model({ ka <- tKA * exp(eta.ka) v <- tV * exp(eta.v) cl <- tCL * exp(eta.cl) d/dt(depot) <- -ka * depot d/dt(central) <- ka * depot - cl / v * central cp <- central / v f(depot) <- DOSE * Favail cp ~ add(add.sd) + prop(prop.sd) }) } poped_db_ode_babelmixr2 <- nlmixr(f, e, popedControl(a=list(c(DOSE=20), c(DOSE=40)), maxa=c(DOSE=200), mina=c(DOSE=0))) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ infer estimation `poped` from control #> ℹ groupsize should be specified; but for now assuming 20 #> ℹ assuming group size m=2 #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ e <- et(amt=1, ii=24, until=250) %>% et(list(c(0, 10), c(0, 10), c(0, 10), c(240, 248), c(240, 248))) %>% dplyr::mutate(time =c(0, 1,2,8,240,245)) # model f <- function() { ini({ tKA <- 0.25 tCL <- 3.75 tV <- 72.8 eta.ka ~ 0.09 eta.cl ~ 0.25 ^ 2 eta.v ~ 0.09 prop.sd <- sqrt(0.04) add.sd <- sqrt(0.0025) }) model({ ka <- tKA * exp(eta.ka) v <- tV * exp(eta.v) cl <- tCL * exp(eta.cl) d/dt(depot) <- -ka * depot d/dt(central) <- ka * depot - cl / v * central cp <- central / v f(depot) <- DOSE cp ~ add(add.sd) + prop(prop.sd) }) } poped_db_ode_babelmixr2 <- nlmixr(f, e, popedControl(a=list(c(DOSE=20), c(DOSE=40)), maxa=c(DOSE=200), mina=c(DOSE=0))) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ infer estimation `poped` from control #> ℹ groupsize should be specified; but for now assuming 20 #> ℹ assuming group size m=2 #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’"},{"path":"/articles/PopED.html","id":"linear-compartment-solution","dir":"Articles","previous_headings":"","what":"Linear compartment solution","title":"PopED","text":"","code":"f2 <- function() { ini({ tV <- 72.8 tKA <- 0.25 tCL <- 3.75 Favail <- fix(0.9) eta.ka ~ 0.09 eta.cl ~ 0.25 ^ 2 eta.v ~ 0.09 prop.sd <- sqrt(0.04) add.sd <- fix(sqrt(5e-6)) }) model({ ka <- tKA * exp(eta.ka) v <- tV * exp(eta.v) cl <- tCL * exp(eta.cl) cp <- linCmt() f(depot) <- DOSE cp ~ add(add.sd) + prop(prop.sd) }) } poped_db_analytic_babelmixr2 <- nlmixr(f, e, popedControl(a=list(c(DOSE=20), c(DOSE=40)), maxa=c(DOSE=200), mina=c(DOSE=0))) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ infer estimation `poped` from control #> ℹ groupsize should be specified; but for now assuming 20 #> ℹ assuming group size m=2"},{"path":"/articles/PopED.html","id":"comparing-method-to-the-speed-of-other-methods","dir":"Articles","previous_headings":"","what":"Comparing method to the speed of other methods","title":"PopED","text":"Note babelmixr2 ode solver fastest ode solver comparison. Among things, model loaded memory need setup time. (benchmarks, mrgsolve, PKPDsim implementations PopED’s website included). Also , speed tools reasonable. opinion, benefit babelmixr2 interface PopED simplicity using nlmixr2 / rxode2 functional models fits directly PopED without relying conversions. interface bit different traditional PopED interface, requires design data-set well popedControl() setup PopED database run PopED tasks. traditionally nlmixr2 takes dataset, “estimation” method controls change estimation method options. babelmixr2 adopts paradigm model, data, control applied PopED. allow easy translation systems. easier translation, hopefully optimal design clinical trials easier achieve.","code":"library(ggplot2) library(microbenchmark) compare <- microbenchmark( evaluate_design(poped_db_analytic), evaluate_design(poped_db_analytic_babelmixr2), evaluate_design(poped_db_ode_babelmixr2), evaluate_design(poped_db_ode_mrg), evaluate_design(poped_db_ode_pkpdsim), times = 100L) autoplot(compare) + theme_bw()"},{"path":"/articles/new-estimation.html","id":"create-a-nlmixr2est-method","dir":"Articles","previous_headings":"","what":"Create a nlmixr2Est() method","title":"Creating a New Estimation Method","text":"method input environment nlmixr2est UI object (see ?nlmixr2Est). output fit object.","code":""},{"path":"/articles/new-estimation.html","id":"create-a-control-method","dir":"Articles","previous_headings":"","what":"Create a control method","title":"Creating a New Estimation Method","text":"control method gives access controls required estimation.","code":""},{"path":"/articles/running-monlix.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-monolix-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run Monolix from a nlmixr2 model","title":"Running Monolix","text":"use Monolix nlmixr2, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run Monolix. setup lixoftConnectors package Monolix, setup needed. Instead run Monolix command line grid processing (example) can figure command run Monolix (often useful use full command path set options, ie options(\"babelmixr2.monolix\"=\"monolix\") use monolixControl(runCommand=\"monolix\"). needed, prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-monlix.html","id":"step-1-run-a-nlmixr2-in-monolix","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in Monolix","title":"Running Monolix","text":"Lets take classic warfarin example. model use nlmixr2 vignettes : monolix run, can run nlmixr2 model using Monolix new estimation method: fit issues informational tidbit - monolix parameter history needs exported charts, please export charts automatically generated well lixoftConnectors package generated recent version Monolix. don’t information important parameter history plots imported see plots. Just like NONMEM translation, monolixControl() modelName helps control output directory Monolix (specified babelmixr2 tries guess based model name based input). Printing nlmixr2 fit see: particular interest comparison Monolix predictions nlmixr predictions. case, believe also imply models predicting thing. Note model predictions close NONMEM Monolix use lsoda ODE solver. Hence small deviation expected, still gives validated Monolix model.","code":"pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } fit <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"monolix\", monolixControl(modelName=\"pk.turnover.emax3\")) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ assuming monolix is running because 'pk.turnover.emax3-monolix.txt' is present #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> ℹ monolix parameter history needs exported charts, please export charts fit #> ── nlmixr² monolix ver 2021R1 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> monolix 1522.704 2448.398 2527.819 -1205.199 2203.836 #> Condition#(Cor) #> monolix 2.697324 #> #> ── Time (sec fit$time): ── #> #> setup table compress other #> elapsed 0.002866 0.124 0.006 4.419134 #> #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV% or SD) #> tktr 0.218 0.179 82 1.24 (0.876, 1.77) 84.0 #> tka 0.00533 0.117 2.19e+03 1.01 (0.8, 1.26) 48.6 #> tcl -2.01 0.0518 2.58 0.135 (0.122, 0.149) 28.5 #> tv 2.04 0.0438 2.14 7.73 (7.09, 8.42) 22.6 #> prop.err 0.0986 0.0986 #> pkadd.err 0.533 0.533 #> temax 4.46 0.527 11.8 0.989 (0.969, 0.996) 0.380 #> tec50 0.0786 0.0889 113 1.08 (0.909, 1.29) 47.8 #> tkout -2.94 0.0261 0.888 0.053 (0.0503, 0.0558) 7.87 #> te0 4.57 0.0114 0.249 96.7 (94.5, 98.9) 5.08 #> pdadd.err 3.79 3.79 #> Shrink(SD)% #> tktr 47.9% #> tka 48.9% #> tcl 1.25% #> tv 6.09% #> prop.err #> pkadd.err #> temax 91.9% #> tec50 6.29% #> tkout 36.6% #> te0 19.9% #> pdadd.err #> #> Covariance Type (fit$covMethod): MonolixLin #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink #> Censoring (fit$censInformation): No censoring #> Minimization message (fit$message): #> IPRED relative difference compared to Monolix IPRED: 0.09%; 95% percentile: (0.01%,0.49%); rtol=0.000941 #> PRED relative difference compared to Monolix PRED: 0.04%; 95% percentile: (0%,0.2%); rtol=0.000428 #> IPRED absolute difference compared to Monolix IPRED: atol=0.00911; 95% percentile: (0.000493, 0.0928) #> PRED absolute difference compared to Monolix PRED: atol=0.000428; 95% percentile: (3.14e-07, 0.203) #> monolix model: 'pk.turnover.emax3-monolix.mlxtran' #> #> ── Fit Data (object fit is a modified tibble): ── #> # A tibble: 483 × 35 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.ktr eta.ka eta.cl #> #> 1 1 0.5 cp 0 1.40 -1.40 0.500 -0.500 -0.934 -0.638 -0.447 0.689 #> 2 1 1 cp 1.9 3.94 -2.04 1.62 0.284 0.511 -0.638 -0.447 0.689 #> 3 1 2 cp 3.3 8.30 -5.00 4.29 -0.987 -1.45 -0.638 -0.447 0.689 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v , eta.emax , eta.ec50 , #> # eta.kout , eta.e0 , cp , depot , gut , #> # center , effect , ktr , ka , cl , v , #> # emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum "},{"path":"/articles/running-monlix.html","id":"optional-step-2-add-conditional-weighted-residualsfocei-objf-to-monolix","dir":"Articles","previous_headings":"","what":"Optional Step 2: Add conditional weighted residuals/focei objf to Monolix","title":"Running Monolix","text":"case NONMEM, gives things available Monolix, like adding conditional weighted residuals: add nlmixr’s CWRES well adding nlmixr2 FOCEi objective function now objective function compared based assumptions, compare performance Monolix NONMEM based objective function. fair, objective function values must always used caution. model performs predicts data far valuable.","code":"fit <- addCwres(fit) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(R²)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done"},{"path":"/articles/running-monlix.html","id":"optional-step-3-use-nlmixr2-for-vpc-reporting-etc-","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 for vpc, reporting, etc.","title":"Running Monolix","text":"Also since nlmixr2 object easy perform VPC :","code":"v1s <- vpcPlot(fit, show=list(obs_dv=TRUE), scales=\"free_y\") + ylab(\"Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ v2s <- vpcPlot(fit, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") + ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") v1s v2s"},{"path":"/articles/running-monlix.html","id":"notes-about-monolix-data-translation","dir":"Articles","previous_headings":"","what":"Notes about Monolix data translation","title":"Running Monolix","text":"input dataset expected compatible rxode2 nlmixr2. dataset converted Monolix format: combination CMT Dose type creates unique ADM variable. ADM definition saved monolix model file babelmixr2 creates macro describing compartment, ie compartment(cmt=#, amount=stateName) babelmixr2 also creates macro type dosing: Bolus/infusion uses depot() adds modeled lag time (Tlag) bioavailability (p) specified Modeled rate uses depot() Tk0=amtDose/rate. babelmixr2 also adds modeled lag time (Tlag) bioavailability (p) specified Modeled duration uses depot() Tk0=dur, also add adds modeled lag time (Tlag) bioavailability (p) specified Turning compartment uses empty macro","code":""},{"path":"/articles/running-nonmem.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-nonmem-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run NONMEM from a nlmixr2 model","title":"Running NONMEM with nlmixr2","text":"use NONMEM nlmixr, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run NONMEM. many cases easy; simply figure command run NONMEM (often useful use full command path). can set options(\"babelmixr2.nonmem\"=\"nmfe743\") use nonmemControl(runCommand=\"nmfe743\"). prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-nonmem.html","id":"step-1-run-a-nlmixr2-in-nonmem","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in NONMEM","title":"Running NONMEM with nlmixr2","text":"Lets take classic warfarin example start comparison. model use nlmixr2 vignettes : Now can run nlmixr2 model using NONMEM simply can run directly: way run ordinary nlmixr2 model, simply new estimation method \"nonmem\" new controller (nonmemControl()) setup options estimation. options nonmemControl() modelName helps control output directory NONMEM (specified babelmixr2 tries guess based model name based input). try , see NONMEM fails rounding errors. standard approach changing sigdig, sigl, tol etc, get successful NONMEM model convergence, course supported. babelmixr2 can .","code":"library(babelmixr2) pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } try(nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\", nonmemControl(readRounding=FALSE, modelName=\"pk.turnover.emax3\")), silent=TRUE) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION TERMINATED #> DUE TO ROUNDING ERRORS (ERROR=134) #> NO. OF FUNCTION EVALUATIONS USED: 1088 #> NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #> #> nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl' #> → terminated with rounding errors, can force nlmixr2/rxode2 to read with nonmemControl(readRounding=TRUE) #> Error : nonmem minimization not successful"},{"path":"/articles/running-nonmem.html","id":"optional-step-2-recover-a-failed-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 2: Recover a failed NONMEM run","title":"Running NONMEM with nlmixr2","text":"One approaches ignore rounding errors occurred read nlmixr2 anyway: may see work happening expected need already completed model. reading NONMEM model, babelmixr2 grabs: NONMEM’s objective function value NONMEM’s covariance (available) NONMEM’s optimization history NONMEM’s final parameter estimates (including ETAs) NONMEM’s PRED IPRED values (validation purposes) used solve ODEs came nlmixr2 optimization procedure. means can compare IPRED PRED values nlmixr2/rxode2 know immediately model validates. similar procedure Kyle Baron advocates validating NONMEM model mrgsolve model (see https://mrgsolve.org/blog/posts/2022-05-validate-translation/ https://mrgsolve.org/blog/posts/2023-update-validation.html), advantage method need simply write one model get validated roxde2/nlmixr2 model. case can see validation print fit object: shows preds ipreds match NONMEM nlmixr2 quite well.","code":"# Can still load the model to get information (possibly pipe) and create a new model f <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\", nonmemControl(readRounding=TRUE, modelName=\"pk.turnover.emax3\")) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 3.0.2 using 2 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 5536 print(f) #> ── nlmixr² nonmem ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem focei 1326.91 2252.605 2332.025 -1107.302 NA #> Condition#(Cor) #> nonmem focei NA #> #> ── Time (sec $time): ── #> #> setup table compress NONMEM #> elapsed 0.032119 0.093 0.01 320.27 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> #> Est. Back-transformed BSV(CV% or SD) Shrink(SD)% #> tktr 6.24e-07 1 86.5 59.8% #> tka -3.01e-06 1 86.5 59.8% #> tcl -2 0.135 28.6 1.34% #> tv 2.05 7.78 22.8 6.44% #> prop.err 0.0986 0.0986 #> pkadd.err 0.512 0.512 #> temax 6.42 0.998 0.00707 100.% #> tec50 0.141 1.15 45.0 6.06% #> tkout -2.95 0.0522 9.16 32.4% #> te0 4.57 96.6 5.24 18.1% #> pdadd.err 3.72 3.72 #> #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink #> Information about run found ($runInfo): #> • NONMEM terminated due to rounding errors, but reading into nlmixr2/rxode2 anyway #> Censoring ($censInformation): No censoring #> Minimization message ($message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION TERMINATED #> DUE TO ROUNDING ERRORS (ERROR=134) #> NO. OF FUNCTION EVALUATIONS USED: 1088 #> NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.36e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.08e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.53e-06, 0.000502); atol=7.15e-05 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (3.79e-07,0.00321); atol=6.08e-06 #> there are solving errors during optimization (see '$prderr') #> nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl' #> #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 483 × 35 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.ktr eta.ka eta.cl #> #> 1 1 0.5 cp 0 1.16 -1.16 0.444 -0.444 -0.864 -0.506 -0.506 0.699 #> 2 1 1 cp 1.9 3.37 -1.47 1.45 0.446 0.840 -0.506 -0.506 0.699 #> 3 1 2 cp 3.3 7.51 -4.21 3.96 -0.660 -1.03 -0.506 -0.506 0.699 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v , eta.emax , eta.ec50 , #> # eta.kout , eta.e0 , cp , depot , gut , #> # center , effect , ktr , ka , cl , v , #> # emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum "},{"path":"/articles/running-nonmem.html","id":"optional-step-3-use-nlmixr2-to-help-understand-why-nonmem-failed","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 to help understand why NONMEM failed","title":"Running NONMEM with nlmixr2","text":"Since nlmixr2 fit, can interesting things fit couldn’t NONMEM even another translator. example, wanted add covariance step can getVarCov(): nlmixr2 generous constitutes covariance step. r,s covariance matrix “” successful covariance step focei, system fall back methods necessary. covariance matrix r,s, regarded caution, can still give us clues things working NONMEM. examining fit, can see shrinkage high temax, tktr tka, dropped, making things likely converge NONMEM.","code":"getVarCov(f) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> calculating covariance matrix #> [====|====|====|====|====|====|====|====|====|====] 0:00:06 #> Warning in foceiFitCpp_(.ret): using R matrix to calculate covariance, can #> check sandwich or S matrix with $covRS and $covS #> Warning in foceiFitCpp_(.ret): gradient problems with covariance; see #> $scaleInfo #> → compress origData in nlmixr2 object, save 27560 #> Updated original fit object f #> tktr tka tcl tv temax #> tktr 1.821078e-02 -1.512272e-02 -2.550343e-05 3.216116e-04 0.0015410335 #> tka -1.512272e-02 1.815814e-02 -1.992622e-05 3.175474e-04 0.0010345827 #> tcl -2.550343e-05 -1.992622e-05 2.477225e-04 1.181659e-05 -0.0008009162 #> tv 3.216116e-04 3.175474e-04 1.181659e-05 3.184497e-04 0.0010914727 #> temax 1.541033e-03 1.034583e-03 -8.009162e-04 1.091473e-03 7.5815740647 #> tec50 1.410716e-04 1.273505e-04 -3.578298e-04 1.229707e-04 0.0483191718 #> tkout 1.023011e-04 1.011022e-04 -9.757882e-05 1.188260e-04 -0.0189641465 #> te0 1.310259e-05 1.399880e-05 -9.833068e-06 1.232683e-05 -0.0004365713 #> tec50 tkout te0 #> tktr 0.0001410716 1.023011e-04 1.310259e-05 #> tka 0.0001273505 1.011022e-04 1.399880e-05 #> tcl -0.0003578298 -9.757882e-05 -9.833068e-06 #> tv 0.0001229707 1.188260e-04 1.232683e-05 #> temax 0.0483191718 -1.896415e-02 -4.365713e-04 #> tec50 0.0018345990 1.544065e-04 -1.357629e-04 #> tkout 0.0001544065 6.320302e-04 5.220487e-05 #> te0 -0.0001357629 5.220487e-05 8.843897e-05"},{"path":"/articles/running-nonmem.html","id":"optional-step-4-use-model-piping-to-get-a-successful-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 4: Use model piping to get a successful NONMEM run","title":"Running NONMEM with nlmixr2","text":"use model piping remove parameters, new run start last model’s best estimates (saving bunch model development time). case, specify output directory pk.turnover.emax4 control get following: can see NONMEM run now successful validates rxode2 model : One thing emphasize: unlike translators, know immediately translation model validate. Hence can start process confidence - know immediately something wrong. related converting NONMEM nlmixr2 fit. Since nlmixr2 object easy perform VPC (true NONMEM models):","code":"f2 <- f %>% model(ktr <- exp(tktr)) %>% model(ka <- exp(tka)) %>% model(emax = expit(temax)) %>% nlmixr(data=nlmixr2data::warfarin, est=\"nonmem\", control=nonmemControl(readRounding=FALSE, modelName=\"pk.turnover.emax4\")) #> ! remove between subject variability `eta.ktr` #> ! remove between subject variability `eta.ka` #> ! remove between subject variability `eta.emax` #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|==== #> ====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 8800 f2 #> ── nlmixr² nonmem ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem focei 1418.923 2338.618 2405.498 -1153.309 1.852796e+16 #> Condition#(Cor) #> nonmem focei 18934770 #> #> ── Time (sec f2$time): ── #> #> setup table compress NONMEM #> elapsed 0.003334 0.102 0.009 505.59 #> #> ── Population Parameters (f2$parFixed or f2$parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV%) #> tktr 6.24e-07 9.05e-05 1.45e+04 1 (1, 1) #> tka -3.57e-06 0.000153 4.29e+03 1 (1, 1) #> tcl -1.99 0.0639 3.2 0.136 (0.12, 0.154) 27.6 #> tv 2.05 2.66 130 7.76 (0.042, 1.44e+03) 23.6 #> prop.err 0.161 0.161 #> pkadd.err 0.571 0.571 #> temax 9.98 4.96 49.7 1 (0.565, 1) #> tec50 0.131 1.61 1.23e+03 1.14 (0.0489, 26.6) 43.6 #> tkout -2.96 28.3 954 0.0517 (4.63e-26, 5.77e+22) 8.63 #> te0 4.57 0.411 9 96.7 (43.2, 217) 5.19 #> pdadd.err 3.59 3.59 #> Shrink(SD)% #> tktr #> tka #> tcl 3.19% #> tv 10.7% #> prop.err #> pkadd.err #> temax #> tec50 7.12% #> tkout 33.8% #> te0 17.2% #> pdadd.err #> #> Covariance Type (f2$covMethod): nonmem.r,s #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance (f2$omega) or correlation (f2$omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in f2$shrink #> Censoring (f2$censInformation): No censoring #> Minimization message (f2$message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION SUCCESSFUL #> HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION. #> REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY #> AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT. #> NO. OF FUNCTION EVALUATIONS USED: 2391 #> NO. OF SIG. DIGITS IN FINAL EST.: 4.1 #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.85e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.45e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.89e-06, 0.000506); atol=7.19e-05 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (5.14e-07,0.00318); atol=6.45e-06 #> nonmem model: 'pk.turnover.emax4-nonmem/pk.turnover.emax4.nmctl' #> #> ── Fit Data (object f2 is a modified tibble): ── #> # A tibble: 483 × 32 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.cl eta.v eta.ec50 #> #> 1 1 0.5 cp 0 1.16 -1.16 0.920 -0.920 -1.56 0.689 0.228 0.160 #> 2 1 1 cp 1.9 3.38 -1.48 2.68 -0.780 -1.09 0.689 0.228 0.160 #> 3 1 2 cp 3.3 7.53 -4.23 5.94 -2.64 -2.36 0.689 0.228 0.160 #> # ℹ 480 more rows #> # ℹ 20 more variables: eta.kout , eta.e0 , cp , depot , #> # gut , center , effect , ktr , ka , cl , #> # v , emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum v1s <- vpcPlot(f2, show=list(obs_dv=TRUE), scales=\"free_y\") + ylab(\"Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ v2s <- vpcPlot(f2, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") + ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") library() v1s v2s"},{"path":"/articles/running-pknca.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Obtain initial estimates and unit conversions with PKNCA","text":"Initial estimates compartmental population PK model can obtained using babelmixr2 \"pknca\" estimation method. Also, central compartment scaling factor can auto-generated based units dosing, concentration measurement, desired volume distribution units, time. need perform NCA analysis hand; \"pknca\" estimation method perform NCA analysis using PKNCA package automatically. methods used converting NCA calculations parameter estimates described help nlmixr2Est.pknca().","code":""},{"path":"/articles/running-pknca.html","id":"initial-example","dir":"Articles","previous_headings":"","what":"Initial example","title":"Obtain initial estimates and unit conversions with PKNCA","text":"Initial model setup nlmixr2 model. must load babelmixr2 library nlmixr() function recognizes est = \"pknca\". use PKNCA get initial estimates, use est = \"pknca\" instead one nlmixr2 estimation methods. unit conversions, provide units control = pkncaControl() argument. Unit conversions supported units can automatically converted; mass/volume can converted mass/volume ratio, mass molar molar mass single mass--molar conversion factor. Now, prepared model updated initial estimates NCA results embedded. can see new model PKNCA estimates looking prepared$ui (model interpreted rxode2) prepared$nca (PKNCAresults object). Note new model, fixed effect initial estimates changed original values. residual error -subject variability unchanged. updated model, can perform estimation new model object, model created nlmixr2:","code":"library(babelmixr2) #> Loading required package: nlmixr2 #> Loading required package: nlmixr2data one.compartment <- function() { ini({ tka <- log(1.57); label(\"Ka (1/hr)\") tcl <- log(2.72); label(\"Cl (L/hr)\") tv <- log(31.5); label(\"V (L)\") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7; label(\"additive residual error (mg/L)\") }) # and a model block with the error specification and model specification model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) vc <- exp(tv + eta.v) d/dt(depot) <- -ka * depot d/dt(center) <- ka * depot - cl / vc * center cp <- center / vc cp ~ add(add.sd) }) } prepared <- nlmixr2( one.compartment, data = theo_sd, est = \"pknca\", control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\") ) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> Loading required namespace: testthat #> ℹ change initial estimate (0.89314878960486) and upper/lower bound (-3.50655789731998 to 3.72508541597241) of `tka` #> → significant model change detected #> → removed from model: '$getSplitModel' #> ℹ change initial estimate (8.41044546236311) and upper/lower bound (5.51439905878865 to 10.899462850803) of `tcl` #> ℹ change initial estimate (10.5377244826318) and upper/lower bound (7.94567233496473 to 13.1050053785005) of `tv` prepared$ui #> ── rxode2-based free-form 2-cmt ODE model ────────────────────────────────────── #> ── Initalization: ── #> Fixed Effects ($theta): #> tka tcl tv add.sd #> 0.8931488 8.4104455 10.5377245 0.7000000 #> #> Omega ($omega): #> eta.ka eta.cl eta.v #> eta.ka 0.6 0.0 0.0 #> eta.cl 0.0 0.3 0.0 #> eta.v 0.0 0.0 0.1 #> #> States ($state or $stateDf): #> Compartment Number Compartment Name #> 1 1 depot #> 2 2 center #> ── μ-referencing ($muRefTable): ── #> theta eta level #> 1 tka eta.ka id #> 2 tcl eta.cl id #> 3 tv eta.v id #> #> ── Model (Normalized Syntax): ── #> function() { #> ini({ #> tka <- c(-3.50655789731998, 0.89314878960486, 3.72508541597241) #> label(\"Ka (1/hr)\") #> tcl <- c(5.51439905878865, 8.41044546236311, 10.899462850803) #> label(\"Cl (L/hr)\") #> tv <- c(7.94567233496473, 10.5377244826318, 13.1050053785005) #> label(\"V (L)\") #> add.sd <- c(0, 0.7) #> label(\"additive residual error (mg/L)\") #> eta.ka ~ 0.6 #> eta.cl ~ 0.3 #> eta.v ~ 0.1 #> }) #> model({ #> ka <- exp(tka + eta.ka) #> cl <- exp(tcl + eta.cl) #> vc <- exp(tv + eta.v) #> d/dt(depot) <- -ka * depot #> d/dt(center) <- ka * depot - cl/vc * center #> cp <- 1000 * center/vc #> cp ~ add(add.sd) #> }) #> } knitr::knit_print( summary(prepared$nca) ) #> Interval Start Interval End N AUClast (hr*ng/mL) Cmax (ng/mL) #> 0 24 12 74.6 [24.3] . #> 0 Inf 12 . 8.65 [17.0] #> Tmax (hr) CL (based on AUClast) (mg/(hr*ng/mL)) #> . 4.22 [23.0] #> 1.14 [0.630, 3.55] . #> Vss (based on AUClast) (mg/(ng/mL)) Half-life (hr) AUCinf,obs (hr*ng/mL) #> 25.0 [18.5] . . #> . 8.18 [2.12] 115 [28.4] #> Cmax (dose-normalized) ((ng/mL)/mg) #> . #> 0.0274 [18.1] #> #> Caption: AUClast, Cmax, CL (based on AUClast), Vss (based on AUClast), AUCinf,obs, Cmax (dose-normalized): geometric mean and geometric coefficient of variation; Tmax: median and range; Half-life: arithmetic mean and standard deviation; N: number of subjects fit <- nlmixr(prepared, data = theo_sd, est = \"focei\", control = list(print = 0)) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(R²)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 3.0.2 using 2 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> calculating covariance matrix #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 5952 #> → compress parHistData in nlmixr2 object, save 10632 fit #> ── nlmixr² FOCEi (outer: nlminb) ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) Condition#(Cor) #> FOCEi 116.9548 373.5546 393.7342 -179.7773 66.5244 12.97786 #> #> ── Time (sec fit$time): ── #> #> setup optimize covariance table compress other #> elapsed 0.097378 0.490291 0.490293 0.115 0.01 7.438038 #> #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #> #> Parameter Est. SE %RSE #> tka Ka (1/hr) 0.469 0.224 47.6 #> tcl Cl (L/hr) 7.92 0.0929 1.17 #> tv V (L) 10.4 0.0602 0.581 #> add.sd additive residual error (mg/L) 0.697 #> Back-transformed(95%CI) BSV(CV%) Shrink(SD)% #> tka 1.6 (1.03, 2.48) 68.0 -0.666% #> tcl 2.75e+03 (2.29e+03, 3.3e+03) 26.1 3.95% #> tv 3.19e+04 (2.83e+04, 3.59e+04) 15.4 14.5% #> add.sd 0.697 #> #> Covariance Type (fit$covMethod): r,s #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink #> Information about run found (fit$runInfo): #> • gradient problems with initial estimate and covariance; see $scaleInfo #> • last objective function was not at minimum, possible problems in optimization #> • ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.)) #> • initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=)) #> Censoring (fit$censInformation): No censoring #> Minimization message (fit$message): #> false convergence (8) #> In an ODE system, false convergence may mean \"useless\" evaluations were performed. #> See https://tinyurl.com/yyrrwkce #> It could also mean the convergence is poor, check results before accepting fit #> You may also try a good derivative free optimization: #> nlmixr2(...,control=list(outerOpt=\"bobyqa\")) #> #> ── Fit Data (object fit is a modified tibble): ── #> # A tibble: 132 × 22 #> ID TIME DV PRED RES WRES IPRED IRES IWRES CPRED CRES CWRES #> #> 1 1 0 0.74 0 0.74 1.06 0 0.74 1.06 0 0.74 1.06 #> 2 1 0.25 2.84 3.27 -0.432 -0.234 3.84 -1.00 -1.44 3.23 -0.389 -0.185 #> 3 1 0.57 6.57 5.84 0.730 0.297 6.78 -0.215 -0.308 5.78 0.786 0.287 #> # ℹ 129 more rows #> # ℹ 10 more variables: eta.ka , eta.cl , eta.v , depot , #> # center , ka , cl , vc , tad , dosenum "},{"path":"/articles/running-pknca.html","id":"give-pknca-a-different-dataset-or-a-completed-nca-analysis","dir":"Articles","previous_headings":"","what":"Give PKNCA a different dataset or a completed NCA analysis","title":"Obtain initial estimates and unit conversions with PKNCA","text":"get initial estimate, babelmixr2 automatically converts modeling dataset format needed PKNCA, NCA automatically performed using data. cases (e.g. studies sparse data), NCA may feasible. cases, can provide different dataset PKNCA compared full modeling dataset. Usually, simplest method provide single-dose, dense-sampling, dose-ranging data (.e. single-ascending dose portion first--human study) estimated. , give data PKNCA using ncaData argument pkncaControl() follows: initial estimates now based NCA calculated dNCA dataset rather full theo_sd dataset. already NCA results calculated PKNCA required parameters (“tmax”, “cmax.dn”, “cllast”), can provide instead using pkncaControl(ncaResults) argument.","code":"# Choose a subset of the full dataset for NCA dNCA <- theo_sd[theo_sd$ID <= 6, ] preparedNcaData <- nlmixr2( one.compartment, data = theo_sd, est = \"pknca\", control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\", ncaData = dNCA) ) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ change initial estimate (0.929027077269762) and upper/lower bound (-3.50655789731998 to 3.32136703319919) of `tka` #> → significant model change detected #> → removed from model: '$getSplitModel' #> ℹ change initial estimate (8.3955404628088) and upper/lower bound (5.85241523541802 to 10.7637056987378) of `tcl` #> ℹ change initial estimate (10.5377244826318) and upper/lower bound (7.94370069836702 to 13.1024358787022) of `tv` preparedNcaData$ui #> ── rxode2-based free-form 2-cmt ODE model ────────────────────────────────────── #> ── Initalization: ── #> Fixed Effects ($theta): #> tka tcl tv add.sd #> 0.9290271 8.3955405 10.5377245 0.7000000 #> #> Omega ($omega): #> eta.ka eta.cl eta.v #> eta.ka 0.6 0.0 0.0 #> eta.cl 0.0 0.3 0.0 #> eta.v 0.0 0.0 0.1 #> #> States ($state or $stateDf): #> Compartment Number Compartment Name #> 1 1 depot #> 2 2 center #> ── μ-referencing ($muRefTable): ── #> theta eta level #> 1 tka eta.ka id #> 2 tcl eta.cl id #> 3 tv eta.v id #> #> ── Model (Normalized Syntax): ── #> function() { #> ini({ #> tka <- c(-3.50655789731998, 0.929027077269762, 3.32136703319919) #> label(\"Ka (1/hr)\") #> tcl <- c(5.85241523541802, 8.3955404628088, 10.7637056987378) #> label(\"Cl (L/hr)\") #> tv <- c(7.94370069836702, 10.5377244826318, 13.1024358787022) #> label(\"V (L)\") #> add.sd <- c(0, 0.7) #> label(\"additive residual error (mg/L)\") #> eta.ka ~ 0.6 #> eta.cl ~ 0.3 #> eta.v ~ 0.1 #> }) #> model({ #> ka <- exp(tka + eta.ka) #> cl <- exp(tcl + eta.cl) #> vc <- exp(tv + eta.v) #> d/dt(depot) <- -ka * depot #> d/dt(center) <- ka * depot - cl/vc * center #> cp <- 1000 * center/vc #> cp ~ add(add.sd) #> }) #> }"},{"path":"/articles/running-pknca.html","id":"model-requirements","dir":"Articles","previous_headings":"","what":"Model requirements","title":"Obtain initial estimates and unit conversions with PKNCA","text":"update initial estimates, model must parameters model() block names expected est = \"pknca\". expected names : ka vc cl vp vp2 q q2 parameter names found model() block automatically traced back initial conditions (ini() block), parameter values updated. parameter estimated log scale, updated parameter value automatically converted log scale.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthew Fidler. Author, maintainer. Bill Denney. Author. Nook Fulloption. Contributor. goldfish art","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Fidler M, Xiong Y, Schoemaker R, Wilkins J, Trame M, Hooijmaijers R, Post T, Wang W (2024). nlmixr: Nonlinear Mixed Effects Models Population Pharmacokinetics Pharmacodynamics. R package version 0.1.5, https://CRAN.R-project.org/package=nlmixr. Fidler M, Wilkins J, Hooijmaijers R, Post T, Schoemaker R, Trame M, Xiong Y, Wang W (2019). “Nonlinear Mixed-Effects Model Development Simulation Using nlmixr Related R Open-Source Packages.” CPT: Pharmacometrics & Systems Pharmacology, 8(9), 621–633. Schoemaker R, Fidler M, Laveille C, Wilkins J, Hooijmaijers R, Post T, Trame M, Xiong Y, Wang W (2019). “Performance SAEM FOCEI Algorithms Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr.” CPT: Pharmacometrics & Systems Pharmacology, 8(12), 923–930.","code":"@Manual{, title = {{nlmixr}: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics}, author = {Matthew Fidler and Yuan Xiong and Rik Schoemaker and Justin Wilkins and Mirjam Trame and Richard Hooijmaijers and Teun Post and Wenping Wang}, year = {2024}, note = {R package version 0.1.5}, url = {https://CRAN.R-project.org/package=nlmixr}, } @Article{, title = {Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages}, author = {Matthew Fidler and Justin Wilkins and Richard Hooijmaijers and Teun Post and Rik Schoemaker and Mirjam Trame and Yuan Xiong and Wenping Wang}, journal = {CPT: Pharmacometrics \\& Systems Pharmacology}, year = {2019}, volume = {8}, pages = {621--633}, number = {9}, month = {sep}, abstract = {nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.}, address = {Hoboken}, publisher = {John Wiley and Sons Inc.}, } @Article{, title = {Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr}, author = {Rik Schoemaker and Matthew Fidler and Christian Laveille and Justin Wilkins and Richard Hooijmaijers and Teun Post and Mirjam Trame and Yuan Xiong and Wenping Wang}, journal = {CPT: Pharmacometrics \\& Systems Pharmacology}, year = {2019}, volume = {8}, pages = {923--930}, number = {12}, month = {dec}, abstract = {The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.}, }"},{"path":"/index.html","id":"babelmixr2","dir":"","previous_headings":"","what":"Use nlmixr2 to Interact with Open Source and Commercial Software","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"goal babelmixr2 convert nlmixr2 syntax commonly used tools.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"can install released version babelmixr2 CRAN : can install r-universe : Otherwise can always install GitHub:","code":"install.packages(\"babelmixr2\") # Download and install babelmixr2 in R install.packages('babelmixr2', repos = c( nlmixr2 = 'https://nlmixr2.r-universe.dev', CRAN = 'https://cloud.r-project.org'))"},{"path":"/index.html","id":"what-you-can-do-with-babelmixr2","dir":"","previous_headings":"","what":"What you can do with babelmixr2","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"Babelmixr2 can help : Running nlmixr2 model commercial nonlinear mixed effects modeling tool like NONMEM Monolix Convert NONMEM model nlmixr2 model (conjunction nonmem2rx) Convert Monolix model nlmixr2 model (conjunction monolix2rx) Calculate scaling factors automatically add initial conditions based non-compartmental analysis (using PKNCA) Perform Optimal design using nlmixr2 interface PopED","code":""},{"path":"/index.html","id":"monolix-setup","dir":"","previous_headings":"","what":"Monolix Setup","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"required, can get/install R ‘lixoftConnectors’ package ‘Monolix’ installation, described following url https://monolixsuite.slp-software.com/r-functions/2024R1/installation--initialization. ‘lixoftConnectors’ available, R can run ‘Monolix’ directly instead using command line.","code":""},{"path":"/index.html","id":"pknca-example","dir":"","previous_headings":"","what":"PKNCA Example","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"installed, use standard interface, can obtain new initial estimates PKNCA:","code":"mod <- nlmixr2( nlmixrFun, nlmmixrData, est = \"pknca\", control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\") )"},{"path":"/index.html","id":"monolix-example","dir":"","previous_headings":"","what":"Monolix example","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"babelmixr2 loaded, can use nlmixr2 convert nlmixr2 model Monolix, run monolix, import back nlmixr2 following:","code":"mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"monolix\")"},{"path":"/index.html","id":"nonmem-example","dir":"","previous_headings":"","what":"NONMEM example","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"babelmixr2 loaded can use nlmixr2 convert nlmixr2 model NONMEM, run NONMEM import back nlmixr2 following:","code":"mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"nonmem\")"},{"path":"/reference/as.nlmixr2.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an object to a nlmixr2 fit object — as.nlmixr2","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Convert object nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"as.nlmixr2( x, ..., table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), ci = 0.95 ) as.nlmixr( x, ..., table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), ci = 0.95 )"},{"path":"/reference/as.nlmixr2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"x Object convert ... arguments table nlmixr2est::tableControl() options rxControl rxode2::rxControl() options, generally needed addl doses handled translation ci confidence interval residual differences calculated (default 0.95)","code":""},{"path":"/reference/as.nlmixr2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Matthew L. Fidler","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"# \\donttest{ # First read in the model (but without residuals) mod <- nonmem2rx(system.file(\"mods/cpt/runODE032.ctl\", package=\"nonmem2rx\"), determineError=FALSE, lst=\".res\", save=FALSE) #> ℹ getting information from '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl' #> ℹ reading in xml file #> ℹ done #> ℹ reading in ext file #> ℹ done #> ℹ reading in phi file #> ℹ done #> ℹ reading in lst file #> ℹ abbreviated list parsing #> ℹ done #> ℹ done #> ℹ splitting control stream by records #> ℹ done #> ℹ Processing record $INPUT #> ℹ Processing record $MODEL #> ℹ Processing record $gTHETA #> ℹ Processing record $OMEGA #> ℹ Processing record $SIGMA #> ℹ Processing record $PROBLEM #> ℹ Processing record $DATA #> ℹ Processing record $SUBROUTINES #> ℹ Processing record $PK #> ℹ Processing record $DES #> ℹ Processing record $ERROR #> ℹ Processing record $ESTIMATION #> ℹ Ignore record $ESTIMATION #> ℹ Processing record $COVARIANCE #> ℹ Ignore record $COVARIANCE #> ℹ Processing record $TABLE #> ℹ change initial estimate of `theta1` to `1.37034036528946` #> ℹ change initial estimate of `theta2` to `4.19814911033061` #> ℹ change initial estimate of `theta3` to `1.38003493562413` #> ℹ change initial estimate of `theta4` to `3.87657341967489` #> ℹ change initial estimate of `theta5` to `0.196446108190896` #> ℹ change initial estimate of `eta1` to `0.101251418415006` #> ℹ change initial estimate of `eta2` to `0.0993872449483344` #> ℹ change initial estimate of `eta3` to `0.101302674763154` #> ℹ change initial estimate of `eta4` to `0.0730497519364148` #> ℹ read in nonmem input data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/Bolus_2CPT.csv #> ℹ ignoring lines that begin with a letter (IGNORE=@)' #> ℹ applying names specified by $INPUT #> ℹ subsetting accept/ignore filters code: .data[-which((.data$SD == 0)),] #> ℹ done #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ read in nonmem IPRED data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.csv #> ℹ done #> ℹ changing most variables to lower case #> ℹ done #> ℹ replace theta names #> ℹ done #> ℹ replace eta names #> ℹ done (no labels) #> ℹ renaming compartments #> ℹ done #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done # define the model with residuals (and change the name of the # parameters) In this step you need to be careful to not change the # estimates and make sure the residual estimates are correct (could # have to change var to sd). mod2 <-function() { ini({ lcl <- 1.37034036528946 lvc <- 4.19814911033061 lq <- 1.38003493562413 lvp <- 3.87657341967489 RSV <- c(0, 0.196446108190896, 1) eta.cl ~ 0.101251418415006 eta.v ~ 0.0993872449483344 eta.q ~ 0.101302674763154 eta.v2 ~ 0.0730497519364148 }) model({ cmt(CENTRAL) cmt(PERI) cl <- exp(lcl + eta.cl) v <- exp(lvc + eta.v) q <- exp(lq + eta.q) v2 <- exp(lvp + eta.v2) v1 <- v scale1 <- v k21 <- q/v2 k12 <- q/v d/dt(CENTRAL) <- k21 * PERI - k12 * CENTRAL - cl * CENTRAL/v1 d/dt(PERI) <- -k21 * PERI + k12 * CENTRAL f <- CENTRAL/scale1 f ~ prop(RSV) }) } # now we create another nonmem2rx object that validates the model above: new <- as.nonmem2rx(mod2, mod) #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ copy 'dfSub' to nonmem2rx model #> ℹ copy 'thetaMat' to nonmem2rx model #> ℹ copy 'dfObs' to nonmem2rx model #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done # once that is done, you can translate to a full nlmixr2 fit (if you wish) fit <- as.nlmixr2(new) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> → optimizing duplicate expressions in EBE model... #> → compiling EBE model... #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 3.0.2 using 2 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 204016 #> → compress parHistData in nlmixr2 object, save 2176 print(fit) #> ── nlmixr² nonmem2rx reading NONMEM ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem2rx 15977.28 20185.64 20237.23 -10083.82 335.4129 #> Condition#(Cor) #> nonmem2rx 2.096559 #> #> ── Time (sec $time): ── #> #> setup table compress NONMEM as.nlmixr2 #> elapsed 0.038503 0.131 0.018 100.95 2.899 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)% #> lcl 1.37 0.0298 2.17 3.94 (3.71, 4.17) 32.6 1.94% #> lvc 4.2 0.0295 0.703 66.6 (62.8, 70.5) 32.3 2.46% #> lq 1.38 0.0547 3.96 3.98 (3.57, 4.42) 32.7 40.5% #> lvp 3.88 0.0348 0.899 48.3 (45.1, 51.7) 27.5 28.4% #> RSV 0.196 0.196 #> #> Covariance Type ($covMethod): nonmem2rx #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink #> Censoring ($censInformation): No censoring #> Minimization message ($message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION SUCCESSFUL #> NO. OF FUNCTION EVALUATIONS USED: 320 #> NO. OF SIG. DIGITS IN FINAL EST.: 2.5 #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.43e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.41e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.25e-05, 0.0418); atol=0.00167 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (1.41e-07,0.00382); atol=6.41e-06 #> nonmem2rx model file: '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl' #> #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 2,280 × 25 #> ID TIME DV PRED RES IPRED IRES IWRES eta.cl eta.v eta.q eta.v2 #> #> 1 1 0.25 1041. 1750. -710. 1215. -175. -0.732 -0.144 0.375 0.0650 0.241 #> 2 1 0.5 1629 1700. -70.8 1192. 437. 1.87 -0.144 0.375 0.0650 0.241 #> 3 1 0.75 878. 1651. -774. 1169. -291. -1.27 -0.144 0.375 0.0650 0.241 #> # ℹ 2,277 more rows #> # ℹ 13 more variables: f , CENTRAL , PERI , cl , v , #> # q , v2 , v1 , scale1 , k21 , k12 , tad , #> # dosenum # }"},{"path":"/reference/babel.poped.database.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand a babelmixr2 PopED database — babel.poped.database","title":"Expand a babelmixr2 PopED database — babel.poped.database","text":"Expand babelmixr2 PopED database","code":""},{"path":"/reference/babel.poped.database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand a babelmixr2 PopED database — babel.poped.database","text":"","code":"babel.poped.database(popedInput, ..., optTime = NA)"},{"path":"/reference/babel.poped.database.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand a babelmixr2 PopED database — babel.poped.database","text":"popedInput babelmixr2 generated PopED database ... parameters sent PopED::create.poped.database() optTime boolean indicate global time indexer inside babelmixr2 reset times different. default TRUE. FALSE can get slightly better run times possibly slightly different results. optTime FALSE global indexer reset every time PopED rxode2 setup problem poped dataset created. can manually reset popedMultipleEndpointResetTimeIndex()","code":""},{"path":"/reference/babel.poped.database.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expand a babelmixr2 PopED database — babel.poped.database","text":"babelmixr2 PopED database ($babelmixr2 database)","code":""},{"path":"/reference/babel.poped.database.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Expand a babelmixr2 PopED database — babel.poped.database","text":"Matthew L. Fidler","code":""},{"path":"/reference/babelBpopIdx.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"may work poped databases population parameters named.","code":""},{"path":"/reference/babelBpopIdx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"","code":"babelBpopIdx(popedInput, var)"},{"path":"/reference/babelBpopIdx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"popedInput babelmixr2 created database var variable query","code":""},{"path":"/reference/babelBpopIdx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"index variable","code":""},{"path":"/reference/babelBpopIdx.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"Matthew L. Fidler","code":""},{"path":"/reference/babelBpopIdx.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"","code":"if (requireNamespace(\"PopED\", quietly=TRUE)) { f <- function() { ini({ tV <- 72.8 tKa <- 0.25 tCl <- 3.75 tF <- fix(0.9) pedCL <- 0.8 eta.v ~ 0.09 eta.ka ~ 0.09 eta.cl ~0.25^2 prop.sd <- fix(sqrt(0.04)) add.sd <- fix(sqrt(5e-6)) }) model({ V<-tV*exp(eta.v) KA<-tKa*exp(eta.ka) * (pedCL**isPediatric) # add covariate for pediatrics CL<-tCl*exp(eta.cl) Favail <- tF N <- floor(t/TAU)+1 y <- (DOSE*Favail/V)*(KA/(KA - CL/V)) * (exp(-CL/V * (t - (N - 1) * TAU)) * (1 - exp(-N * CL/V * TAU))/(1 - exp(-CL/V * TAU)) - exp(-KA * (t - (N - 1) * TAU)) * (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU))) y ~ prop(prop.sd) + add(add.sd) }) } e <- et(c( 1,8,10,240,245)) babel.db <- nlmixr2(f, e, \"poped\", popedControl(m = 2, groupsize=20, bUseGrouped_xt=TRUE, a=list(c(DOSE=20,TAU=24,isPediatric = 0), c(DOSE=40, TAU=24,isPediatric = 0)))) babelBpopIdx(babel.db, \"pedCL\") } #> #> #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> [1] 4"},{"path":"/reference/bblDatToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Convert nlmixr2-compatible data formats (possible)","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"bblDatToMonolix( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToNonmem( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToRxode( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToMrgsolve( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToPknca( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL )"},{"path":"/reference/bblDatToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"model rxode2 model conversion data Input dataset. table table control; mostly figure additional columns keep. rxControl rxode2 control options; figure handle addl dosing information. env NULL (default) nothing done. environment, function nlmixr2est::.foceiPreProcessData(data, env, model, rxControl) called provided environment.","code":""},{"path":"/reference/bblDatToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"function bblDatToMonolix() return list : Monolix compatible dataset ($monolix) Monolix ADM information ($adm) function nlmixrDataToNonmem() return dataset compatible NONMEM. function nlmixrDataToMrgsolve() return dataset compatible mrgsolve. Unlike NONMEM, supports replacement events evid=8 (note rxode2 replacement evid 5). function nlmixrDataToRxode() normalize dataset use newer evid definitions closer NONMEM instead classic definitions used lower level","code":""},{"path":"/reference/bblDatToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Matthew L. Fidler","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } bblDatToMonolix(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> $monolix #> ID TIME EVID AMT II DV ADM YTYPE SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 0 1 0 2 #> 3 1 1.0 0 NA 0 1.9 0 1 0 3 #> 4 1 2.0 0 NA 0 3.3 0 1 0 4 #> 5 1 3.0 0 NA 0 6.6 0 1 0 5 #> 6 1 6.0 0 NA 0 9.1 0 1 0 6 #> 7 1 9.0 0 NA 0 10.8 0 1 0 7 #> 8 1 12.0 0 NA 0 8.6 0 1 0 8 #> 9 1 24.0 0 NA 0 5.6 0 1 0 9 #> 10 1 24.0 0 NA 0 44.0 0 2 0 10 #> 11 1 36.0 0 NA 0 4.0 0 1 0 11 #> 12 1 36.0 0 NA 0 27.0 0 2 0 12 #> 13 1 48.0 0 NA 0 2.7 0 1 0 13 #> 14 1 48.0 0 NA 0 28.0 0 2 0 14 #> 15 1 72.0 0 NA 0 0.8 0 1 0 15 #> 16 1 72.0 0 NA 0 31.0 0 2 0 16 #> 17 1 96.0 0 NA 0 60.0 0 2 0 17 #> 18 1 120.0 0 NA 0 65.0 0 2 0 18 #> 19 1 144.0 0 NA 0 71.0 0 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 0 2 0 21 #> 22 2 24.0 0 NA 0 9.2 0 1 0 22 #> 23 2 24.0 0 NA 0 49.0 0 2 0 23 #> 24 2 36.0 0 NA 0 8.5 0 1 0 24 #> 25 2 36.0 0 NA 0 32.0 0 2 0 25 #> 26 2 48.0 0 NA 0 6.4 0 1 0 26 #> 27 2 48.0 0 NA 0 26.0 0 2 0 27 #> 28 2 72.0 0 NA 0 4.8 0 1 0 28 #> 29 2 72.0 0 NA 0 22.0 0 2 0 29 #> 30 2 96.0 0 NA 0 3.1 0 1 0 30 #> 31 2 96.0 0 NA 0 28.0 0 2 0 31 #> 32 2 120.0 0 NA 0 2.5 0 1 0 32 #> 33 2 120.0 0 NA 0 33.0 0 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 0 2 0 35 #> 36 3 0.5 0 NA 0 0.0 0 1 0 36 #> 37 3 2.0 0 NA 0 8.4 0 1 0 37 #> 38 3 3.0 0 NA 0 9.7 0 1 0 38 #> 39 3 6.0 0 NA 0 9.8 0 1 0 39 #> 40 3 12.0 0 NA 0 11.0 0 1 0 40 #> 41 3 24.0 0 NA 0 8.3 0 1 0 41 #> 42 3 24.0 0 NA 0 46.0 0 2 0 42 #> 43 3 36.0 0 NA 0 7.7 0 1 0 43 #> 44 3 36.0 0 NA 0 22.0 0 2 0 44 #> 45 3 48.0 0 NA 0 6.3 0 1 0 45 #> 46 3 48.0 0 NA 0 19.0 0 2 0 46 #> 47 3 72.0 0 NA 0 4.1 0 1 0 47 #> 48 3 72.0 0 NA 0 20.0 0 2 0 48 #> 49 3 96.0 0 NA 0 3.0 0 1 0 49 #> 50 3 96.0 0 NA 0 42.0 0 2 0 50 #> 51 3 120.0 0 NA 0 1.4 0 1 0 51 #> 52 3 120.0 0 NA 0 49.0 0 2 0 52 #> 53 3 144.0 0 NA 0 54.0 0 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 0 2 0 55 #> 56 4 3.0 0 NA 0 12.0 0 1 0 56 #> 57 4 6.0 0 NA 0 13.2 0 1 0 57 #> 58 4 9.0 0 NA 0 14.4 0 1 0 58 #> 59 4 24.0 0 NA 0 9.6 0 1 0 59 #> 60 4 24.0 0 NA 0 30.0 0 2 0 60 #> 61 4 36.0 0 NA 0 8.2 0 1 0 61 #> 62 4 36.0 0 NA 0 24.0 0 2 0 62 #> 63 4 48.0 0 NA 0 7.8 0 1 0 63 #> 64 4 48.0 0 NA 0 13.0 0 2 0 64 #> 65 4 72.0 0 NA 0 5.8 0 1 0 65 #> 66 4 72.0 0 NA 0 9.0 0 2 0 66 #> 67 4 96.0 0 NA 0 4.3 0 1 0 67 #> 68 4 96.0 0 NA 0 9.0 0 2 0 68 #> 69 4 120.0 0 NA 0 3.0 0 1 0 69 #> 70 4 120.0 0 NA 0 11.0 0 2 0 70 #> 71 4 144.0 0 NA 0 12.0 0 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 0 2 0 73 #> 74 5 3.0 0 NA 0 11.1 0 1 0 74 #> 75 5 6.0 0 NA 0 11.9 0 1 0 75 #> 76 5 9.0 0 NA 0 9.8 0 1 0 76 #> 77 5 12.0 0 NA 0 11.0 0 1 0 77 #> 78 5 24.0 0 NA 0 8.5 0 1 0 78 #> 79 5 24.0 0 NA 0 43.0 0 2 0 79 #> 80 5 36.0 0 NA 0 7.6 0 1 0 80 #> 81 5 36.0 0 NA 0 25.0 0 2 0 81 #> 82 5 48.0 0 NA 0 5.4 0 1 0 82 #> 83 5 48.0 0 NA 0 18.0 0 2 0 83 #> 84 5 72.0 0 NA 0 4.5 0 1 0 84 #> 85 5 72.0 0 NA 0 17.0 0 2 0 85 #> 86 5 96.0 0 NA 0 3.3 0 1 0 86 #> 87 5 96.0 0 NA 0 23.0 0 2 0 87 #> 88 5 120.0 0 NA 0 2.3 0 1 0 88 #> 89 5 120.0 0 NA 0 29.0 0 2 0 89 #> 90 5 144.0 0 NA 0 41.0 0 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 0 2 0 92 #> 93 6 6.0 0 NA 0 8.6 0 1 0 93 #> 94 6 12.0 0 NA 0 8.6 0 1 0 94 #> 95 6 24.0 0 NA 0 7.0 0 1 0 95 #> 96 6 24.0 0 NA 0 34.0 0 2 0 96 #> 97 6 36.0 0 NA 0 5.7 0 1 0 97 #> 98 6 36.0 0 NA 0 23.0 0 2 0 98 #> 99 6 48.0 0 NA 0 4.7 0 1 0 99 #> 100 6 48.0 0 NA 0 20.0 0 2 0 100 #> 101 6 72.0 0 NA 0 3.3 0 1 0 101 #> 102 6 72.0 0 NA 0 16.0 0 2 0 102 #> 103 6 96.0 0 NA 0 2.3 0 1 0 103 #> 104 6 96.0 0 NA 0 17.0 0 2 0 104 #> 105 6 120.0 0 NA 0 1.7 0 1 0 105 #> 106 6 120.0 0 NA 0 18.0 0 2 0 106 #> 107 6 144.0 0 NA 0 25.0 0 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 0 1 0 109 #> 110 7 6.0 0 NA 0 12.4 0 1 0 110 #> 111 7 9.0 0 NA 0 12.7 0 1 0 111 #> 112 7 12.0 0 NA 0 8.8 0 1 0 112 #> 113 7 24.0 0 NA 0 6.1 0 1 0 113 #> 114 7 24.0 0 NA 0 36.0 0 2 0 114 #> 115 7 36.0 0 NA 0 3.5 0 1 0 115 #> 116 7 36.0 0 NA 0 33.0 0 2 0 116 #> 117 7 48.0 0 NA 0 1.8 0 1 0 117 #> 118 7 48.0 0 NA 0 28.0 0 2 0 118 #> 119 7 72.0 0 NA 0 1.5 0 1 0 119 #> 120 7 72.0 0 NA 0 52.0 0 2 0 120 #> 121 7 96.0 0 NA 0 1.0 0 1 0 121 #> 122 7 96.0 0 NA 0 80.0 0 2 0 122 #> 123 7 120.0 0 NA 0 90.0 0 2 0 123 #> 124 7 144.0 0 NA 0 100.0 0 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 0 2 0 126 #> 127 8 2.0 0 NA 0 17.6 0 1 0 127 #> 128 8 3.0 0 NA 0 17.3 0 1 0 128 #> 129 8 6.0 0 NA 0 15.0 0 1 0 129 #> 130 8 9.0 0 NA 0 15.0 0 1 0 130 #> 131 8 12.0 0 NA 0 12.4 0 1 0 131 #> 132 8 24.0 0 NA 0 7.9 0 1 0 132 #> 133 8 24.0 0 NA 0 35.0 0 2 0 133 #> 134 8 36.0 0 NA 0 7.9 0 1 0 134 #> 135 8 36.0 0 NA 0 20.0 0 2 0 135 #> 136 8 48.0 0 NA 0 5.1 0 1 0 136 #> 137 8 48.0 0 NA 0 12.0 0 2 0 137 #> 138 8 72.0 0 NA 0 3.6 0 1 0 138 #> 139 8 72.0 0 NA 0 16.0 0 2 0 139 #> 140 8 96.0 0 NA 0 2.4 0 1 0 140 #> 141 8 96.0 0 NA 0 23.0 0 2 0 141 #> 142 8 120.0 0 NA 0 2.0 0 1 0 142 #> 143 8 120.0 0 NA 0 36.0 0 2 0 143 #> 144 8 144.0 0 NA 0 48.0 0 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 0 2 0 146 #> 147 9 0.5 0 NA 0 0.0 0 1 0 147 #> 148 9 1.0 0 NA 0 1.0 0 1 0 148 #> 149 9 2.0 0 NA 0 4.6 0 1 0 149 #> 150 9 3.0 0 NA 0 12.7 0 1 0 150 #> 151 9 3.0 0 NA 0 8.0 0 1 0 151 #> 152 9 6.0 0 NA 0 12.7 0 1 0 152 #> 153 9 6.0 0 NA 0 11.5 0 1 0 153 #> 154 9 9.0 0 NA 0 12.9 0 1 0 154 #> 155 9 9.0 0 NA 0 11.4 0 1 0 155 #> 156 9 12.0 0 NA 0 11.4 0 1 0 156 #> 157 9 12.0 0 NA 0 11.0 0 1 0 157 #> 158 9 24.0 0 NA 0 9.1 0 1 0 158 #> 159 9 24.0 0 NA 0 33.0 0 2 0 159 #> 160 9 36.0 0 NA 0 8.2 0 1 0 160 #> 161 9 36.0 0 NA 0 22.0 0 2 0 161 #> 162 9 48.0 0 NA 0 5.9 0 1 0 162 #> 163 9 48.0 0 NA 0 16.0 0 2 0 163 #> 164 9 72.0 0 NA 0 3.6 0 1 0 164 #> 165 9 72.0 0 NA 0 18.0 0 2 0 165 #> 166 9 96.0 0 NA 0 1.7 0 1 0 166 #> 167 9 96.0 0 NA 0 32.0 0 2 0 167 #> 168 9 120.0 0 NA 0 1.1 0 1 0 168 #> 169 9 120.0 0 NA 0 30.0 0 2 0 169 #> 170 9 144.0 0 NA 0 45.0 0 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 0 2 0 172 #> 173 10 24.0 0 NA 0 8.6 0 1 0 173 #> 174 10 24.0 0 NA 0 39.0 0 2 0 174 #> 175 10 36.0 0 NA 0 8.0 0 1 0 175 #> 176 10 36.0 0 NA 0 22.0 0 2 0 176 #> 177 10 48.0 0 NA 0 6.0 0 1 0 177 #> 178 10 48.0 0 NA 0 17.0 0 2 0 178 #> 179 10 72.0 0 NA 0 4.4 0 1 0 179 #> 180 10 72.0 0 NA 0 17.0 0 2 0 180 #> 181 10 96.0 0 NA 0 3.6 0 1 0 181 #> 182 10 96.0 0 NA 0 22.0 0 2 0 182 #> 183 10 120.0 0 NA 0 2.8 0 1 0 183 #> 184 10 120.0 0 NA 0 25.0 0 2 0 184 #> 185 10 144.0 0 NA 0 33.0 0 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 0 2 0 187 #> 188 11 1.5 0 NA 0 11.4 0 1 0 188 #> 189 11 3.0 0 NA 0 15.4 0 1 0 189 #> 190 11 6.0 0 NA 0 17.5 0 1 0 190 #> 191 11 12.0 0 NA 0 14.0 0 1 0 191 #> 192 11 24.0 0 NA 0 9.0 0 1 0 192 #> 193 11 24.0 0 NA 0 37.0 0 2 0 193 #> 194 11 36.0 0 NA 0 8.9 0 1 0 194 #> 195 11 36.0 0 NA 0 24.0 0 2 0 195 #> 196 11 48.0 0 NA 0 6.6 0 1 0 196 #> 197 11 48.0 0 NA 0 14.0 0 2 0 197 #> 198 11 72.0 0 NA 0 4.2 0 1 0 198 #> 199 11 72.0 0 NA 0 11.0 0 2 0 199 #> 200 11 96.0 0 NA 0 3.6 0 1 0 200 #> 201 11 96.0 0 NA 0 14.0 0 2 0 201 #> 202 11 120.0 0 NA 0 2.6 0 1 0 202 #> 203 11 120.0 0 NA 0 23.0 0 2 0 203 #> 204 11 144.0 0 NA 0 33.0 0 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 0 2 0 206 #> 207 12 1.5 0 NA 0 0.6 0 1 0 207 #> 208 12 3.0 0 NA 0 2.8 0 1 0 208 #> 209 12 6.0 0 NA 0 13.8 0 1 0 209 #> 210 12 9.0 0 NA 0 15.0 0 1 0 210 #> 211 12 24.0 0 NA 0 10.5 0 1 0 211 #> 212 12 24.0 0 NA 0 25.0 0 2 0 212 #> 213 12 36.0 0 NA 0 9.1 0 1 0 213 #> 214 12 36.0 0 NA 0 15.0 0 2 0 214 #> 215 12 48.0 0 NA 0 6.6 0 1 0 215 #> 216 12 48.0 0 NA 0 11.0 0 2 0 216 #> 217 12 72.0 0 NA 0 4.9 0 1 0 217 #> 218 12 96.0 0 NA 0 2.4 0 1 0 218 #> 219 12 120.0 0 NA 0 1.9 0 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 0 2 0 221 #> 222 13 1.5 0 NA 0 3.6 0 1 0 222 #> 223 13 3.0 0 NA 0 12.9 0 1 0 223 #> 224 13 6.0 0 NA 0 12.9 0 1 0 224 #> 225 13 9.0 0 NA 0 10.2 0 1 0 225 #> 226 13 24.0 0 NA 0 6.4 0 1 0 226 #> 227 13 24.0 0 NA 0 41.0 0 2 0 227 #> 228 13 36.0 0 NA 0 6.9 0 1 0 228 #> 229 13 36.0 0 NA 0 23.0 0 2 0 229 #> 230 13 48.0 0 NA 0 4.5 0 1 0 230 #> 231 13 48.0 0 NA 0 16.0 0 2 0 231 #> 232 13 72.0 0 NA 0 3.2 0 1 0 232 #> 233 13 72.0 0 NA 0 14.0 0 2 0 233 #> 234 13 96.0 0 NA 0 2.4 0 1 0 234 #> 235 13 96.0 0 NA 0 18.0 0 2 0 235 #> 236 13 120.0 0 NA 0 1.3 0 1 0 236 #> 237 13 120.0 0 NA 0 22.0 0 2 0 237 #> 238 13 144.0 0 NA 0 35.0 0 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 0 2 0 240 #> 241 14 0.5 0 NA 0 0.0 0 1 0 241 #> 242 14 1.0 0 NA 0 2.7 0 1 0 242 #> 243 14 2.0 0 NA 0 11.6 0 1 0 243 #> 244 14 3.0 0 NA 0 11.6 0 1 0 244 #> 245 14 6.0 0 NA 0 11.3 0 1 0 245 #> 246 14 9.0 0 NA 0 9.7 0 1 0 246 #> 247 14 24.0 0 NA 0 6.5 0 1 0 247 #> 248 14 24.0 0 NA 0 32.0 0 2 0 248 #> 249 14 36.0 0 NA 0 5.2 0 1 0 249 #> 250 14 36.0 0 NA 0 22.0 0 2 0 250 #> 251 14 48.0 0 NA 0 3.6 0 1 0 251 #> 252 14 48.0 0 NA 0 21.0 0 2 0 252 #> 253 14 72.0 0 NA 0 2.4 0 1 0 253 #> 254 14 72.0 0 NA 0 28.0 0 2 0 254 #> 255 14 96.0 0 NA 0 0.9 0 1 0 255 #> 256 14 96.0 0 NA 0 38.0 0 2 0 256 #> 257 14 120.0 0 NA 0 46.0 0 2 0 257 #> 258 14 144.0 0 NA 0 65.0 0 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 0 2 0 260 #> 261 15 1.0 0 NA 0 6.6 0 1 0 261 #> 262 15 3.0 0 NA 0 11.9 0 1 0 262 #> 263 15 6.0 0 NA 0 11.7 0 1 0 263 #> 264 15 9.0 0 NA 0 12.2 0 1 0 264 #> 265 15 24.0 0 NA 0 8.1 0 1 0 265 #> 266 15 24.0 0 NA 0 43.0 0 2 0 266 #> 267 15 36.0 0 NA 0 7.4 0 1 0 267 #> 268 15 36.0 0 NA 0 26.0 0 2 0 268 #> 269 15 48.0 0 NA 0 6.8 0 1 0 269 #> 270 15 48.0 0 NA 0 15.0 0 2 0 270 #> 271 15 72.0 0 NA 0 5.3 0 1 0 271 #> 272 15 72.0 0 NA 0 13.0 0 2 0 272 #> 273 15 96.0 0 NA 0 3.0 0 1 0 273 #> 274 15 96.0 0 NA 0 21.0 0 2 0 274 #> 275 15 120.0 0 NA 0 2.0 0 1 0 275 #> 276 15 120.0 0 NA 0 28.0 0 2 0 276 #> 277 15 144.0 0 NA 0 39.0 0 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 0 2 0 279 #> 280 16 24.0 0 NA 0 10.4 0 1 0 280 #> 281 16 24.0 0 NA 0 42.0 0 2 0 281 #> 282 16 36.0 0 NA 0 8.9 0 1 0 282 #> 283 16 36.0 0 NA 0 32.0 0 2 0 283 #> 284 16 48.0 0 NA 0 7.0 0 1 0 284 #> 285 16 48.0 0 NA 0 26.0 0 2 0 285 #> 286 16 72.0 0 NA 0 4.4 0 1 0 286 #> 287 16 72.0 0 NA 0 31.0 0 2 0 287 #> 288 16 96.0 0 NA 0 3.2 0 1 0 288 #> 289 16 96.0 0 NA 0 33.0 0 2 0 289 #> 290 16 120.0 0 NA 0 2.4 0 1 0 290 #> 291 16 120.0 0 NA 0 54.0 0 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 0 2 0 293 #> 294 17 24.0 0 NA 0 7.6 0 1 0 294 #> 295 17 24.0 0 NA 0 35.0 0 2 0 295 #> 296 17 36.0 0 NA 0 6.4 0 1 0 296 #> 297 17 36.0 0 NA 0 23.0 0 2 0 297 #> 298 17 48.0 0 NA 0 6.0 0 1 0 298 #> 299 17 48.0 0 NA 0 17.0 0 2 0 299 #> 300 17 72.0 0 NA 0 4.0 0 1 0 300 #> 301 17 72.0 0 NA 0 18.0 0 2 0 301 #> 302 17 96.0 0 NA 0 3.1 0 1 0 302 #> 303 17 96.0 0 NA 0 18.0 0 2 0 303 #> 304 17 120.0 0 NA 0 2.0 0 1 0 304 #> 305 17 120.0 0 NA 0 21.0 0 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 0 2 0 307 #> 308 18 24.0 0 NA 0 7.6 0 1 0 308 #> 309 18 24.0 0 NA 0 32.0 0 2 0 309 #> 310 18 36.0 0 NA 0 6.6 0 1 0 310 #> 311 18 36.0 0 NA 0 20.0 0 2 0 311 #> 312 18 48.0 0 NA 0 5.4 0 1 0 312 #> 313 18 48.0 0 NA 0 18.0 0 2 0 313 #> 314 18 72.0 0 NA 0 3.4 0 1 0 314 #> 315 18 72.0 0 NA 0 18.0 0 2 0 315 #> 316 18 96.0 0 NA 0 1.2 0 1 0 316 #> 317 18 96.0 0 NA 0 19.0 0 2 0 317 #> 318 18 120.0 0 NA 0 0.9 0 1 0 318 #> 319 18 120.0 0 NA 0 29.0 0 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 0 2 0 321 #> 322 19 24.0 0 NA 0 6.6 0 1 0 322 #> 323 19 24.0 0 NA 0 33.0 0 2 0 323 #> 324 19 36.0 0 NA 0 5.3 0 1 0 324 #> 325 19 36.0 0 NA 0 28.0 0 2 0 325 #> 326 19 48.0 0 NA 0 3.6 0 1 0 326 #> 327 19 48.0 0 NA 0 18.0 0 2 0 327 #> 328 19 72.0 0 NA 0 2.7 0 1 0 328 #> 329 19 72.0 0 NA 0 18.0 0 2 0 329 #> 330 19 96.0 0 NA 0 1.4 0 1 0 330 #> 331 19 96.0 0 NA 0 17.0 0 2 0 331 #> 332 19 120.0 0 NA 0 1.1 0 1 0 332 #> 333 19 120.0 0 NA 0 26.0 0 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 0 2 0 335 #> 336 20 24.0 0 NA 0 9.6 0 1 0 336 #> 337 20 24.0 0 NA 0 41.0 0 2 0 337 #> 338 20 36.0 0 NA 0 8.0 0 1 0 338 #> 339 20 36.0 0 NA 0 30.0 0 2 0 339 #> 340 20 48.0 0 NA 0 6.6 0 1 0 340 #> 341 20 48.0 0 NA 0 22.0 0 2 0 341 #> 342 20 72.0 0 NA 0 5.6 0 1 0 342 #> 343 20 72.0 0 NA 0 23.0 0 2 0 343 #> 344 20 96.0 0 NA 0 3.5 0 1 0 344 #> 345 20 96.0 0 NA 0 23.0 0 2 0 345 #> 346 20 120.0 0 NA 0 2.3 0 1 0 346 #> 347 20 120.0 0 NA 0 35.0 0 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 0 2 0 349 #> 350 21 24.0 0 NA 0 7.3 0 1 0 350 #> 351 21 24.0 0 NA 0 46.0 0 2 0 351 #> 352 21 36.0 0 NA 0 6.1 0 1 0 352 #> 353 21 36.0 0 NA 0 27.0 0 2 0 353 #> 354 21 48.0 0 NA 0 4.3 0 1 0 354 #> 355 21 48.0 0 NA 0 22.0 0 2 0 355 #> 356 21 72.0 0 NA 0 3.2 0 1 0 356 #> 357 21 72.0 0 NA 0 36.0 0 2 0 357 #> 358 21 96.0 0 NA 0 2.3 0 1 0 358 #> 359 21 96.0 0 NA 0 40.0 0 2 0 359 #> 360 21 120.0 0 NA 0 1.9 0 1 0 360 #> 361 21 120.0 0 NA 0 44.0 0 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 0 2 0 363 #> 364 22 24.0 0 NA 0 8.9 0 1 0 364 #> 365 22 24.0 0 NA 0 35.0 0 2 0 365 #> 366 22 36.0 0 NA 0 8.4 0 1 0 366 #> 367 22 36.0 0 NA 0 27.0 0 2 0 367 #> 368 22 48.0 0 NA 0 8.0 0 1 0 368 #> 369 22 48.0 0 NA 0 23.0 0 2 0 369 #> 370 22 72.0 0 NA 0 4.4 0 1 0 370 #> 371 22 72.0 0 NA 0 27.0 0 2 0 371 #> 372 22 96.0 0 NA 0 3.2 0 1 0 372 #> 373 22 96.0 0 NA 0 43.0 0 2 0 373 #> 374 22 120.0 0 NA 0 1.7 0 1 0 374 #> 375 22 120.0 0 NA 0 43.0 0 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 0 2 0 377 #> 378 23 24.0 0 NA 0 9.8 0 1 0 378 #> 379 23 24.0 0 NA 0 34.0 0 2 0 379 #> 380 23 36.0 0 NA 0 8.4 0 1 0 380 #> 381 23 36.0 0 NA 0 24.0 0 2 0 381 #> 382 23 48.0 0 NA 0 6.6 0 1 0 382 #> 383 23 48.0 0 NA 0 15.0 0 2 0 383 #> 384 23 72.0 0 NA 0 4.8 0 1 0 384 #> 385 23 72.0 0 NA 0 15.0 0 2 0 385 #> 386 23 96.0 0 NA 0 3.2 0 1 0 386 #> 387 23 96.0 0 NA 0 19.0 0 2 0 387 #> 388 23 120.0 0 NA 0 2.4 0 1 0 388 #> 389 23 120.0 0 NA 0 19.0 0 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 0 2 0 391 #> 392 24 24.0 0 NA 0 8.2 0 1 0 392 #> 393 24 24.0 0 NA 0 37.0 0 2 0 393 #> 394 24 36.0 0 NA 0 7.5 0 1 0 394 #> 395 24 36.0 0 NA 0 20.0 0 2 0 395 #> 396 24 48.0 0 NA 0 6.8 0 1 0 396 #> 397 24 48.0 0 NA 0 20.0 0 2 0 397 #> 398 24 72.0 0 NA 0 5.5 0 1 0 398 #> 399 24 72.0 0 NA 0 26.0 0 2 0 399 #> 400 24 96.0 0 NA 0 4.5 0 1 0 400 #> 401 24 96.0 0 NA 0 28.0 0 2 0 401 #> 402 24 120.0 0 NA 0 3.7 0 1 0 402 #> 403 24 120.0 0 NA 0 50.0 0 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 0 2 0 405 #> 406 25 24.0 0 NA 0 11.0 0 1 0 406 #> 407 25 24.0 0 NA 0 32.0 0 2 0 407 #> 408 25 36.0 0 NA 0 10.0 0 1 0 408 #> 409 25 36.0 0 NA 0 20.0 0 2 0 409 #> 410 25 48.0 0 NA 0 8.2 0 1 0 410 #> 411 25 48.0 0 NA 0 17.0 0 2 0 411 #> 412 25 72.0 0 NA 0 6.0 0 1 0 412 #> 413 25 72.0 0 NA 0 19.0 0 2 0 413 #> 414 25 96.0 0 NA 0 3.7 0 1 0 414 #> 415 25 96.0 0 NA 0 21.0 0 2 0 415 #> 416 25 120.0 0 NA 0 2.6 0 1 0 416 #> 417 25 120.0 0 NA 0 30.0 0 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 0 2 0 419 #> 420 26 24.0 0 NA 0 10.0 0 1 0 420 #> 421 26 24.0 0 NA 0 41.0 0 2 0 421 #> 422 26 36.0 0 NA 0 9.0 0 1 0 422 #> 423 26 36.0 0 NA 0 28.0 0 2 0 423 #> 424 26 48.0 0 NA 0 7.3 0 1 0 424 #> 425 26 48.0 0 NA 0 19.0 0 2 0 425 #> 426 26 72.0 0 NA 0 5.2 0 1 0 426 #> 427 26 72.0 0 NA 0 17.0 0 2 0 427 #> 428 26 96.0 0 NA 0 3.7 0 1 0 428 #> 429 26 96.0 0 NA 0 17.0 0 2 0 429 #> 430 26 120.0 0 NA 0 2.7 0 1 0 430 #> 431 26 120.0 0 NA 0 24.0 0 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 0 2 0 433 #> 434 27 24.0 0 NA 0 11.8 0 1 0 434 #> 435 27 24.0 0 NA 0 32.0 0 2 0 435 #> 436 27 36.0 0 NA 0 9.2 0 1 0 436 #> 437 27 36.0 0 NA 0 21.0 0 2 0 437 #> 438 27 48.0 0 NA 0 7.7 0 1 0 438 #> 439 27 48.0 0 NA 0 19.0 0 2 0 439 #> 440 27 72.0 0 NA 0 4.9 0 1 0 440 #> 441 27 72.0 0 NA 0 22.0 0 2 0 441 #> 442 27 96.0 0 NA 0 3.4 0 1 0 442 #> 443 27 96.0 0 NA 0 33.0 0 2 0 443 #> 444 27 120.0 0 NA 0 2.7 0 1 0 444 #> 445 27 120.0 0 NA 0 46.0 0 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 0 2 0 447 #> 448 28 24.0 0 NA 0 10.1 0 1 0 448 #> 449 28 24.0 0 NA 0 39.0 0 2 0 449 #> 450 28 36.0 0 NA 0 8.0 0 1 0 450 #> 451 28 36.0 0 NA 0 25.0 0 2 0 451 #> 452 28 48.0 0 NA 0 6.0 0 1 0 452 #> 453 28 48.0 0 NA 0 16.0 0 2 0 453 #> 454 28 72.0 0 NA 0 4.9 0 1 0 454 #> 455 28 72.0 0 NA 0 14.0 0 2 0 455 #> 456 28 96.0 0 NA 0 3.4 0 1 0 456 #> 457 28 96.0 0 NA 0 15.0 0 2 0 457 #> 458 28 120.0 0 NA 0 2.0 0 1 0 458 #> 459 28 120.0 0 NA 0 20.0 0 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 0 2 0 461 #> 462 29 24.0 0 NA 0 8.3 0 1 0 462 #> 463 29 24.0 0 NA 0 35.0 0 2 0 463 #> 464 29 36.0 0 NA 0 7.0 0 1 0 464 #> 465 29 36.0 0 NA 0 21.0 0 2 0 465 #> 466 29 48.0 0 NA 0 5.6 0 1 0 466 #> 467 29 48.0 0 NA 0 18.0 0 2 0 467 #> 468 29 72.0 0 NA 0 4.1 0 1 0 468 #> 469 29 72.0 0 NA 0 20.0 0 2 0 469 #> 470 29 96.0 0 NA 0 3.1 0 1 0 470 #> 471 29 96.0 0 NA 0 29.0 0 2 0 471 #> 472 29 120.0 0 NA 0 2.2 0 1 0 472 #> 473 29 120.0 0 NA 0 41.0 0 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 0 2 0 475 #> 476 30 24.0 0 NA 0 9.9 0 1 0 476 #> 477 30 24.0 0 NA 0 45.0 0 2 0 477 #> 478 30 36.0 0 NA 0 7.5 0 1 0 478 #> 479 30 36.0 0 NA 0 24.0 0 2 0 479 #> 480 30 48.0 0 NA 0 6.5 0 1 0 480 #> 481 30 48.0 0 NA 0 23.0 0 2 0 481 #> 482 30 72.0 0 NA 0 4.1 0 1 0 482 #> 483 30 72.0 0 NA 0 26.0 0 2 0 483 #> 484 30 96.0 0 NA 0 2.9 0 1 0 484 #> 485 30 96.0 0 NA 0 28.0 0 2 0 485 #> 486 30 120.0 0 NA 0 2.3 0 1 0 486 #> 487 30 120.0 0 NA 0 39.0 0 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 0 2 0 489 #> 490 31 24.0 0 NA 0 9.5 0 1 0 490 #> 491 31 24.0 0 NA 0 45.0 0 2 0 491 #> 492 31 36.0 0 NA 0 7.8 0 1 0 492 #> 493 31 36.0 0 NA 0 30.0 0 2 0 493 #> 494 31 48.0 0 NA 0 6.4 0 1 0 494 #> 495 31 48.0 0 NA 0 24.0 0 2 0 495 #> 496 31 72.0 0 NA 0 4.5 0 1 0 496 #> 497 31 72.0 0 NA 0 22.0 0 2 0 497 #> 498 31 96.0 0 NA 0 3.4 0 1 0 498 #> 499 31 96.0 0 NA 0 28.0 0 2 0 499 #> 500 31 120.0 0 NA 0 2.5 0 1 0 500 #> 501 31 120.0 0 NA 0 42.0 0 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 0 2 0 503 #> 504 32 24.0 0 NA 0 8.9 0 1 0 504 #> 505 32 24.0 0 NA 0 36.0 0 2 0 505 #> 506 32 36.0 0 NA 0 7.7 0 1 0 506 #> 507 32 36.0 0 NA 0 27.0 0 2 0 507 #> 508 32 48.0 0 NA 0 6.9 0 1 0 508 #> 509 32 48.0 0 NA 0 24.0 0 2 0 509 #> 510 32 72.0 0 NA 0 4.4 0 1 0 510 #> 511 32 72.0 0 NA 0 23.0 0 2 0 511 #> 512 32 96.0 0 NA 0 3.5 0 1 0 512 #> 513 32 96.0 0 NA 0 20.0 0 2 0 513 #> 514 32 120.0 0 NA 0 2.5 0 1 0 514 #> 515 32 120.0 0 NA 0 22.0 0 2 0 515 #> #> $adm #> adm cmt type #> 1 1 1 bolus #> bblDatToNonmem(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ID TIME EVID AMT DV CMT DVID nlmixrRowNums #> 1 1 0.0 1 100.0 NA 1 NA 1 #> 2 1 0.5 0 NA 0.0 NA 1 2 #> 3 1 1.0 0 NA 1.9 NA 1 3 #> 4 1 2.0 0 NA 3.3 NA 1 4 #> 5 1 3.0 0 NA 6.6 NA 1 5 #> 6 1 6.0 0 NA 9.1 NA 1 6 #> 7 1 9.0 0 NA 10.8 NA 1 7 #> 8 1 12.0 0 NA 8.6 NA 1 8 #> 9 1 24.0 0 NA 5.6 NA 1 9 #> 10 1 24.0 0 NA 44.0 NA 2 10 #> 11 1 36.0 0 NA 4.0 NA 1 11 #> 12 1 36.0 0 NA 27.0 NA 2 12 #> 13 1 48.0 0 NA 2.7 NA 1 13 #> 14 1 48.0 0 NA 28.0 NA 2 14 #> 15 1 72.0 0 NA 0.8 NA 1 15 #> 16 1 72.0 0 NA 31.0 NA 2 16 #> 17 1 96.0 0 NA 60.0 NA 2 17 #> 18 1 120.0 0 NA 65.0 NA 2 18 #> 19 1 144.0 0 NA 71.0 NA 2 19 #> 20 2 0.0 1 100.0 NA 1 NA 20 #> 21 2 0.0 0 NA 100.0 NA 2 21 #> 22 2 24.0 0 NA 9.2 NA 1 22 #> 23 2 24.0 0 NA 49.0 NA 2 23 #> 24 2 36.0 0 NA 8.5 NA 1 24 #> 25 2 36.0 0 NA 32.0 NA 2 25 #> 26 2 48.0 0 NA 6.4 NA 1 26 #> 27 2 48.0 0 NA 26.0 NA 2 27 #> 28 2 72.0 0 NA 4.8 NA 1 28 #> 29 2 72.0 0 NA 22.0 NA 2 29 #> 30 2 96.0 0 NA 3.1 NA 1 30 #> 31 2 96.0 0 NA 28.0 NA 2 31 #> 32 2 120.0 0 NA 2.5 NA 1 32 #> 33 2 120.0 0 NA 33.0 NA 2 33 #> 34 3 0.0 1 100.0 NA 1 NA 34 #> 35 3 0.0 0 NA 100.0 NA 2 35 #> 36 3 0.5 0 NA 0.0 NA 1 36 #> 37 3 2.0 0 NA 8.4 NA 1 37 #> 38 3 3.0 0 NA 9.7 NA 1 38 #> 39 3 6.0 0 NA 9.8 NA 1 39 #> 40 3 12.0 0 NA 11.0 NA 1 40 #> 41 3 24.0 0 NA 8.3 NA 1 41 #> 42 3 24.0 0 NA 46.0 NA 2 42 #> 43 3 36.0 0 NA 7.7 NA 1 43 #> 44 3 36.0 0 NA 22.0 NA 2 44 #> 45 3 48.0 0 NA 6.3 NA 1 45 #> 46 3 48.0 0 NA 19.0 NA 2 46 #> 47 3 72.0 0 NA 4.1 NA 1 47 #> 48 3 72.0 0 NA 20.0 NA 2 48 #> 49 3 96.0 0 NA 3.0 NA 1 49 #> 50 3 96.0 0 NA 42.0 NA 2 50 #> 51 3 120.0 0 NA 1.4 NA 1 51 #> 52 3 120.0 0 NA 49.0 NA 2 52 #> 53 3 144.0 0 NA 54.0 NA 2 53 #> 54 4 0.0 1 120.0 NA 1 NA 54 #> 55 4 0.0 0 NA 100.0 NA 2 55 #> 56 4 3.0 0 NA 12.0 NA 1 56 #> 57 4 6.0 0 NA 13.2 NA 1 57 #> 58 4 9.0 0 NA 14.4 NA 1 58 #> 59 4 24.0 0 NA 9.6 NA 1 59 #> 60 4 24.0 0 NA 30.0 NA 2 60 #> 61 4 36.0 0 NA 8.2 NA 1 61 #> 62 4 36.0 0 NA 24.0 NA 2 62 #> 63 4 48.0 0 NA 7.8 NA 1 63 #> 64 4 48.0 0 NA 13.0 NA 2 64 #> 65 4 72.0 0 NA 5.8 NA 1 65 #> 66 4 72.0 0 NA 9.0 NA 2 66 #> 67 4 96.0 0 NA 4.3 NA 1 67 #> 68 4 96.0 0 NA 9.0 NA 2 68 #> 69 4 120.0 0 NA 3.0 NA 1 69 #> 70 4 120.0 0 NA 11.0 NA 2 70 #> 71 4 144.0 0 NA 12.0 NA 2 71 #> 72 5 0.0 1 60.0 NA 1 NA 72 #> 73 5 0.0 0 NA 82.0 NA 2 73 #> 74 5 3.0 0 NA 11.1 NA 1 74 #> 75 5 6.0 0 NA 11.9 NA 1 75 #> 76 5 9.0 0 NA 9.8 NA 1 76 #> 77 5 12.0 0 NA 11.0 NA 1 77 #> 78 5 24.0 0 NA 8.5 NA 1 78 #> 79 5 24.0 0 NA 43.0 NA 2 79 #> 80 5 36.0 0 NA 7.6 NA 1 80 #> 81 5 36.0 0 NA 25.0 NA 2 81 #> 82 5 48.0 0 NA 5.4 NA 1 82 #> 83 5 48.0 0 NA 18.0 NA 2 83 #> 84 5 72.0 0 NA 4.5 NA 1 84 #> 85 5 72.0 0 NA 17.0 NA 2 85 #> 86 5 96.0 0 NA 3.3 NA 1 86 #> 87 5 96.0 0 NA 23.0 NA 2 87 #> 88 5 120.0 0 NA 2.3 NA 1 88 #> 89 5 120.0 0 NA 29.0 NA 2 89 #> 90 5 144.0 0 NA 41.0 NA 2 90 #> 91 6 0.0 1 113.0 NA 1 NA 91 #> 92 6 0.0 0 NA 100.0 NA 2 92 #> 93 6 6.0 0 NA 8.6 NA 1 93 #> 94 6 12.0 0 NA 8.6 NA 1 94 #> 95 6 24.0 0 NA 7.0 NA 1 95 #> 96 6 24.0 0 NA 34.0 NA 2 96 #> 97 6 36.0 0 NA 5.7 NA 1 97 #> 98 6 36.0 0 NA 23.0 NA 2 98 #> 99 6 48.0 0 NA 4.7 NA 1 99 #> 100 6 48.0 0 NA 20.0 NA 2 100 #> 101 6 72.0 0 NA 3.3 NA 1 101 #> 102 6 72.0 0 NA 16.0 NA 2 102 #> 103 6 96.0 0 NA 2.3 NA 1 103 #> 104 6 96.0 0 NA 17.0 NA 2 104 #> 105 6 120.0 0 NA 1.7 NA 1 105 #> 106 6 120.0 0 NA 18.0 NA 2 106 #> 107 6 144.0 0 NA 25.0 NA 2 107 #> 108 7 0.0 1 90.0 NA 1 NA 108 #> 109 7 3.0 0 NA 13.4 NA 1 109 #> 110 7 6.0 0 NA 12.4 NA 1 110 #> 111 7 9.0 0 NA 12.7 NA 1 111 #> 112 7 12.0 0 NA 8.8 NA 1 112 #> 113 7 24.0 0 NA 6.1 NA 1 113 #> 114 7 24.0 0 NA 36.0 NA 2 114 #> 115 7 36.0 0 NA 3.5 NA 1 115 #> 116 7 36.0 0 NA 33.0 NA 2 116 #> 117 7 48.0 0 NA 1.8 NA 1 117 #> 118 7 48.0 0 NA 28.0 NA 2 118 #> 119 7 72.0 0 NA 1.5 NA 1 119 #> 120 7 72.0 0 NA 52.0 NA 2 120 #> 121 7 96.0 0 NA 1.0 NA 1 121 #> 122 7 96.0 0 NA 80.0 NA 2 122 #> 123 7 120.0 0 NA 90.0 NA 2 123 #> 124 7 144.0 0 NA 100.0 NA 2 124 #> 125 8 0.0 1 135.0 NA 1 NA 125 #> 126 8 0.0 0 NA 88.0 NA 2 126 #> 127 8 2.0 0 NA 17.6 NA 1 127 #> 128 8 3.0 0 NA 17.3 NA 1 128 #> 129 8 6.0 0 NA 15.0 NA 1 129 #> 130 8 9.0 0 NA 15.0 NA 1 130 #> 131 8 12.0 0 NA 12.4 NA 1 131 #> 132 8 24.0 0 NA 7.9 NA 1 132 #> 133 8 24.0 0 NA 35.0 NA 2 133 #> 134 8 36.0 0 NA 7.9 NA 1 134 #> 135 8 36.0 0 NA 20.0 NA 2 135 #> 136 8 48.0 0 NA 5.1 NA 1 136 #> 137 8 48.0 0 NA 12.0 NA 2 137 #> 138 8 72.0 0 NA 3.6 NA 1 138 #> 139 8 72.0 0 NA 16.0 NA 2 139 #> 140 8 96.0 0 NA 2.4 NA 1 140 #> 141 8 96.0 0 NA 23.0 NA 2 141 #> 142 8 120.0 0 NA 2.0 NA 1 142 #> 143 8 120.0 0 NA 36.0 NA 2 143 #> 144 8 144.0 0 NA 48.0 NA 2 144 #> 145 9 0.0 1 75.0 NA 1 NA 145 #> 146 9 0.0 0 NA 92.0 NA 2 146 #> 147 9 0.5 0 NA 0.0 NA 1 147 #> 148 9 1.0 0 NA 1.0 NA 1 148 #> 149 9 2.0 0 NA 4.6 NA 1 149 #> 150 9 3.0 0 NA 12.7 NA 1 150 #> 151 9 3.0 0 NA 8.0 NA 1 151 #> 152 9 6.0 0 NA 12.7 NA 1 152 #> 153 9 6.0 0 NA 11.5 NA 1 153 #> 154 9 9.0 0 NA 12.9 NA 1 154 #> 155 9 9.0 0 NA 11.4 NA 1 155 #> 156 9 12.0 0 NA 11.4 NA 1 156 #> 157 9 12.0 0 NA 11.0 NA 1 157 #> 158 9 24.0 0 NA 9.1 NA 1 158 #> 159 9 24.0 0 NA 33.0 NA 2 159 #> 160 9 36.0 0 NA 8.2 NA 1 160 #> 161 9 36.0 0 NA 22.0 NA 2 161 #> 162 9 48.0 0 NA 5.9 NA 1 162 #> 163 9 48.0 0 NA 16.0 NA 2 163 #> 164 9 72.0 0 NA 3.6 NA 1 164 #> 165 9 72.0 0 NA 18.0 NA 2 165 #> 166 9 96.0 0 NA 1.7 NA 1 166 #> 167 9 96.0 0 NA 32.0 NA 2 167 #> 168 9 120.0 0 NA 1.1 NA 1 168 #> 169 9 120.0 0 NA 30.0 NA 2 169 #> 170 9 144.0 0 NA 45.0 NA 2 170 #> 171 10 0.0 1 105.0 NA 1 NA 171 #> 172 10 0.0 0 NA 90.0 NA 2 172 #> 173 10 24.0 0 NA 8.6 NA 1 173 #> 174 10 24.0 0 NA 39.0 NA 2 174 #> 175 10 36.0 0 NA 8.0 NA 1 175 #> 176 10 36.0 0 NA 22.0 NA 2 176 #> 177 10 48.0 0 NA 6.0 NA 1 177 #> 178 10 48.0 0 NA 17.0 NA 2 178 #> 179 10 72.0 0 NA 4.4 NA 1 179 #> 180 10 72.0 0 NA 17.0 NA 2 180 #> 181 10 96.0 0 NA 3.6 NA 1 181 #> 182 10 96.0 0 NA 22.0 NA 2 182 #> 183 10 120.0 0 NA 2.8 NA 1 183 #> 184 10 120.0 0 NA 25.0 NA 2 184 #> 185 10 144.0 0 NA 33.0 NA 2 185 #> 186 11 0.0 1 123.0 NA 1 NA 186 #> 187 11 0.0 0 NA 100.0 NA 2 187 #> 188 11 1.5 0 NA 11.4 NA 1 188 #> 189 11 3.0 0 NA 15.4 NA 1 189 #> 190 11 6.0 0 NA 17.5 NA 1 190 #> 191 11 12.0 0 NA 14.0 NA 1 191 #> 192 11 24.0 0 NA 9.0 NA 1 192 #> 193 11 24.0 0 NA 37.0 NA 2 193 #> 194 11 36.0 0 NA 8.9 NA 1 194 #> 195 11 36.0 0 NA 24.0 NA 2 195 #> 196 11 48.0 0 NA 6.6 NA 1 196 #> 197 11 48.0 0 NA 14.0 NA 2 197 #> 198 11 72.0 0 NA 4.2 NA 1 198 #> 199 11 72.0 0 NA 11.0 NA 2 199 #> 200 11 96.0 0 NA 3.6 NA 1 200 #> 201 11 96.0 0 NA 14.0 NA 2 201 #> 202 11 120.0 0 NA 2.6 NA 1 202 #> 203 11 120.0 0 NA 23.0 NA 2 203 #> 204 11 144.0 0 NA 33.0 NA 2 204 #> 205 12 0.0 1 113.0 NA 1 NA 205 #> 206 12 0.0 0 NA 85.0 NA 2 206 #> 207 12 1.5 0 NA 0.6 NA 1 207 #> 208 12 3.0 0 NA 2.8 NA 1 208 #> 209 12 6.0 0 NA 13.8 NA 1 209 #> 210 12 9.0 0 NA 15.0 NA 1 210 #> 211 12 24.0 0 NA 10.5 NA 1 211 #> 212 12 24.0 0 NA 25.0 NA 2 212 #> 213 12 36.0 0 NA 9.1 NA 1 213 #> 214 12 36.0 0 NA 15.0 NA 2 214 #> 215 12 48.0 0 NA 6.6 NA 1 215 #> 216 12 48.0 0 NA 11.0 NA 2 216 #> 217 12 72.0 0 NA 4.9 NA 1 217 #> 218 12 96.0 0 NA 2.4 NA 1 218 #> 219 12 120.0 0 NA 1.9 NA 1 219 #> 220 13 0.0 1 113.0 NA 1 NA 220 #> 221 13 0.0 0 NA 88.0 NA 2 221 #> 222 13 1.5 0 NA 3.6 NA 1 222 #> 223 13 3.0 0 NA 12.9 NA 1 223 #> 224 13 6.0 0 NA 12.9 NA 1 224 #> 225 13 9.0 0 NA 10.2 NA 1 225 #> 226 13 24.0 0 NA 6.4 NA 1 226 #> 227 13 24.0 0 NA 41.0 NA 2 227 #> 228 13 36.0 0 NA 6.9 NA 1 228 #> 229 13 36.0 0 NA 23.0 NA 2 229 #> 230 13 48.0 0 NA 4.5 NA 1 230 #> 231 13 48.0 0 NA 16.0 NA 2 231 #> 232 13 72.0 0 NA 3.2 NA 1 232 #> 233 13 72.0 0 NA 14.0 NA 2 233 #> 234 13 96.0 0 NA 2.4 NA 1 234 #> 235 13 96.0 0 NA 18.0 NA 2 235 #> 236 13 120.0 0 NA 1.3 NA 1 236 #> 237 13 120.0 0 NA 22.0 NA 2 237 #> 238 13 144.0 0 NA 35.0 NA 2 238 #> 239 14 0.0 1 75.0 NA 1 NA 239 #> 240 14 0.0 0 NA 85.0 NA 2 240 #> 241 14 0.5 0 NA 0.0 NA 1 241 #> 242 14 1.0 0 NA 2.7 NA 1 242 #> 243 14 2.0 0 NA 11.6 NA 1 243 #> 244 14 3.0 0 NA 11.6 NA 1 244 #> 245 14 6.0 0 NA 11.3 NA 1 245 #> 246 14 9.0 0 NA 9.7 NA 1 246 #> 247 14 24.0 0 NA 6.5 NA 1 247 #> 248 14 24.0 0 NA 32.0 NA 2 248 #> 249 14 36.0 0 NA 5.2 NA 1 249 #> 250 14 36.0 0 NA 22.0 NA 2 250 #> 251 14 48.0 0 NA 3.6 NA 1 251 #> 252 14 48.0 0 NA 21.0 NA 2 252 #> 253 14 72.0 0 NA 2.4 NA 1 253 #> 254 14 72.0 0 NA 28.0 NA 2 254 #> 255 14 96.0 0 NA 0.9 NA 1 255 #> 256 14 96.0 0 NA 38.0 NA 2 256 #> 257 14 120.0 0 NA 46.0 NA 2 257 #> 258 14 144.0 0 NA 65.0 NA 2 258 #> 259 15 0.0 1 85.0 NA 1 NA 259 #> 260 15 0.0 0 NA 100.0 NA 2 260 #> 261 15 1.0 0 NA 6.6 NA 1 261 #> 262 15 3.0 0 NA 11.9 NA 1 262 #> 263 15 6.0 0 NA 11.7 NA 1 263 #> 264 15 9.0 0 NA 12.2 NA 1 264 #> 265 15 24.0 0 NA 8.1 NA 1 265 #> 266 15 24.0 0 NA 43.0 NA 2 266 #> 267 15 36.0 0 NA 7.4 NA 1 267 #> 268 15 36.0 0 NA 26.0 NA 2 268 #> 269 15 48.0 0 NA 6.8 NA 1 269 #> 270 15 48.0 0 NA 15.0 NA 2 270 #> 271 15 72.0 0 NA 5.3 NA 1 271 #> 272 15 72.0 0 NA 13.0 NA 2 272 #> 273 15 96.0 0 NA 3.0 NA 1 273 #> 274 15 96.0 0 NA 21.0 NA 2 274 #> 275 15 120.0 0 NA 2.0 NA 1 275 #> 276 15 120.0 0 NA 28.0 NA 2 276 #> 277 15 144.0 0 NA 39.0 NA 2 277 #> 278 16 0.0 1 87.0 NA 1 NA 278 #> 279 16 0.0 0 NA 100.0 NA 2 279 #> 280 16 24.0 0 NA 10.4 NA 1 280 #> 281 16 24.0 0 NA 42.0 NA 2 281 #> 282 16 36.0 0 NA 8.9 NA 1 282 #> 283 16 36.0 0 NA 32.0 NA 2 283 #> 284 16 48.0 0 NA 7.0 NA 1 284 #> 285 16 48.0 0 NA 26.0 NA 2 285 #> 286 16 72.0 0 NA 4.4 NA 1 286 #> 287 16 72.0 0 NA 31.0 NA 2 287 #> 288 16 96.0 0 NA 3.2 NA 1 288 #> 289 16 96.0 0 NA 33.0 NA 2 289 #> 290 16 120.0 0 NA 2.4 NA 1 290 #> 291 16 120.0 0 NA 54.0 NA 2 291 #> 292 17 0.0 1 117.0 NA 1 NA 292 #> 293 17 0.0 0 NA 100.0 NA 2 293 #> 294 17 24.0 0 NA 7.6 NA 1 294 #> 295 17 24.0 0 NA 35.0 NA 2 295 #> 296 17 36.0 0 NA 6.4 NA 1 296 #> 297 17 36.0 0 NA 23.0 NA 2 297 #> 298 17 48.0 0 NA 6.0 NA 1 298 #> 299 17 48.0 0 NA 17.0 NA 2 299 #> 300 17 72.0 0 NA 4.0 NA 1 300 #> 301 17 72.0 0 NA 18.0 NA 2 301 #> 302 17 96.0 0 NA 3.1 NA 1 302 #> 303 17 96.0 0 NA 18.0 NA 2 303 #> 304 17 120.0 0 NA 2.0 NA 1 304 #> 305 17 120.0 0 NA 21.0 NA 2 305 #> 306 18 0.0 1 112.0 NA 1 NA 306 #> 307 18 0.0 0 NA 100.0 NA 2 307 #> 308 18 24.0 0 NA 7.6 NA 1 308 #> 309 18 24.0 0 NA 32.0 NA 2 309 #> 310 18 36.0 0 NA 6.6 NA 1 310 #> 311 18 36.0 0 NA 20.0 NA 2 311 #> 312 18 48.0 0 NA 5.4 NA 1 312 #> 313 18 48.0 0 NA 18.0 NA 2 313 #> 314 18 72.0 0 NA 3.4 NA 1 314 #> 315 18 72.0 0 NA 18.0 NA 2 315 #> 316 18 96.0 0 NA 1.2 NA 1 316 #> 317 18 96.0 0 NA 19.0 NA 2 317 #> 318 18 120.0 0 NA 0.9 NA 1 318 #> 319 18 120.0 0 NA 29.0 NA 2 319 #> 320 19 0.0 1 95.5 NA 1 NA 320 #> 321 19 0.0 0 NA 100.0 NA 2 321 #> 322 19 24.0 0 NA 6.6 NA 1 322 #> 323 19 24.0 0 NA 33.0 NA 2 323 #> 324 19 36.0 0 NA 5.3 NA 1 324 #> 325 19 36.0 0 NA 28.0 NA 2 325 #> 326 19 48.0 0 NA 3.6 NA 1 326 #> 327 19 48.0 0 NA 18.0 NA 2 327 #> 328 19 72.0 0 NA 2.7 NA 1 328 #> 329 19 72.0 0 NA 18.0 NA 2 329 #> 330 19 96.0 0 NA 1.4 NA 1 330 #> 331 19 96.0 0 NA 17.0 NA 2 331 #> 332 19 120.0 0 NA 1.1 NA 1 332 #> 333 19 120.0 0 NA 26.0 NA 2 333 #> 334 20 0.0 1 88.5 NA 1 NA 334 #> 335 20 0.0 0 NA 100.0 NA 2 335 #> 336 20 24.0 0 NA 9.6 NA 1 336 #> 337 20 24.0 0 NA 41.0 NA 2 337 #> 338 20 36.0 0 NA 8.0 NA 1 338 #> 339 20 36.0 0 NA 30.0 NA 2 339 #> 340 20 48.0 0 NA 6.6 NA 1 340 #> 341 20 48.0 0 NA 22.0 NA 2 341 #> 342 20 72.0 0 NA 5.6 NA 1 342 #> 343 20 72.0 0 NA 23.0 NA 2 343 #> 344 20 96.0 0 NA 3.5 NA 1 344 #> 345 20 96.0 0 NA 23.0 NA 2 345 #> 346 20 120.0 0 NA 2.3 NA 1 346 #> 347 20 120.0 0 NA 35.0 NA 2 347 #> 348 21 0.0 1 93.0 NA 1 NA 348 #> 349 21 0.0 0 NA 100.0 NA 2 349 #> 350 21 24.0 0 NA 7.3 NA 1 350 #> 351 21 24.0 0 NA 46.0 NA 2 351 #> 352 21 36.0 0 NA 6.1 NA 1 352 #> 353 21 36.0 0 NA 27.0 NA 2 353 #> 354 21 48.0 0 NA 4.3 NA 1 354 #> 355 21 48.0 0 NA 22.0 NA 2 355 #> 356 21 72.0 0 NA 3.2 NA 1 356 #> 357 21 72.0 0 NA 36.0 NA 2 357 #> 358 21 96.0 0 NA 2.3 NA 1 358 #> 359 21 96.0 0 NA 40.0 NA 2 359 #> 360 21 120.0 0 NA 1.9 NA 1 360 #> 361 21 120.0 0 NA 44.0 NA 2 361 #> 362 22 0.0 1 87.0 NA 1 NA 362 #> 363 22 0.0 0 NA 100.0 NA 2 363 #> 364 22 24.0 0 NA 8.9 NA 1 364 #> 365 22 24.0 0 NA 35.0 NA 2 365 #> 366 22 36.0 0 NA 8.4 NA 1 366 #> 367 22 36.0 0 NA 27.0 NA 2 367 #> 368 22 48.0 0 NA 8.0 NA 1 368 #> 369 22 48.0 0 NA 23.0 NA 2 369 #> 370 22 72.0 0 NA 4.4 NA 1 370 #> 371 22 72.0 0 NA 27.0 NA 2 371 #> 372 22 96.0 0 NA 3.2 NA 1 372 #> 373 22 96.0 0 NA 43.0 NA 2 373 #> 374 22 120.0 0 NA 1.7 NA 1 374 #> 375 22 120.0 0 NA 43.0 NA 2 375 #> 376 23 0.0 1 110.0 NA 1 NA 376 #> 377 23 0.0 0 NA 100.0 NA 2 377 #> 378 23 24.0 0 NA 9.8 NA 1 378 #> 379 23 24.0 0 NA 34.0 NA 2 379 #> 380 23 36.0 0 NA 8.4 NA 1 380 #> 381 23 36.0 0 NA 24.0 NA 2 381 #> 382 23 48.0 0 NA 6.6 NA 1 382 #> 383 23 48.0 0 NA 15.0 NA 2 383 #> 384 23 72.0 0 NA 4.8 NA 1 384 #> 385 23 72.0 0 NA 15.0 NA 2 385 #> 386 23 96.0 0 NA 3.2 NA 1 386 #> 387 23 96.0 0 NA 19.0 NA 2 387 #> 388 23 120.0 0 NA 2.4 NA 1 388 #> 389 23 120.0 0 NA 19.0 NA 2 389 #> 390 24 0.0 1 115.0 NA 1 NA 390 #> 391 24 0.0 0 NA 88.0 NA 2 391 #> 392 24 24.0 0 NA 8.2 NA 1 392 #> 393 24 24.0 0 NA 37.0 NA 2 393 #> 394 24 36.0 0 NA 7.5 NA 1 394 #> 395 24 36.0 0 NA 20.0 NA 2 395 #> 396 24 48.0 0 NA 6.8 NA 1 396 #> 397 24 48.0 0 NA 20.0 NA 2 397 #> 398 24 72.0 0 NA 5.5 NA 1 398 #> 399 24 72.0 0 NA 26.0 NA 2 399 #> 400 24 96.0 0 NA 4.5 NA 1 400 #> 401 24 96.0 0 NA 28.0 NA 2 401 #> 402 24 120.0 0 NA 3.7 NA 1 402 #> 403 24 120.0 0 NA 50.0 NA 2 403 #> 404 25 0.0 1 112.0 NA 1 NA 404 #> 405 25 0.0 0 NA 100.0 NA 2 405 #> 406 25 24.0 0 NA 11.0 NA 1 406 #> 407 25 24.0 0 NA 32.0 NA 2 407 #> 408 25 36.0 0 NA 10.0 NA 1 408 #> 409 25 36.0 0 NA 20.0 NA 2 409 #> 410 25 48.0 0 NA 8.2 NA 1 410 #> 411 25 48.0 0 NA 17.0 NA 2 411 #> 412 25 72.0 0 NA 6.0 NA 1 412 #> 413 25 72.0 0 NA 19.0 NA 2 413 #> 414 25 96.0 0 NA 3.7 NA 1 414 #> 415 25 96.0 0 NA 21.0 NA 2 415 #> 416 25 120.0 0 NA 2.6 NA 1 416 #> 417 25 120.0 0 NA 30.0 NA 2 417 #> 418 26 0.0 1 120.0 NA 1 NA 418 #> 419 26 0.0 0 NA 100.0 NA 2 419 #> 420 26 24.0 0 NA 10.0 NA 1 420 #> 421 26 24.0 0 NA 41.0 NA 2 421 #> 422 26 36.0 0 NA 9.0 NA 1 422 #> 423 26 36.0 0 NA 28.0 NA 2 423 #> 424 26 48.0 0 NA 7.3 NA 1 424 #> 425 26 48.0 0 NA 19.0 NA 2 425 #> 426 26 72.0 0 NA 5.2 NA 1 426 #> 427 26 72.0 0 NA 17.0 NA 2 427 #> 428 26 96.0 0 NA 3.7 NA 1 428 #> 429 26 96.0 0 NA 17.0 NA 2 429 #> 430 26 120.0 0 NA 2.7 NA 1 430 #> 431 26 120.0 0 NA 24.0 NA 2 431 #> 432 27 0.0 1 120.0 NA 1 NA 432 #> 433 27 0.0 0 NA 100.0 NA 2 433 #> 434 27 24.0 0 NA 11.8 NA 1 434 #> 435 27 24.0 0 NA 32.0 NA 2 435 #> 436 27 36.0 0 NA 9.2 NA 1 436 #> 437 27 36.0 0 NA 21.0 NA 2 437 #> 438 27 48.0 0 NA 7.7 NA 1 438 #> 439 27 48.0 0 NA 19.0 NA 2 439 #> 440 27 72.0 0 NA 4.9 NA 1 440 #> 441 27 72.0 0 NA 22.0 NA 2 441 #> 442 27 96.0 0 NA 3.4 NA 1 442 #> 443 27 96.0 0 NA 33.0 NA 2 443 #> 444 27 120.0 0 NA 2.7 NA 1 444 #> 445 27 120.0 0 NA 46.0 NA 2 445 #> 446 28 0.0 1 120.0 NA 1 NA 446 #> 447 28 0.0 0 NA 100.0 NA 2 447 #> 448 28 24.0 0 NA 10.1 NA 1 448 #> 449 28 24.0 0 NA 39.0 NA 2 449 #> 450 28 36.0 0 NA 8.0 NA 1 450 #> 451 28 36.0 0 NA 25.0 NA 2 451 #> 452 28 48.0 0 NA 6.0 NA 1 452 #> 453 28 48.0 0 NA 16.0 NA 2 453 #> 454 28 72.0 0 NA 4.9 NA 1 454 #> 455 28 72.0 0 NA 14.0 NA 2 455 #> 456 28 96.0 0 NA 3.4 NA 1 456 #> 457 28 96.0 0 NA 15.0 NA 2 457 #> 458 28 120.0 0 NA 2.0 NA 1 458 #> 459 28 120.0 0 NA 20.0 NA 2 459 #> 460 29 0.0 1 153.0 NA 1 NA 460 #> 461 29 0.0 0 NA 86.0 NA 2 461 #> 462 29 24.0 0 NA 8.3 NA 1 462 #> 463 29 24.0 0 NA 35.0 NA 2 463 #> 464 29 36.0 0 NA 7.0 NA 1 464 #> 465 29 36.0 0 NA 21.0 NA 2 465 #> 466 29 48.0 0 NA 5.6 NA 1 466 #> 467 29 48.0 0 NA 18.0 NA 2 467 #> 468 29 72.0 0 NA 4.1 NA 1 468 #> 469 29 72.0 0 NA 20.0 NA 2 469 #> 470 29 96.0 0 NA 3.1 NA 1 470 #> 471 29 96.0 0 NA 29.0 NA 2 471 #> 472 29 120.0 0 NA 2.2 NA 1 472 #> 473 29 120.0 0 NA 41.0 NA 2 473 #> 474 30 0.0 1 105.0 NA 1 NA 474 #> 475 30 0.0 0 NA 100.0 NA 2 475 #> 476 30 24.0 0 NA 9.9 NA 1 476 #> 477 30 24.0 0 NA 45.0 NA 2 477 #> 478 30 36.0 0 NA 7.5 NA 1 478 #> 479 30 36.0 0 NA 24.0 NA 2 479 #> 480 30 48.0 0 NA 6.5 NA 1 480 #> 481 30 48.0 0 NA 23.0 NA 2 481 #> 482 30 72.0 0 NA 4.1 NA 1 482 #> 483 30 72.0 0 NA 26.0 NA 2 483 #> 484 30 96.0 0 NA 2.9 NA 1 484 #> 485 30 96.0 0 NA 28.0 NA 2 485 #> 486 30 120.0 0 NA 2.3 NA 1 486 #> 487 30 120.0 0 NA 39.0 NA 2 487 #> 488 31 0.0 1 125.0 NA 1 NA 488 #> 489 31 0.0 0 NA 100.0 NA 2 489 #> 490 31 24.0 0 NA 9.5 NA 1 490 #> 491 31 24.0 0 NA 45.0 NA 2 491 #> 492 31 36.0 0 NA 7.8 NA 1 492 #> 493 31 36.0 0 NA 30.0 NA 2 493 #> 494 31 48.0 0 NA 6.4 NA 1 494 #> 495 31 48.0 0 NA 24.0 NA 2 495 #> 496 31 72.0 0 NA 4.5 NA 1 496 #> 497 31 72.0 0 NA 22.0 NA 2 497 #> 498 31 96.0 0 NA 3.4 NA 1 498 #> 499 31 96.0 0 NA 28.0 NA 2 499 #> 500 31 120.0 0 NA 2.5 NA 1 500 #> 501 31 120.0 0 NA 42.0 NA 2 501 #> 502 32 0.0 1 93.0 NA 1 NA 502 #> 503 32 0.0 0 NA 100.0 NA 2 503 #> 504 32 24.0 0 NA 8.9 NA 1 504 #> 505 32 24.0 0 NA 36.0 NA 2 505 #> 506 32 36.0 0 NA 7.7 NA 1 506 #> 507 32 36.0 0 NA 27.0 NA 2 507 #> 508 32 48.0 0 NA 6.9 NA 1 508 #> 509 32 48.0 0 NA 24.0 NA 2 509 #> 510 32 72.0 0 NA 4.4 NA 1 510 #> 511 32 72.0 0 NA 23.0 NA 2 511 #> 512 32 96.0 0 NA 3.5 NA 1 512 #> 513 32 96.0 0 NA 20.0 NA 2 513 #> 514 32 120.0 0 NA 2.5 NA 1 514 #> 515 32 120.0 0 NA 22.0 NA 2 515 bblDatToMrgsolve(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ID TIME EVID AMT II DV CMT DVID SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 5 1 0 2 #> 3 1 1.0 0 NA 0 1.9 5 1 0 3 #> 4 1 2.0 0 NA 0 3.3 5 1 0 4 #> 5 1 3.0 0 NA 0 6.6 5 1 0 5 #> 6 1 6.0 0 NA 0 9.1 5 1 0 6 #> 7 1 9.0 0 NA 0 10.8 5 1 0 7 #> 8 1 12.0 0 NA 0 8.6 5 1 0 8 #> 9 1 24.0 0 NA 0 5.6 5 1 0 9 #> 10 1 24.0 0 NA 0 44.0 6 2 0 10 #> 11 1 36.0 0 NA 0 4.0 5 1 0 11 #> 12 1 36.0 0 NA 0 27.0 6 2 0 12 #> 13 1 48.0 0 NA 0 2.7 5 1 0 13 #> 14 1 48.0 0 NA 0 28.0 6 2 0 14 #> 15 1 72.0 0 NA 0 0.8 5 1 0 15 #> 16 1 72.0 0 NA 0 31.0 6 2 0 16 #> 17 1 96.0 0 NA 0 60.0 6 2 0 17 #> 18 1 120.0 0 NA 0 65.0 6 2 0 18 #> 19 1 144.0 0 NA 0 71.0 6 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 6 2 0 21 #> 22 2 24.0 0 NA 0 9.2 5 1 0 22 #> 23 2 24.0 0 NA 0 49.0 6 2 0 23 #> 24 2 36.0 0 NA 0 8.5 5 1 0 24 #> 25 2 36.0 0 NA 0 32.0 6 2 0 25 #> 26 2 48.0 0 NA 0 6.4 5 1 0 26 #> 27 2 48.0 0 NA 0 26.0 6 2 0 27 #> 28 2 72.0 0 NA 0 4.8 5 1 0 28 #> 29 2 72.0 0 NA 0 22.0 6 2 0 29 #> 30 2 96.0 0 NA 0 3.1 5 1 0 30 #> 31 2 96.0 0 NA 0 28.0 6 2 0 31 #> 32 2 120.0 0 NA 0 2.5 5 1 0 32 #> 33 2 120.0 0 NA 0 33.0 6 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 6 2 0 35 #> 36 3 0.5 0 NA 0 0.0 5 1 0 36 #> 37 3 2.0 0 NA 0 8.4 5 1 0 37 #> 38 3 3.0 0 NA 0 9.7 5 1 0 38 #> 39 3 6.0 0 NA 0 9.8 5 1 0 39 #> 40 3 12.0 0 NA 0 11.0 5 1 0 40 #> 41 3 24.0 0 NA 0 8.3 5 1 0 41 #> 42 3 24.0 0 NA 0 46.0 6 2 0 42 #> 43 3 36.0 0 NA 0 7.7 5 1 0 43 #> 44 3 36.0 0 NA 0 22.0 6 2 0 44 #> 45 3 48.0 0 NA 0 6.3 5 1 0 45 #> 46 3 48.0 0 NA 0 19.0 6 2 0 46 #> 47 3 72.0 0 NA 0 4.1 5 1 0 47 #> 48 3 72.0 0 NA 0 20.0 6 2 0 48 #> 49 3 96.0 0 NA 0 3.0 5 1 0 49 #> 50 3 96.0 0 NA 0 42.0 6 2 0 50 #> 51 3 120.0 0 NA 0 1.4 5 1 0 51 #> 52 3 120.0 0 NA 0 49.0 6 2 0 52 #> 53 3 144.0 0 NA 0 54.0 6 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 6 2 0 55 #> 56 4 3.0 0 NA 0 12.0 5 1 0 56 #> 57 4 6.0 0 NA 0 13.2 5 1 0 57 #> 58 4 9.0 0 NA 0 14.4 5 1 0 58 #> 59 4 24.0 0 NA 0 9.6 5 1 0 59 #> 60 4 24.0 0 NA 0 30.0 6 2 0 60 #> 61 4 36.0 0 NA 0 8.2 5 1 0 61 #> 62 4 36.0 0 NA 0 24.0 6 2 0 62 #> 63 4 48.0 0 NA 0 7.8 5 1 0 63 #> 64 4 48.0 0 NA 0 13.0 6 2 0 64 #> 65 4 72.0 0 NA 0 5.8 5 1 0 65 #> 66 4 72.0 0 NA 0 9.0 6 2 0 66 #> 67 4 96.0 0 NA 0 4.3 5 1 0 67 #> 68 4 96.0 0 NA 0 9.0 6 2 0 68 #> 69 4 120.0 0 NA 0 3.0 5 1 0 69 #> 70 4 120.0 0 NA 0 11.0 6 2 0 70 #> 71 4 144.0 0 NA 0 12.0 6 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 6 2 0 73 #> 74 5 3.0 0 NA 0 11.1 5 1 0 74 #> 75 5 6.0 0 NA 0 11.9 5 1 0 75 #> 76 5 9.0 0 NA 0 9.8 5 1 0 76 #> 77 5 12.0 0 NA 0 11.0 5 1 0 77 #> 78 5 24.0 0 NA 0 8.5 5 1 0 78 #> 79 5 24.0 0 NA 0 43.0 6 2 0 79 #> 80 5 36.0 0 NA 0 7.6 5 1 0 80 #> 81 5 36.0 0 NA 0 25.0 6 2 0 81 #> 82 5 48.0 0 NA 0 5.4 5 1 0 82 #> 83 5 48.0 0 NA 0 18.0 6 2 0 83 #> 84 5 72.0 0 NA 0 4.5 5 1 0 84 #> 85 5 72.0 0 NA 0 17.0 6 2 0 85 #> 86 5 96.0 0 NA 0 3.3 5 1 0 86 #> 87 5 96.0 0 NA 0 23.0 6 2 0 87 #> 88 5 120.0 0 NA 0 2.3 5 1 0 88 #> 89 5 120.0 0 NA 0 29.0 6 2 0 89 #> 90 5 144.0 0 NA 0 41.0 6 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 6 2 0 92 #> 93 6 6.0 0 NA 0 8.6 5 1 0 93 #> 94 6 12.0 0 NA 0 8.6 5 1 0 94 #> 95 6 24.0 0 NA 0 7.0 5 1 0 95 #> 96 6 24.0 0 NA 0 34.0 6 2 0 96 #> 97 6 36.0 0 NA 0 5.7 5 1 0 97 #> 98 6 36.0 0 NA 0 23.0 6 2 0 98 #> 99 6 48.0 0 NA 0 4.7 5 1 0 99 #> 100 6 48.0 0 NA 0 20.0 6 2 0 100 #> 101 6 72.0 0 NA 0 3.3 5 1 0 101 #> 102 6 72.0 0 NA 0 16.0 6 2 0 102 #> 103 6 96.0 0 NA 0 2.3 5 1 0 103 #> 104 6 96.0 0 NA 0 17.0 6 2 0 104 #> 105 6 120.0 0 NA 0 1.7 5 1 0 105 #> 106 6 120.0 0 NA 0 18.0 6 2 0 106 #> 107 6 144.0 0 NA 0 25.0 6 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 5 1 0 109 #> 110 7 6.0 0 NA 0 12.4 5 1 0 110 #> 111 7 9.0 0 NA 0 12.7 5 1 0 111 #> 112 7 12.0 0 NA 0 8.8 5 1 0 112 #> 113 7 24.0 0 NA 0 6.1 5 1 0 113 #> 114 7 24.0 0 NA 0 36.0 6 2 0 114 #> 115 7 36.0 0 NA 0 3.5 5 1 0 115 #> 116 7 36.0 0 NA 0 33.0 6 2 0 116 #> 117 7 48.0 0 NA 0 1.8 5 1 0 117 #> 118 7 48.0 0 NA 0 28.0 6 2 0 118 #> 119 7 72.0 0 NA 0 1.5 5 1 0 119 #> 120 7 72.0 0 NA 0 52.0 6 2 0 120 #> 121 7 96.0 0 NA 0 1.0 5 1 0 121 #> 122 7 96.0 0 NA 0 80.0 6 2 0 122 #> 123 7 120.0 0 NA 0 90.0 6 2 0 123 #> 124 7 144.0 0 NA 0 100.0 6 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 6 2 0 126 #> 127 8 2.0 0 NA 0 17.6 5 1 0 127 #> 128 8 3.0 0 NA 0 17.3 5 1 0 128 #> 129 8 6.0 0 NA 0 15.0 5 1 0 129 #> 130 8 9.0 0 NA 0 15.0 5 1 0 130 #> 131 8 12.0 0 NA 0 12.4 5 1 0 131 #> 132 8 24.0 0 NA 0 7.9 5 1 0 132 #> 133 8 24.0 0 NA 0 35.0 6 2 0 133 #> 134 8 36.0 0 NA 0 7.9 5 1 0 134 #> 135 8 36.0 0 NA 0 20.0 6 2 0 135 #> 136 8 48.0 0 NA 0 5.1 5 1 0 136 #> 137 8 48.0 0 NA 0 12.0 6 2 0 137 #> 138 8 72.0 0 NA 0 3.6 5 1 0 138 #> 139 8 72.0 0 NA 0 16.0 6 2 0 139 #> 140 8 96.0 0 NA 0 2.4 5 1 0 140 #> 141 8 96.0 0 NA 0 23.0 6 2 0 141 #> 142 8 120.0 0 NA 0 2.0 5 1 0 142 #> 143 8 120.0 0 NA 0 36.0 6 2 0 143 #> 144 8 144.0 0 NA 0 48.0 6 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 6 2 0 146 #> 147 9 0.5 0 NA 0 0.0 5 1 0 147 #> 148 9 1.0 0 NA 0 1.0 5 1 0 148 #> 149 9 2.0 0 NA 0 4.6 5 1 0 149 #> 150 9 3.0 0 NA 0 12.7 5 1 0 150 #> 151 9 3.0 0 NA 0 8.0 5 1 0 151 #> 152 9 6.0 0 NA 0 12.7 5 1 0 152 #> 153 9 6.0 0 NA 0 11.5 5 1 0 153 #> 154 9 9.0 0 NA 0 12.9 5 1 0 154 #> 155 9 9.0 0 NA 0 11.4 5 1 0 155 #> 156 9 12.0 0 NA 0 11.4 5 1 0 156 #> 157 9 12.0 0 NA 0 11.0 5 1 0 157 #> 158 9 24.0 0 NA 0 9.1 5 1 0 158 #> 159 9 24.0 0 NA 0 33.0 6 2 0 159 #> 160 9 36.0 0 NA 0 8.2 5 1 0 160 #> 161 9 36.0 0 NA 0 22.0 6 2 0 161 #> 162 9 48.0 0 NA 0 5.9 5 1 0 162 #> 163 9 48.0 0 NA 0 16.0 6 2 0 163 #> 164 9 72.0 0 NA 0 3.6 5 1 0 164 #> 165 9 72.0 0 NA 0 18.0 6 2 0 165 #> 166 9 96.0 0 NA 0 1.7 5 1 0 166 #> 167 9 96.0 0 NA 0 32.0 6 2 0 167 #> 168 9 120.0 0 NA 0 1.1 5 1 0 168 #> 169 9 120.0 0 NA 0 30.0 6 2 0 169 #> 170 9 144.0 0 NA 0 45.0 6 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 6 2 0 172 #> 173 10 24.0 0 NA 0 8.6 5 1 0 173 #> 174 10 24.0 0 NA 0 39.0 6 2 0 174 #> 175 10 36.0 0 NA 0 8.0 5 1 0 175 #> 176 10 36.0 0 NA 0 22.0 6 2 0 176 #> 177 10 48.0 0 NA 0 6.0 5 1 0 177 #> 178 10 48.0 0 NA 0 17.0 6 2 0 178 #> 179 10 72.0 0 NA 0 4.4 5 1 0 179 #> 180 10 72.0 0 NA 0 17.0 6 2 0 180 #> 181 10 96.0 0 NA 0 3.6 5 1 0 181 #> 182 10 96.0 0 NA 0 22.0 6 2 0 182 #> 183 10 120.0 0 NA 0 2.8 5 1 0 183 #> 184 10 120.0 0 NA 0 25.0 6 2 0 184 #> 185 10 144.0 0 NA 0 33.0 6 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 6 2 0 187 #> 188 11 1.5 0 NA 0 11.4 5 1 0 188 #> 189 11 3.0 0 NA 0 15.4 5 1 0 189 #> 190 11 6.0 0 NA 0 17.5 5 1 0 190 #> 191 11 12.0 0 NA 0 14.0 5 1 0 191 #> 192 11 24.0 0 NA 0 9.0 5 1 0 192 #> 193 11 24.0 0 NA 0 37.0 6 2 0 193 #> 194 11 36.0 0 NA 0 8.9 5 1 0 194 #> 195 11 36.0 0 NA 0 24.0 6 2 0 195 #> 196 11 48.0 0 NA 0 6.6 5 1 0 196 #> 197 11 48.0 0 NA 0 14.0 6 2 0 197 #> 198 11 72.0 0 NA 0 4.2 5 1 0 198 #> 199 11 72.0 0 NA 0 11.0 6 2 0 199 #> 200 11 96.0 0 NA 0 3.6 5 1 0 200 #> 201 11 96.0 0 NA 0 14.0 6 2 0 201 #> 202 11 120.0 0 NA 0 2.6 5 1 0 202 #> 203 11 120.0 0 NA 0 23.0 6 2 0 203 #> 204 11 144.0 0 NA 0 33.0 6 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 6 2 0 206 #> 207 12 1.5 0 NA 0 0.6 5 1 0 207 #> 208 12 3.0 0 NA 0 2.8 5 1 0 208 #> 209 12 6.0 0 NA 0 13.8 5 1 0 209 #> 210 12 9.0 0 NA 0 15.0 5 1 0 210 #> 211 12 24.0 0 NA 0 10.5 5 1 0 211 #> 212 12 24.0 0 NA 0 25.0 6 2 0 212 #> 213 12 36.0 0 NA 0 9.1 5 1 0 213 #> 214 12 36.0 0 NA 0 15.0 6 2 0 214 #> 215 12 48.0 0 NA 0 6.6 5 1 0 215 #> 216 12 48.0 0 NA 0 11.0 6 2 0 216 #> 217 12 72.0 0 NA 0 4.9 5 1 0 217 #> 218 12 96.0 0 NA 0 2.4 5 1 0 218 #> 219 12 120.0 0 NA 0 1.9 5 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 6 2 0 221 #> 222 13 1.5 0 NA 0 3.6 5 1 0 222 #> 223 13 3.0 0 NA 0 12.9 5 1 0 223 #> 224 13 6.0 0 NA 0 12.9 5 1 0 224 #> 225 13 9.0 0 NA 0 10.2 5 1 0 225 #> 226 13 24.0 0 NA 0 6.4 5 1 0 226 #> 227 13 24.0 0 NA 0 41.0 6 2 0 227 #> 228 13 36.0 0 NA 0 6.9 5 1 0 228 #> 229 13 36.0 0 NA 0 23.0 6 2 0 229 #> 230 13 48.0 0 NA 0 4.5 5 1 0 230 #> 231 13 48.0 0 NA 0 16.0 6 2 0 231 #> 232 13 72.0 0 NA 0 3.2 5 1 0 232 #> 233 13 72.0 0 NA 0 14.0 6 2 0 233 #> 234 13 96.0 0 NA 0 2.4 5 1 0 234 #> 235 13 96.0 0 NA 0 18.0 6 2 0 235 #> 236 13 120.0 0 NA 0 1.3 5 1 0 236 #> 237 13 120.0 0 NA 0 22.0 6 2 0 237 #> 238 13 144.0 0 NA 0 35.0 6 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 6 2 0 240 #> 241 14 0.5 0 NA 0 0.0 5 1 0 241 #> 242 14 1.0 0 NA 0 2.7 5 1 0 242 #> 243 14 2.0 0 NA 0 11.6 5 1 0 243 #> 244 14 3.0 0 NA 0 11.6 5 1 0 244 #> 245 14 6.0 0 NA 0 11.3 5 1 0 245 #> 246 14 9.0 0 NA 0 9.7 5 1 0 246 #> 247 14 24.0 0 NA 0 6.5 5 1 0 247 #> 248 14 24.0 0 NA 0 32.0 6 2 0 248 #> 249 14 36.0 0 NA 0 5.2 5 1 0 249 #> 250 14 36.0 0 NA 0 22.0 6 2 0 250 #> 251 14 48.0 0 NA 0 3.6 5 1 0 251 #> 252 14 48.0 0 NA 0 21.0 6 2 0 252 #> 253 14 72.0 0 NA 0 2.4 5 1 0 253 #> 254 14 72.0 0 NA 0 28.0 6 2 0 254 #> 255 14 96.0 0 NA 0 0.9 5 1 0 255 #> 256 14 96.0 0 NA 0 38.0 6 2 0 256 #> 257 14 120.0 0 NA 0 46.0 6 2 0 257 #> 258 14 144.0 0 NA 0 65.0 6 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 6 2 0 260 #> 261 15 1.0 0 NA 0 6.6 5 1 0 261 #> 262 15 3.0 0 NA 0 11.9 5 1 0 262 #> 263 15 6.0 0 NA 0 11.7 5 1 0 263 #> 264 15 9.0 0 NA 0 12.2 5 1 0 264 #> 265 15 24.0 0 NA 0 8.1 5 1 0 265 #> 266 15 24.0 0 NA 0 43.0 6 2 0 266 #> 267 15 36.0 0 NA 0 7.4 5 1 0 267 #> 268 15 36.0 0 NA 0 26.0 6 2 0 268 #> 269 15 48.0 0 NA 0 6.8 5 1 0 269 #> 270 15 48.0 0 NA 0 15.0 6 2 0 270 #> 271 15 72.0 0 NA 0 5.3 5 1 0 271 #> 272 15 72.0 0 NA 0 13.0 6 2 0 272 #> 273 15 96.0 0 NA 0 3.0 5 1 0 273 #> 274 15 96.0 0 NA 0 21.0 6 2 0 274 #> 275 15 120.0 0 NA 0 2.0 5 1 0 275 #> 276 15 120.0 0 NA 0 28.0 6 2 0 276 #> 277 15 144.0 0 NA 0 39.0 6 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 6 2 0 279 #> 280 16 24.0 0 NA 0 10.4 5 1 0 280 #> 281 16 24.0 0 NA 0 42.0 6 2 0 281 #> 282 16 36.0 0 NA 0 8.9 5 1 0 282 #> 283 16 36.0 0 NA 0 32.0 6 2 0 283 #> 284 16 48.0 0 NA 0 7.0 5 1 0 284 #> 285 16 48.0 0 NA 0 26.0 6 2 0 285 #> 286 16 72.0 0 NA 0 4.4 5 1 0 286 #> 287 16 72.0 0 NA 0 31.0 6 2 0 287 #> 288 16 96.0 0 NA 0 3.2 5 1 0 288 #> 289 16 96.0 0 NA 0 33.0 6 2 0 289 #> 290 16 120.0 0 NA 0 2.4 5 1 0 290 #> 291 16 120.0 0 NA 0 54.0 6 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 6 2 0 293 #> 294 17 24.0 0 NA 0 7.6 5 1 0 294 #> 295 17 24.0 0 NA 0 35.0 6 2 0 295 #> 296 17 36.0 0 NA 0 6.4 5 1 0 296 #> 297 17 36.0 0 NA 0 23.0 6 2 0 297 #> 298 17 48.0 0 NA 0 6.0 5 1 0 298 #> 299 17 48.0 0 NA 0 17.0 6 2 0 299 #> 300 17 72.0 0 NA 0 4.0 5 1 0 300 #> 301 17 72.0 0 NA 0 18.0 6 2 0 301 #> 302 17 96.0 0 NA 0 3.1 5 1 0 302 #> 303 17 96.0 0 NA 0 18.0 6 2 0 303 #> 304 17 120.0 0 NA 0 2.0 5 1 0 304 #> 305 17 120.0 0 NA 0 21.0 6 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 6 2 0 307 #> 308 18 24.0 0 NA 0 7.6 5 1 0 308 #> 309 18 24.0 0 NA 0 32.0 6 2 0 309 #> 310 18 36.0 0 NA 0 6.6 5 1 0 310 #> 311 18 36.0 0 NA 0 20.0 6 2 0 311 #> 312 18 48.0 0 NA 0 5.4 5 1 0 312 #> 313 18 48.0 0 NA 0 18.0 6 2 0 313 #> 314 18 72.0 0 NA 0 3.4 5 1 0 314 #> 315 18 72.0 0 NA 0 18.0 6 2 0 315 #> 316 18 96.0 0 NA 0 1.2 5 1 0 316 #> 317 18 96.0 0 NA 0 19.0 6 2 0 317 #> 318 18 120.0 0 NA 0 0.9 5 1 0 318 #> 319 18 120.0 0 NA 0 29.0 6 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 6 2 0 321 #> 322 19 24.0 0 NA 0 6.6 5 1 0 322 #> 323 19 24.0 0 NA 0 33.0 6 2 0 323 #> 324 19 36.0 0 NA 0 5.3 5 1 0 324 #> 325 19 36.0 0 NA 0 28.0 6 2 0 325 #> 326 19 48.0 0 NA 0 3.6 5 1 0 326 #> 327 19 48.0 0 NA 0 18.0 6 2 0 327 #> 328 19 72.0 0 NA 0 2.7 5 1 0 328 #> 329 19 72.0 0 NA 0 18.0 6 2 0 329 #> 330 19 96.0 0 NA 0 1.4 5 1 0 330 #> 331 19 96.0 0 NA 0 17.0 6 2 0 331 #> 332 19 120.0 0 NA 0 1.1 5 1 0 332 #> 333 19 120.0 0 NA 0 26.0 6 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 6 2 0 335 #> 336 20 24.0 0 NA 0 9.6 5 1 0 336 #> 337 20 24.0 0 NA 0 41.0 6 2 0 337 #> 338 20 36.0 0 NA 0 8.0 5 1 0 338 #> 339 20 36.0 0 NA 0 30.0 6 2 0 339 #> 340 20 48.0 0 NA 0 6.6 5 1 0 340 #> 341 20 48.0 0 NA 0 22.0 6 2 0 341 #> 342 20 72.0 0 NA 0 5.6 5 1 0 342 #> 343 20 72.0 0 NA 0 23.0 6 2 0 343 #> 344 20 96.0 0 NA 0 3.5 5 1 0 344 #> 345 20 96.0 0 NA 0 23.0 6 2 0 345 #> 346 20 120.0 0 NA 0 2.3 5 1 0 346 #> 347 20 120.0 0 NA 0 35.0 6 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 6 2 0 349 #> 350 21 24.0 0 NA 0 7.3 5 1 0 350 #> 351 21 24.0 0 NA 0 46.0 6 2 0 351 #> 352 21 36.0 0 NA 0 6.1 5 1 0 352 #> 353 21 36.0 0 NA 0 27.0 6 2 0 353 #> 354 21 48.0 0 NA 0 4.3 5 1 0 354 #> 355 21 48.0 0 NA 0 22.0 6 2 0 355 #> 356 21 72.0 0 NA 0 3.2 5 1 0 356 #> 357 21 72.0 0 NA 0 36.0 6 2 0 357 #> 358 21 96.0 0 NA 0 2.3 5 1 0 358 #> 359 21 96.0 0 NA 0 40.0 6 2 0 359 #> 360 21 120.0 0 NA 0 1.9 5 1 0 360 #> 361 21 120.0 0 NA 0 44.0 6 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 6 2 0 363 #> 364 22 24.0 0 NA 0 8.9 5 1 0 364 #> 365 22 24.0 0 NA 0 35.0 6 2 0 365 #> 366 22 36.0 0 NA 0 8.4 5 1 0 366 #> 367 22 36.0 0 NA 0 27.0 6 2 0 367 #> 368 22 48.0 0 NA 0 8.0 5 1 0 368 #> 369 22 48.0 0 NA 0 23.0 6 2 0 369 #> 370 22 72.0 0 NA 0 4.4 5 1 0 370 #> 371 22 72.0 0 NA 0 27.0 6 2 0 371 #> 372 22 96.0 0 NA 0 3.2 5 1 0 372 #> 373 22 96.0 0 NA 0 43.0 6 2 0 373 #> 374 22 120.0 0 NA 0 1.7 5 1 0 374 #> 375 22 120.0 0 NA 0 43.0 6 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 6 2 0 377 #> 378 23 24.0 0 NA 0 9.8 5 1 0 378 #> 379 23 24.0 0 NA 0 34.0 6 2 0 379 #> 380 23 36.0 0 NA 0 8.4 5 1 0 380 #> 381 23 36.0 0 NA 0 24.0 6 2 0 381 #> 382 23 48.0 0 NA 0 6.6 5 1 0 382 #> 383 23 48.0 0 NA 0 15.0 6 2 0 383 #> 384 23 72.0 0 NA 0 4.8 5 1 0 384 #> 385 23 72.0 0 NA 0 15.0 6 2 0 385 #> 386 23 96.0 0 NA 0 3.2 5 1 0 386 #> 387 23 96.0 0 NA 0 19.0 6 2 0 387 #> 388 23 120.0 0 NA 0 2.4 5 1 0 388 #> 389 23 120.0 0 NA 0 19.0 6 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 6 2 0 391 #> 392 24 24.0 0 NA 0 8.2 5 1 0 392 #> 393 24 24.0 0 NA 0 37.0 6 2 0 393 #> 394 24 36.0 0 NA 0 7.5 5 1 0 394 #> 395 24 36.0 0 NA 0 20.0 6 2 0 395 #> 396 24 48.0 0 NA 0 6.8 5 1 0 396 #> 397 24 48.0 0 NA 0 20.0 6 2 0 397 #> 398 24 72.0 0 NA 0 5.5 5 1 0 398 #> 399 24 72.0 0 NA 0 26.0 6 2 0 399 #> 400 24 96.0 0 NA 0 4.5 5 1 0 400 #> 401 24 96.0 0 NA 0 28.0 6 2 0 401 #> 402 24 120.0 0 NA 0 3.7 5 1 0 402 #> 403 24 120.0 0 NA 0 50.0 6 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 6 2 0 405 #> 406 25 24.0 0 NA 0 11.0 5 1 0 406 #> 407 25 24.0 0 NA 0 32.0 6 2 0 407 #> 408 25 36.0 0 NA 0 10.0 5 1 0 408 #> 409 25 36.0 0 NA 0 20.0 6 2 0 409 #> 410 25 48.0 0 NA 0 8.2 5 1 0 410 #> 411 25 48.0 0 NA 0 17.0 6 2 0 411 #> 412 25 72.0 0 NA 0 6.0 5 1 0 412 #> 413 25 72.0 0 NA 0 19.0 6 2 0 413 #> 414 25 96.0 0 NA 0 3.7 5 1 0 414 #> 415 25 96.0 0 NA 0 21.0 6 2 0 415 #> 416 25 120.0 0 NA 0 2.6 5 1 0 416 #> 417 25 120.0 0 NA 0 30.0 6 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 6 2 0 419 #> 420 26 24.0 0 NA 0 10.0 5 1 0 420 #> 421 26 24.0 0 NA 0 41.0 6 2 0 421 #> 422 26 36.0 0 NA 0 9.0 5 1 0 422 #> 423 26 36.0 0 NA 0 28.0 6 2 0 423 #> 424 26 48.0 0 NA 0 7.3 5 1 0 424 #> 425 26 48.0 0 NA 0 19.0 6 2 0 425 #> 426 26 72.0 0 NA 0 5.2 5 1 0 426 #> 427 26 72.0 0 NA 0 17.0 6 2 0 427 #> 428 26 96.0 0 NA 0 3.7 5 1 0 428 #> 429 26 96.0 0 NA 0 17.0 6 2 0 429 #> 430 26 120.0 0 NA 0 2.7 5 1 0 430 #> 431 26 120.0 0 NA 0 24.0 6 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 6 2 0 433 #> 434 27 24.0 0 NA 0 11.8 5 1 0 434 #> 435 27 24.0 0 NA 0 32.0 6 2 0 435 #> 436 27 36.0 0 NA 0 9.2 5 1 0 436 #> 437 27 36.0 0 NA 0 21.0 6 2 0 437 #> 438 27 48.0 0 NA 0 7.7 5 1 0 438 #> 439 27 48.0 0 NA 0 19.0 6 2 0 439 #> 440 27 72.0 0 NA 0 4.9 5 1 0 440 #> 441 27 72.0 0 NA 0 22.0 6 2 0 441 #> 442 27 96.0 0 NA 0 3.4 5 1 0 442 #> 443 27 96.0 0 NA 0 33.0 6 2 0 443 #> 444 27 120.0 0 NA 0 2.7 5 1 0 444 #> 445 27 120.0 0 NA 0 46.0 6 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 6 2 0 447 #> 448 28 24.0 0 NA 0 10.1 5 1 0 448 #> 449 28 24.0 0 NA 0 39.0 6 2 0 449 #> 450 28 36.0 0 NA 0 8.0 5 1 0 450 #> 451 28 36.0 0 NA 0 25.0 6 2 0 451 #> 452 28 48.0 0 NA 0 6.0 5 1 0 452 #> 453 28 48.0 0 NA 0 16.0 6 2 0 453 #> 454 28 72.0 0 NA 0 4.9 5 1 0 454 #> 455 28 72.0 0 NA 0 14.0 6 2 0 455 #> 456 28 96.0 0 NA 0 3.4 5 1 0 456 #> 457 28 96.0 0 NA 0 15.0 6 2 0 457 #> 458 28 120.0 0 NA 0 2.0 5 1 0 458 #> 459 28 120.0 0 NA 0 20.0 6 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 6 2 0 461 #> 462 29 24.0 0 NA 0 8.3 5 1 0 462 #> 463 29 24.0 0 NA 0 35.0 6 2 0 463 #> 464 29 36.0 0 NA 0 7.0 5 1 0 464 #> 465 29 36.0 0 NA 0 21.0 6 2 0 465 #> 466 29 48.0 0 NA 0 5.6 5 1 0 466 #> 467 29 48.0 0 NA 0 18.0 6 2 0 467 #> 468 29 72.0 0 NA 0 4.1 5 1 0 468 #> 469 29 72.0 0 NA 0 20.0 6 2 0 469 #> 470 29 96.0 0 NA 0 3.1 5 1 0 470 #> 471 29 96.0 0 NA 0 29.0 6 2 0 471 #> 472 29 120.0 0 NA 0 2.2 5 1 0 472 #> 473 29 120.0 0 NA 0 41.0 6 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 6 2 0 475 #> 476 30 24.0 0 NA 0 9.9 5 1 0 476 #> 477 30 24.0 0 NA 0 45.0 6 2 0 477 #> 478 30 36.0 0 NA 0 7.5 5 1 0 478 #> 479 30 36.0 0 NA 0 24.0 6 2 0 479 #> 480 30 48.0 0 NA 0 6.5 5 1 0 480 #> 481 30 48.0 0 NA 0 23.0 6 2 0 481 #> 482 30 72.0 0 NA 0 4.1 5 1 0 482 #> 483 30 72.0 0 NA 0 26.0 6 2 0 483 #> 484 30 96.0 0 NA 0 2.9 5 1 0 484 #> 485 30 96.0 0 NA 0 28.0 6 2 0 485 #> 486 30 120.0 0 NA 0 2.3 5 1 0 486 #> 487 30 120.0 0 NA 0 39.0 6 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 6 2 0 489 #> 490 31 24.0 0 NA 0 9.5 5 1 0 490 #> 491 31 24.0 0 NA 0 45.0 6 2 0 491 #> 492 31 36.0 0 NA 0 7.8 5 1 0 492 #> 493 31 36.0 0 NA 0 30.0 6 2 0 493 #> 494 31 48.0 0 NA 0 6.4 5 1 0 494 #> 495 31 48.0 0 NA 0 24.0 6 2 0 495 #> 496 31 72.0 0 NA 0 4.5 5 1 0 496 #> 497 31 72.0 0 NA 0 22.0 6 2 0 497 #> 498 31 96.0 0 NA 0 3.4 5 1 0 498 #> 499 31 96.0 0 NA 0 28.0 6 2 0 499 #> 500 31 120.0 0 NA 0 2.5 5 1 0 500 #> 501 31 120.0 0 NA 0 42.0 6 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 6 2 0 503 #> 504 32 24.0 0 NA 0 8.9 5 1 0 504 #> 505 32 24.0 0 NA 0 36.0 6 2 0 505 #> 506 32 36.0 0 NA 0 7.7 5 1 0 506 #> 507 32 36.0 0 NA 0 27.0 6 2 0 507 #> 508 32 48.0 0 NA 0 6.9 5 1 0 508 #> 509 32 48.0 0 NA 0 24.0 6 2 0 509 #> 510 32 72.0 0 NA 0 4.4 5 1 0 510 #> 511 32 72.0 0 NA 0 23.0 6 2 0 511 #> 512 32 96.0 0 NA 0 3.5 5 1 0 512 #> 513 32 96.0 0 NA 0 20.0 6 2 0 513 #> 514 32 120.0 0 NA 0 2.5 5 1 0 514 #> 515 32 120.0 0 NA 0 22.0 6 2 0 515 bblDatToRxode(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ID TIME EVID AMT II DV CMT DVID SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 5 1 0 2 #> 3 1 1.0 0 NA 0 1.9 5 1 0 3 #> 4 1 2.0 0 NA 0 3.3 5 1 0 4 #> 5 1 3.0 0 NA 0 6.6 5 1 0 5 #> 6 1 6.0 0 NA 0 9.1 5 1 0 6 #> 7 1 9.0 0 NA 0 10.8 5 1 0 7 #> 8 1 12.0 0 NA 0 8.6 5 1 0 8 #> 9 1 24.0 0 NA 0 5.6 5 1 0 9 #> 10 1 24.0 0 NA 0 44.0 6 2 0 10 #> 11 1 36.0 0 NA 0 4.0 5 1 0 11 #> 12 1 36.0 0 NA 0 27.0 6 2 0 12 #> 13 1 48.0 0 NA 0 2.7 5 1 0 13 #> 14 1 48.0 0 NA 0 28.0 6 2 0 14 #> 15 1 72.0 0 NA 0 0.8 5 1 0 15 #> 16 1 72.0 0 NA 0 31.0 6 2 0 16 #> 17 1 96.0 0 NA 0 60.0 6 2 0 17 #> 18 1 120.0 0 NA 0 65.0 6 2 0 18 #> 19 1 144.0 0 NA 0 71.0 6 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 6 2 0 21 #> 22 2 24.0 0 NA 0 9.2 5 1 0 22 #> 23 2 24.0 0 NA 0 49.0 6 2 0 23 #> 24 2 36.0 0 NA 0 8.5 5 1 0 24 #> 25 2 36.0 0 NA 0 32.0 6 2 0 25 #> 26 2 48.0 0 NA 0 6.4 5 1 0 26 #> 27 2 48.0 0 NA 0 26.0 6 2 0 27 #> 28 2 72.0 0 NA 0 4.8 5 1 0 28 #> 29 2 72.0 0 NA 0 22.0 6 2 0 29 #> 30 2 96.0 0 NA 0 3.1 5 1 0 30 #> 31 2 96.0 0 NA 0 28.0 6 2 0 31 #> 32 2 120.0 0 NA 0 2.5 5 1 0 32 #> 33 2 120.0 0 NA 0 33.0 6 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 6 2 0 35 #> 36 3 0.5 0 NA 0 0.0 5 1 0 36 #> 37 3 2.0 0 NA 0 8.4 5 1 0 37 #> 38 3 3.0 0 NA 0 9.7 5 1 0 38 #> 39 3 6.0 0 NA 0 9.8 5 1 0 39 #> 40 3 12.0 0 NA 0 11.0 5 1 0 40 #> 41 3 24.0 0 NA 0 8.3 5 1 0 41 #> 42 3 24.0 0 NA 0 46.0 6 2 0 42 #> 43 3 36.0 0 NA 0 7.7 5 1 0 43 #> 44 3 36.0 0 NA 0 22.0 6 2 0 44 #> 45 3 48.0 0 NA 0 6.3 5 1 0 45 #> 46 3 48.0 0 NA 0 19.0 6 2 0 46 #> 47 3 72.0 0 NA 0 4.1 5 1 0 47 #> 48 3 72.0 0 NA 0 20.0 6 2 0 48 #> 49 3 96.0 0 NA 0 3.0 5 1 0 49 #> 50 3 96.0 0 NA 0 42.0 6 2 0 50 #> 51 3 120.0 0 NA 0 1.4 5 1 0 51 #> 52 3 120.0 0 NA 0 49.0 6 2 0 52 #> 53 3 144.0 0 NA 0 54.0 6 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 6 2 0 55 #> 56 4 3.0 0 NA 0 12.0 5 1 0 56 #> 57 4 6.0 0 NA 0 13.2 5 1 0 57 #> 58 4 9.0 0 NA 0 14.4 5 1 0 58 #> 59 4 24.0 0 NA 0 9.6 5 1 0 59 #> 60 4 24.0 0 NA 0 30.0 6 2 0 60 #> 61 4 36.0 0 NA 0 8.2 5 1 0 61 #> 62 4 36.0 0 NA 0 24.0 6 2 0 62 #> 63 4 48.0 0 NA 0 7.8 5 1 0 63 #> 64 4 48.0 0 NA 0 13.0 6 2 0 64 #> 65 4 72.0 0 NA 0 5.8 5 1 0 65 #> 66 4 72.0 0 NA 0 9.0 6 2 0 66 #> 67 4 96.0 0 NA 0 4.3 5 1 0 67 #> 68 4 96.0 0 NA 0 9.0 6 2 0 68 #> 69 4 120.0 0 NA 0 3.0 5 1 0 69 #> 70 4 120.0 0 NA 0 11.0 6 2 0 70 #> 71 4 144.0 0 NA 0 12.0 6 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 6 2 0 73 #> 74 5 3.0 0 NA 0 11.1 5 1 0 74 #> 75 5 6.0 0 NA 0 11.9 5 1 0 75 #> 76 5 9.0 0 NA 0 9.8 5 1 0 76 #> 77 5 12.0 0 NA 0 11.0 5 1 0 77 #> 78 5 24.0 0 NA 0 8.5 5 1 0 78 #> 79 5 24.0 0 NA 0 43.0 6 2 0 79 #> 80 5 36.0 0 NA 0 7.6 5 1 0 80 #> 81 5 36.0 0 NA 0 25.0 6 2 0 81 #> 82 5 48.0 0 NA 0 5.4 5 1 0 82 #> 83 5 48.0 0 NA 0 18.0 6 2 0 83 #> 84 5 72.0 0 NA 0 4.5 5 1 0 84 #> 85 5 72.0 0 NA 0 17.0 6 2 0 85 #> 86 5 96.0 0 NA 0 3.3 5 1 0 86 #> 87 5 96.0 0 NA 0 23.0 6 2 0 87 #> 88 5 120.0 0 NA 0 2.3 5 1 0 88 #> 89 5 120.0 0 NA 0 29.0 6 2 0 89 #> 90 5 144.0 0 NA 0 41.0 6 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 6 2 0 92 #> 93 6 6.0 0 NA 0 8.6 5 1 0 93 #> 94 6 12.0 0 NA 0 8.6 5 1 0 94 #> 95 6 24.0 0 NA 0 7.0 5 1 0 95 #> 96 6 24.0 0 NA 0 34.0 6 2 0 96 #> 97 6 36.0 0 NA 0 5.7 5 1 0 97 #> 98 6 36.0 0 NA 0 23.0 6 2 0 98 #> 99 6 48.0 0 NA 0 4.7 5 1 0 99 #> 100 6 48.0 0 NA 0 20.0 6 2 0 100 #> 101 6 72.0 0 NA 0 3.3 5 1 0 101 #> 102 6 72.0 0 NA 0 16.0 6 2 0 102 #> 103 6 96.0 0 NA 0 2.3 5 1 0 103 #> 104 6 96.0 0 NA 0 17.0 6 2 0 104 #> 105 6 120.0 0 NA 0 1.7 5 1 0 105 #> 106 6 120.0 0 NA 0 18.0 6 2 0 106 #> 107 6 144.0 0 NA 0 25.0 6 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 5 1 0 109 #> 110 7 6.0 0 NA 0 12.4 5 1 0 110 #> 111 7 9.0 0 NA 0 12.7 5 1 0 111 #> 112 7 12.0 0 NA 0 8.8 5 1 0 112 #> 113 7 24.0 0 NA 0 6.1 5 1 0 113 #> 114 7 24.0 0 NA 0 36.0 6 2 0 114 #> 115 7 36.0 0 NA 0 3.5 5 1 0 115 #> 116 7 36.0 0 NA 0 33.0 6 2 0 116 #> 117 7 48.0 0 NA 0 1.8 5 1 0 117 #> 118 7 48.0 0 NA 0 28.0 6 2 0 118 #> 119 7 72.0 0 NA 0 1.5 5 1 0 119 #> 120 7 72.0 0 NA 0 52.0 6 2 0 120 #> 121 7 96.0 0 NA 0 1.0 5 1 0 121 #> 122 7 96.0 0 NA 0 80.0 6 2 0 122 #> 123 7 120.0 0 NA 0 90.0 6 2 0 123 #> 124 7 144.0 0 NA 0 100.0 6 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 6 2 0 126 #> 127 8 2.0 0 NA 0 17.6 5 1 0 127 #> 128 8 3.0 0 NA 0 17.3 5 1 0 128 #> 129 8 6.0 0 NA 0 15.0 5 1 0 129 #> 130 8 9.0 0 NA 0 15.0 5 1 0 130 #> 131 8 12.0 0 NA 0 12.4 5 1 0 131 #> 132 8 24.0 0 NA 0 7.9 5 1 0 132 #> 133 8 24.0 0 NA 0 35.0 6 2 0 133 #> 134 8 36.0 0 NA 0 7.9 5 1 0 134 #> 135 8 36.0 0 NA 0 20.0 6 2 0 135 #> 136 8 48.0 0 NA 0 5.1 5 1 0 136 #> 137 8 48.0 0 NA 0 12.0 6 2 0 137 #> 138 8 72.0 0 NA 0 3.6 5 1 0 138 #> 139 8 72.0 0 NA 0 16.0 6 2 0 139 #> 140 8 96.0 0 NA 0 2.4 5 1 0 140 #> 141 8 96.0 0 NA 0 23.0 6 2 0 141 #> 142 8 120.0 0 NA 0 2.0 5 1 0 142 #> 143 8 120.0 0 NA 0 36.0 6 2 0 143 #> 144 8 144.0 0 NA 0 48.0 6 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 6 2 0 146 #> 147 9 0.5 0 NA 0 0.0 5 1 0 147 #> 148 9 1.0 0 NA 0 1.0 5 1 0 148 #> 149 9 2.0 0 NA 0 4.6 5 1 0 149 #> 150 9 3.0 0 NA 0 12.7 5 1 0 150 #> 151 9 3.0 0 NA 0 8.0 5 1 0 151 #> 152 9 6.0 0 NA 0 12.7 5 1 0 152 #> 153 9 6.0 0 NA 0 11.5 5 1 0 153 #> 154 9 9.0 0 NA 0 12.9 5 1 0 154 #> 155 9 9.0 0 NA 0 11.4 5 1 0 155 #> 156 9 12.0 0 NA 0 11.4 5 1 0 156 #> 157 9 12.0 0 NA 0 11.0 5 1 0 157 #> 158 9 24.0 0 NA 0 9.1 5 1 0 158 #> 159 9 24.0 0 NA 0 33.0 6 2 0 159 #> 160 9 36.0 0 NA 0 8.2 5 1 0 160 #> 161 9 36.0 0 NA 0 22.0 6 2 0 161 #> 162 9 48.0 0 NA 0 5.9 5 1 0 162 #> 163 9 48.0 0 NA 0 16.0 6 2 0 163 #> 164 9 72.0 0 NA 0 3.6 5 1 0 164 #> 165 9 72.0 0 NA 0 18.0 6 2 0 165 #> 166 9 96.0 0 NA 0 1.7 5 1 0 166 #> 167 9 96.0 0 NA 0 32.0 6 2 0 167 #> 168 9 120.0 0 NA 0 1.1 5 1 0 168 #> 169 9 120.0 0 NA 0 30.0 6 2 0 169 #> 170 9 144.0 0 NA 0 45.0 6 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 6 2 0 172 #> 173 10 24.0 0 NA 0 8.6 5 1 0 173 #> 174 10 24.0 0 NA 0 39.0 6 2 0 174 #> 175 10 36.0 0 NA 0 8.0 5 1 0 175 #> 176 10 36.0 0 NA 0 22.0 6 2 0 176 #> 177 10 48.0 0 NA 0 6.0 5 1 0 177 #> 178 10 48.0 0 NA 0 17.0 6 2 0 178 #> 179 10 72.0 0 NA 0 4.4 5 1 0 179 #> 180 10 72.0 0 NA 0 17.0 6 2 0 180 #> 181 10 96.0 0 NA 0 3.6 5 1 0 181 #> 182 10 96.0 0 NA 0 22.0 6 2 0 182 #> 183 10 120.0 0 NA 0 2.8 5 1 0 183 #> 184 10 120.0 0 NA 0 25.0 6 2 0 184 #> 185 10 144.0 0 NA 0 33.0 6 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 6 2 0 187 #> 188 11 1.5 0 NA 0 11.4 5 1 0 188 #> 189 11 3.0 0 NA 0 15.4 5 1 0 189 #> 190 11 6.0 0 NA 0 17.5 5 1 0 190 #> 191 11 12.0 0 NA 0 14.0 5 1 0 191 #> 192 11 24.0 0 NA 0 9.0 5 1 0 192 #> 193 11 24.0 0 NA 0 37.0 6 2 0 193 #> 194 11 36.0 0 NA 0 8.9 5 1 0 194 #> 195 11 36.0 0 NA 0 24.0 6 2 0 195 #> 196 11 48.0 0 NA 0 6.6 5 1 0 196 #> 197 11 48.0 0 NA 0 14.0 6 2 0 197 #> 198 11 72.0 0 NA 0 4.2 5 1 0 198 #> 199 11 72.0 0 NA 0 11.0 6 2 0 199 #> 200 11 96.0 0 NA 0 3.6 5 1 0 200 #> 201 11 96.0 0 NA 0 14.0 6 2 0 201 #> 202 11 120.0 0 NA 0 2.6 5 1 0 202 #> 203 11 120.0 0 NA 0 23.0 6 2 0 203 #> 204 11 144.0 0 NA 0 33.0 6 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 6 2 0 206 #> 207 12 1.5 0 NA 0 0.6 5 1 0 207 #> 208 12 3.0 0 NA 0 2.8 5 1 0 208 #> 209 12 6.0 0 NA 0 13.8 5 1 0 209 #> 210 12 9.0 0 NA 0 15.0 5 1 0 210 #> 211 12 24.0 0 NA 0 10.5 5 1 0 211 #> 212 12 24.0 0 NA 0 25.0 6 2 0 212 #> 213 12 36.0 0 NA 0 9.1 5 1 0 213 #> 214 12 36.0 0 NA 0 15.0 6 2 0 214 #> 215 12 48.0 0 NA 0 6.6 5 1 0 215 #> 216 12 48.0 0 NA 0 11.0 6 2 0 216 #> 217 12 72.0 0 NA 0 4.9 5 1 0 217 #> 218 12 96.0 0 NA 0 2.4 5 1 0 218 #> 219 12 120.0 0 NA 0 1.9 5 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 6 2 0 221 #> 222 13 1.5 0 NA 0 3.6 5 1 0 222 #> 223 13 3.0 0 NA 0 12.9 5 1 0 223 #> 224 13 6.0 0 NA 0 12.9 5 1 0 224 #> 225 13 9.0 0 NA 0 10.2 5 1 0 225 #> 226 13 24.0 0 NA 0 6.4 5 1 0 226 #> 227 13 24.0 0 NA 0 41.0 6 2 0 227 #> 228 13 36.0 0 NA 0 6.9 5 1 0 228 #> 229 13 36.0 0 NA 0 23.0 6 2 0 229 #> 230 13 48.0 0 NA 0 4.5 5 1 0 230 #> 231 13 48.0 0 NA 0 16.0 6 2 0 231 #> 232 13 72.0 0 NA 0 3.2 5 1 0 232 #> 233 13 72.0 0 NA 0 14.0 6 2 0 233 #> 234 13 96.0 0 NA 0 2.4 5 1 0 234 #> 235 13 96.0 0 NA 0 18.0 6 2 0 235 #> 236 13 120.0 0 NA 0 1.3 5 1 0 236 #> 237 13 120.0 0 NA 0 22.0 6 2 0 237 #> 238 13 144.0 0 NA 0 35.0 6 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 6 2 0 240 #> 241 14 0.5 0 NA 0 0.0 5 1 0 241 #> 242 14 1.0 0 NA 0 2.7 5 1 0 242 #> 243 14 2.0 0 NA 0 11.6 5 1 0 243 #> 244 14 3.0 0 NA 0 11.6 5 1 0 244 #> 245 14 6.0 0 NA 0 11.3 5 1 0 245 #> 246 14 9.0 0 NA 0 9.7 5 1 0 246 #> 247 14 24.0 0 NA 0 6.5 5 1 0 247 #> 248 14 24.0 0 NA 0 32.0 6 2 0 248 #> 249 14 36.0 0 NA 0 5.2 5 1 0 249 #> 250 14 36.0 0 NA 0 22.0 6 2 0 250 #> 251 14 48.0 0 NA 0 3.6 5 1 0 251 #> 252 14 48.0 0 NA 0 21.0 6 2 0 252 #> 253 14 72.0 0 NA 0 2.4 5 1 0 253 #> 254 14 72.0 0 NA 0 28.0 6 2 0 254 #> 255 14 96.0 0 NA 0 0.9 5 1 0 255 #> 256 14 96.0 0 NA 0 38.0 6 2 0 256 #> 257 14 120.0 0 NA 0 46.0 6 2 0 257 #> 258 14 144.0 0 NA 0 65.0 6 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 6 2 0 260 #> 261 15 1.0 0 NA 0 6.6 5 1 0 261 #> 262 15 3.0 0 NA 0 11.9 5 1 0 262 #> 263 15 6.0 0 NA 0 11.7 5 1 0 263 #> 264 15 9.0 0 NA 0 12.2 5 1 0 264 #> 265 15 24.0 0 NA 0 8.1 5 1 0 265 #> 266 15 24.0 0 NA 0 43.0 6 2 0 266 #> 267 15 36.0 0 NA 0 7.4 5 1 0 267 #> 268 15 36.0 0 NA 0 26.0 6 2 0 268 #> 269 15 48.0 0 NA 0 6.8 5 1 0 269 #> 270 15 48.0 0 NA 0 15.0 6 2 0 270 #> 271 15 72.0 0 NA 0 5.3 5 1 0 271 #> 272 15 72.0 0 NA 0 13.0 6 2 0 272 #> 273 15 96.0 0 NA 0 3.0 5 1 0 273 #> 274 15 96.0 0 NA 0 21.0 6 2 0 274 #> 275 15 120.0 0 NA 0 2.0 5 1 0 275 #> 276 15 120.0 0 NA 0 28.0 6 2 0 276 #> 277 15 144.0 0 NA 0 39.0 6 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 6 2 0 279 #> 280 16 24.0 0 NA 0 10.4 5 1 0 280 #> 281 16 24.0 0 NA 0 42.0 6 2 0 281 #> 282 16 36.0 0 NA 0 8.9 5 1 0 282 #> 283 16 36.0 0 NA 0 32.0 6 2 0 283 #> 284 16 48.0 0 NA 0 7.0 5 1 0 284 #> 285 16 48.0 0 NA 0 26.0 6 2 0 285 #> 286 16 72.0 0 NA 0 4.4 5 1 0 286 #> 287 16 72.0 0 NA 0 31.0 6 2 0 287 #> 288 16 96.0 0 NA 0 3.2 5 1 0 288 #> 289 16 96.0 0 NA 0 33.0 6 2 0 289 #> 290 16 120.0 0 NA 0 2.4 5 1 0 290 #> 291 16 120.0 0 NA 0 54.0 6 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 6 2 0 293 #> 294 17 24.0 0 NA 0 7.6 5 1 0 294 #> 295 17 24.0 0 NA 0 35.0 6 2 0 295 #> 296 17 36.0 0 NA 0 6.4 5 1 0 296 #> 297 17 36.0 0 NA 0 23.0 6 2 0 297 #> 298 17 48.0 0 NA 0 6.0 5 1 0 298 #> 299 17 48.0 0 NA 0 17.0 6 2 0 299 #> 300 17 72.0 0 NA 0 4.0 5 1 0 300 #> 301 17 72.0 0 NA 0 18.0 6 2 0 301 #> 302 17 96.0 0 NA 0 3.1 5 1 0 302 #> 303 17 96.0 0 NA 0 18.0 6 2 0 303 #> 304 17 120.0 0 NA 0 2.0 5 1 0 304 #> 305 17 120.0 0 NA 0 21.0 6 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 6 2 0 307 #> 308 18 24.0 0 NA 0 7.6 5 1 0 308 #> 309 18 24.0 0 NA 0 32.0 6 2 0 309 #> 310 18 36.0 0 NA 0 6.6 5 1 0 310 #> 311 18 36.0 0 NA 0 20.0 6 2 0 311 #> 312 18 48.0 0 NA 0 5.4 5 1 0 312 #> 313 18 48.0 0 NA 0 18.0 6 2 0 313 #> 314 18 72.0 0 NA 0 3.4 5 1 0 314 #> 315 18 72.0 0 NA 0 18.0 6 2 0 315 #> 316 18 96.0 0 NA 0 1.2 5 1 0 316 #> 317 18 96.0 0 NA 0 19.0 6 2 0 317 #> 318 18 120.0 0 NA 0 0.9 5 1 0 318 #> 319 18 120.0 0 NA 0 29.0 6 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 6 2 0 321 #> 322 19 24.0 0 NA 0 6.6 5 1 0 322 #> 323 19 24.0 0 NA 0 33.0 6 2 0 323 #> 324 19 36.0 0 NA 0 5.3 5 1 0 324 #> 325 19 36.0 0 NA 0 28.0 6 2 0 325 #> 326 19 48.0 0 NA 0 3.6 5 1 0 326 #> 327 19 48.0 0 NA 0 18.0 6 2 0 327 #> 328 19 72.0 0 NA 0 2.7 5 1 0 328 #> 329 19 72.0 0 NA 0 18.0 6 2 0 329 #> 330 19 96.0 0 NA 0 1.4 5 1 0 330 #> 331 19 96.0 0 NA 0 17.0 6 2 0 331 #> 332 19 120.0 0 NA 0 1.1 5 1 0 332 #> 333 19 120.0 0 NA 0 26.0 6 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 6 2 0 335 #> 336 20 24.0 0 NA 0 9.6 5 1 0 336 #> 337 20 24.0 0 NA 0 41.0 6 2 0 337 #> 338 20 36.0 0 NA 0 8.0 5 1 0 338 #> 339 20 36.0 0 NA 0 30.0 6 2 0 339 #> 340 20 48.0 0 NA 0 6.6 5 1 0 340 #> 341 20 48.0 0 NA 0 22.0 6 2 0 341 #> 342 20 72.0 0 NA 0 5.6 5 1 0 342 #> 343 20 72.0 0 NA 0 23.0 6 2 0 343 #> 344 20 96.0 0 NA 0 3.5 5 1 0 344 #> 345 20 96.0 0 NA 0 23.0 6 2 0 345 #> 346 20 120.0 0 NA 0 2.3 5 1 0 346 #> 347 20 120.0 0 NA 0 35.0 6 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 6 2 0 349 #> 350 21 24.0 0 NA 0 7.3 5 1 0 350 #> 351 21 24.0 0 NA 0 46.0 6 2 0 351 #> 352 21 36.0 0 NA 0 6.1 5 1 0 352 #> 353 21 36.0 0 NA 0 27.0 6 2 0 353 #> 354 21 48.0 0 NA 0 4.3 5 1 0 354 #> 355 21 48.0 0 NA 0 22.0 6 2 0 355 #> 356 21 72.0 0 NA 0 3.2 5 1 0 356 #> 357 21 72.0 0 NA 0 36.0 6 2 0 357 #> 358 21 96.0 0 NA 0 2.3 5 1 0 358 #> 359 21 96.0 0 NA 0 40.0 6 2 0 359 #> 360 21 120.0 0 NA 0 1.9 5 1 0 360 #> 361 21 120.0 0 NA 0 44.0 6 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 6 2 0 363 #> 364 22 24.0 0 NA 0 8.9 5 1 0 364 #> 365 22 24.0 0 NA 0 35.0 6 2 0 365 #> 366 22 36.0 0 NA 0 8.4 5 1 0 366 #> 367 22 36.0 0 NA 0 27.0 6 2 0 367 #> 368 22 48.0 0 NA 0 8.0 5 1 0 368 #> 369 22 48.0 0 NA 0 23.0 6 2 0 369 #> 370 22 72.0 0 NA 0 4.4 5 1 0 370 #> 371 22 72.0 0 NA 0 27.0 6 2 0 371 #> 372 22 96.0 0 NA 0 3.2 5 1 0 372 #> 373 22 96.0 0 NA 0 43.0 6 2 0 373 #> 374 22 120.0 0 NA 0 1.7 5 1 0 374 #> 375 22 120.0 0 NA 0 43.0 6 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 6 2 0 377 #> 378 23 24.0 0 NA 0 9.8 5 1 0 378 #> 379 23 24.0 0 NA 0 34.0 6 2 0 379 #> 380 23 36.0 0 NA 0 8.4 5 1 0 380 #> 381 23 36.0 0 NA 0 24.0 6 2 0 381 #> 382 23 48.0 0 NA 0 6.6 5 1 0 382 #> 383 23 48.0 0 NA 0 15.0 6 2 0 383 #> 384 23 72.0 0 NA 0 4.8 5 1 0 384 #> 385 23 72.0 0 NA 0 15.0 6 2 0 385 #> 386 23 96.0 0 NA 0 3.2 5 1 0 386 #> 387 23 96.0 0 NA 0 19.0 6 2 0 387 #> 388 23 120.0 0 NA 0 2.4 5 1 0 388 #> 389 23 120.0 0 NA 0 19.0 6 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 6 2 0 391 #> 392 24 24.0 0 NA 0 8.2 5 1 0 392 #> 393 24 24.0 0 NA 0 37.0 6 2 0 393 #> 394 24 36.0 0 NA 0 7.5 5 1 0 394 #> 395 24 36.0 0 NA 0 20.0 6 2 0 395 #> 396 24 48.0 0 NA 0 6.8 5 1 0 396 #> 397 24 48.0 0 NA 0 20.0 6 2 0 397 #> 398 24 72.0 0 NA 0 5.5 5 1 0 398 #> 399 24 72.0 0 NA 0 26.0 6 2 0 399 #> 400 24 96.0 0 NA 0 4.5 5 1 0 400 #> 401 24 96.0 0 NA 0 28.0 6 2 0 401 #> 402 24 120.0 0 NA 0 3.7 5 1 0 402 #> 403 24 120.0 0 NA 0 50.0 6 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 6 2 0 405 #> 406 25 24.0 0 NA 0 11.0 5 1 0 406 #> 407 25 24.0 0 NA 0 32.0 6 2 0 407 #> 408 25 36.0 0 NA 0 10.0 5 1 0 408 #> 409 25 36.0 0 NA 0 20.0 6 2 0 409 #> 410 25 48.0 0 NA 0 8.2 5 1 0 410 #> 411 25 48.0 0 NA 0 17.0 6 2 0 411 #> 412 25 72.0 0 NA 0 6.0 5 1 0 412 #> 413 25 72.0 0 NA 0 19.0 6 2 0 413 #> 414 25 96.0 0 NA 0 3.7 5 1 0 414 #> 415 25 96.0 0 NA 0 21.0 6 2 0 415 #> 416 25 120.0 0 NA 0 2.6 5 1 0 416 #> 417 25 120.0 0 NA 0 30.0 6 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 6 2 0 419 #> 420 26 24.0 0 NA 0 10.0 5 1 0 420 #> 421 26 24.0 0 NA 0 41.0 6 2 0 421 #> 422 26 36.0 0 NA 0 9.0 5 1 0 422 #> 423 26 36.0 0 NA 0 28.0 6 2 0 423 #> 424 26 48.0 0 NA 0 7.3 5 1 0 424 #> 425 26 48.0 0 NA 0 19.0 6 2 0 425 #> 426 26 72.0 0 NA 0 5.2 5 1 0 426 #> 427 26 72.0 0 NA 0 17.0 6 2 0 427 #> 428 26 96.0 0 NA 0 3.7 5 1 0 428 #> 429 26 96.0 0 NA 0 17.0 6 2 0 429 #> 430 26 120.0 0 NA 0 2.7 5 1 0 430 #> 431 26 120.0 0 NA 0 24.0 6 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 6 2 0 433 #> 434 27 24.0 0 NA 0 11.8 5 1 0 434 #> 435 27 24.0 0 NA 0 32.0 6 2 0 435 #> 436 27 36.0 0 NA 0 9.2 5 1 0 436 #> 437 27 36.0 0 NA 0 21.0 6 2 0 437 #> 438 27 48.0 0 NA 0 7.7 5 1 0 438 #> 439 27 48.0 0 NA 0 19.0 6 2 0 439 #> 440 27 72.0 0 NA 0 4.9 5 1 0 440 #> 441 27 72.0 0 NA 0 22.0 6 2 0 441 #> 442 27 96.0 0 NA 0 3.4 5 1 0 442 #> 443 27 96.0 0 NA 0 33.0 6 2 0 443 #> 444 27 120.0 0 NA 0 2.7 5 1 0 444 #> 445 27 120.0 0 NA 0 46.0 6 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 6 2 0 447 #> 448 28 24.0 0 NA 0 10.1 5 1 0 448 #> 449 28 24.0 0 NA 0 39.0 6 2 0 449 #> 450 28 36.0 0 NA 0 8.0 5 1 0 450 #> 451 28 36.0 0 NA 0 25.0 6 2 0 451 #> 452 28 48.0 0 NA 0 6.0 5 1 0 452 #> 453 28 48.0 0 NA 0 16.0 6 2 0 453 #> 454 28 72.0 0 NA 0 4.9 5 1 0 454 #> 455 28 72.0 0 NA 0 14.0 6 2 0 455 #> 456 28 96.0 0 NA 0 3.4 5 1 0 456 #> 457 28 96.0 0 NA 0 15.0 6 2 0 457 #> 458 28 120.0 0 NA 0 2.0 5 1 0 458 #> 459 28 120.0 0 NA 0 20.0 6 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 6 2 0 461 #> 462 29 24.0 0 NA 0 8.3 5 1 0 462 #> 463 29 24.0 0 NA 0 35.0 6 2 0 463 #> 464 29 36.0 0 NA 0 7.0 5 1 0 464 #> 465 29 36.0 0 NA 0 21.0 6 2 0 465 #> 466 29 48.0 0 NA 0 5.6 5 1 0 466 #> 467 29 48.0 0 NA 0 18.0 6 2 0 467 #> 468 29 72.0 0 NA 0 4.1 5 1 0 468 #> 469 29 72.0 0 NA 0 20.0 6 2 0 469 #> 470 29 96.0 0 NA 0 3.1 5 1 0 470 #> 471 29 96.0 0 NA 0 29.0 6 2 0 471 #> 472 29 120.0 0 NA 0 2.2 5 1 0 472 #> 473 29 120.0 0 NA 0 41.0 6 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 6 2 0 475 #> 476 30 24.0 0 NA 0 9.9 5 1 0 476 #> 477 30 24.0 0 NA 0 45.0 6 2 0 477 #> 478 30 36.0 0 NA 0 7.5 5 1 0 478 #> 479 30 36.0 0 NA 0 24.0 6 2 0 479 #> 480 30 48.0 0 NA 0 6.5 5 1 0 480 #> 481 30 48.0 0 NA 0 23.0 6 2 0 481 #> 482 30 72.0 0 NA 0 4.1 5 1 0 482 #> 483 30 72.0 0 NA 0 26.0 6 2 0 483 #> 484 30 96.0 0 NA 0 2.9 5 1 0 484 #> 485 30 96.0 0 NA 0 28.0 6 2 0 485 #> 486 30 120.0 0 NA 0 2.3 5 1 0 486 #> 487 30 120.0 0 NA 0 39.0 6 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 6 2 0 489 #> 490 31 24.0 0 NA 0 9.5 5 1 0 490 #> 491 31 24.0 0 NA 0 45.0 6 2 0 491 #> 492 31 36.0 0 NA 0 7.8 5 1 0 492 #> 493 31 36.0 0 NA 0 30.0 6 2 0 493 #> 494 31 48.0 0 NA 0 6.4 5 1 0 494 #> 495 31 48.0 0 NA 0 24.0 6 2 0 495 #> 496 31 72.0 0 NA 0 4.5 5 1 0 496 #> 497 31 72.0 0 NA 0 22.0 6 2 0 497 #> 498 31 96.0 0 NA 0 3.4 5 1 0 498 #> 499 31 96.0 0 NA 0 28.0 6 2 0 499 #> 500 31 120.0 0 NA 0 2.5 5 1 0 500 #> 501 31 120.0 0 NA 0 42.0 6 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 6 2 0 503 #> 504 32 24.0 0 NA 0 8.9 5 1 0 504 #> 505 32 24.0 0 NA 0 36.0 6 2 0 505 #> 506 32 36.0 0 NA 0 7.7 5 1 0 506 #> 507 32 36.0 0 NA 0 27.0 6 2 0 507 #> 508 32 48.0 0 NA 0 6.9 5 1 0 508 #> 509 32 48.0 0 NA 0 24.0 6 2 0 509 #> 510 32 72.0 0 NA 0 4.4 5 1 0 510 #> 511 32 72.0 0 NA 0 23.0 6 2 0 511 #> 512 32 96.0 0 NA 0 3.5 5 1 0 512 #> 513 32 96.0 0 NA 0 20.0 6 2 0 513 #> 514 32 120.0 0 NA 0 2.5 5 1 0 514 #> 515 32 120.0 0 NA 0 22.0 6 2 0 515"},{"path":"/reference/dot-popedCluster.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","title":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","text":"Internal function use PopED run PopED parallel Windows","code":""},{"path":"/reference/dot-popedCluster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","text":"","code":".popedCluster(babelmixr2)"},{"path":"/reference/dot-popedCluster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","text":"babelmixr2 environment poped environment","code":""},{"path":"/reference/dot-popedCluster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","text":"nothing, called side effects","code":""},{"path":"/reference/dot-popedCluster.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedF.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the function value from the rxode2 solve — .popedF","title":"Get the function value from the rxode2 solve — .popedF","text":"called directly","code":""},{"path":"/reference/dot-popedF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the function value from the rxode2 solve — .popedF","text":"","code":".popedF(popedDb)"},{"path":"/reference/dot-popedF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the function value from the rxode2 solve — .popedF","text":"popedDb poped DB babelmixr2 issue","code":""},{"path":"/reference/dot-popedF.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the function value from the rxode2 solve — .popedF","text":"rxode2 weights poped error function","code":""},{"path":"/reference/dot-popedF.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the function value from the rxode2 solve — .popedF","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedFree.html","id":null,"dir":"Reference","previous_headings":"","what":"Free Poped memory (if any is allocated) — .popedFree","title":"Free Poped memory (if any is allocated) — .popedFree","text":"called directly used babelmixr2's poped interface","code":""},{"path":"/reference/dot-popedFree.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Free Poped memory (if any is allocated) — .popedFree","text":"","code":".popedFree()"},{"path":"/reference/dot-popedFree.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Free Poped memory (if any is allocated) — .popedFree","text":"nothing, called side effects","code":""},{"path":"/reference/dot-popedFree.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Free Poped memory (if any is allocated) — .popedFree","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedRxRunSetup.html","id":null,"dir":"Reference","previous_headings":"","what":"Setup poped if needed — .popedRxRunSetup","title":"Setup poped if needed — .popedRxRunSetup","text":"called user","code":""},{"path":"/reference/dot-popedRxRunSetup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Setup poped if needed — .popedRxRunSetup","text":"","code":".popedRxRunSetup(popedDb)"},{"path":"/reference/dot-popedRxRunSetup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Setup poped if needed — .popedRxRunSetup","text":"nothing, called side effects","code":""},{"path":"/reference/dot-popedRxRunSetup.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Setup poped if needed — .popedRxRunSetup","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedSetup.html","id":null,"dir":"Reference","previous_headings":"","what":"Setup the PopED environment — .popedSetup","title":"Setup the PopED environment — .popedSetup","text":"typically called directly","code":""},{"path":"/reference/dot-popedSetup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Setup the PopED environment — .popedSetup","text":"","code":".popedSetup(e, eglobal, full = FALSE)"},{"path":"/reference/dot-popedSetup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Setup the PopED environment — .popedSetup","text":"e environment setup information popEd eglobal global environment poped info full setup full model","code":""},{"path":"/reference/dot-popedSetup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Setup the PopED environment — .popedSetup","text":"nothing, called side effects","code":""},{"path":"/reference/dot-popedSetup.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Setup the PopED environment — .popedSetup","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedSolveIdME.html","id":null,"dir":"Reference","previous_headings":"","what":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","title":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","text":"really called directly (setup correctly can crash R)","code":""},{"path":"/reference/dot-popedSolveIdME.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","text":"","code":".popedSolveIdME(theta, umt, mt, ms, nend, id, totn) .popedSolveIdME2(theta, umt, mt, ms, nend, id, totn)"},{"path":"/reference/dot-popedSolveIdME.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","text":"theta parameters (includes covariates modeling times) umt unique times sampled mt original unsorted time (match f/w ) ms model switch parameter integer starting 1 (related dvid rxode2) nend specifies number endpoints model id design identifier totn total number design points tested","code":""},{"path":"/reference/dot-popedSolveIdME.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","text":"data frame $f $w corresponding function value standard deviation sampling point","code":""},{"path":"/reference/dot-popedSolveIdME.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedW.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the weight from the rxode2 solve — .popedW","title":"Get the weight from the rxode2 solve — .popedW","text":"called directly","code":""},{"path":"/reference/dot-popedW.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the weight from the rxode2 solve — .popedW","text":"","code":".popedW(popedDb)"},{"path":"/reference/dot-popedW.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the weight from the rxode2 solve — .popedW","text":"popedDb poped DB babelmixr2 issue","code":""},{"path":"/reference/dot-popedW.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the weight from the rxode2 solve — .popedW","text":"rxode2 weights poped error function","code":""},{"path":"/reference/dot-popedW.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the weight from the rxode2 solve — .popedW","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-setupPopEDdatabase.html","id":null,"dir":"Reference","previous_headings":"","what":"Setup the poped database — .setupPopEDdatabase","title":"Setup the poped database — .setupPopEDdatabase","text":"Setup poped database","code":""},{"path":"/reference/dot-setupPopEDdatabase.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Setup the poped database — .setupPopEDdatabase","text":"","code":".setupPopEDdatabase(ui, data, control)"},{"path":"/reference/dot-setupPopEDdatabase.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Setup the poped database — .setupPopEDdatabase","text":"ui rxode2 ui function data babelmixr2 design data control PopED control","code":""},{"path":"/reference/dot-setupPopEDdatabase.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Setup the poped database — .setupPopEDdatabase","text":"PopED database","code":""},{"path":"/reference/dot-setupPopEDdatabase.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Setup the poped database — .setupPopEDdatabase","text":"Matthew L. Fidler","code":""},{"path":"/reference/getStandardColNames.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine standardized rxode2 column names from data — getStandardColNames","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"Determine standardized rxode2 column names data","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data)"},{"path":"/reference/getStandardColNames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"data data.frame source column names","code":""},{"path":"/reference/getStandardColNames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"named character vector names standardized names values either name column data NA column present data.","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data.frame(ID=1, DV=2, Time=3, CmT=4)) #> id time amt rate dur evid cmt ss ii addl dv #> \"ID\" \"Time\" NA NA NA NA \"CmT\" NA NA NA \"DV\" #> mdv dvid cens limit #> NA NA NA NA"},{"path":"/reference/modelUnitConversion.html","id":null,"dir":"Reference","previous_headings":"","what":"Unit conversion for pharmacokinetic models — modelUnitConversion","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"Unit conversion pharmacokinetic models","code":""},{"path":"/reference/modelUnitConversion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion( dvu = NA_character_, amtu = NA_character_, timeu = NA_character_, volumeu = NA_character_ )"},{"path":"/reference/modelUnitConversion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"dvu, amtu, timeu units DV, AMT, TIME columns data volumeu units volume parameters model","code":""},{"path":"/reference/modelUnitConversion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"list names units associated parameter (\"amtu\", \"clearanceu\", \"volumeu\", \"timeu\", \"dvu\") numeric value multiply modeled estimate (example, cp) model consistent data units.","code":""},{"path":[]},{"path":"/reference/modelUnitConversion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion(dvu = \"ng/mL\", amtu = \"mg\", timeu = \"hr\", volumeu = \"L\") #> Loading required namespace: testthat #> $amtu #> [1] \"mg\" #> #> $clearanceu #> [1] \"L/h\" #> #> $volumeu #> [1] \"L\" #> #> $timeu #> [1] \"hr\" #> #> $dvu #> [1] \"ng/mL\" #> #> $cmtu #> [1] \"mg/L\" #> #> $dvConversion #> [1] 1000 #>"},{"path":"/reference/monolixControl.html","id":null,"dir":"Reference","previous_headings":"","what":"Monolix Controller for nlmixr2 — monolixControl","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Monolix Controller nlmixr2","code":""},{"path":"/reference/monolixControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Monolix Controller for nlmixr2 — monolixControl","text":"","code":"monolixControl( nbSSDoses = 7, useLinearization = FALSE, stiff = FALSE, addProp = c(\"combined2\", \"combined1\"), exploratoryAutoStop = FALSE, smoothingAutoStop = FALSE, burnInIterations = 5, smoothingIterations = 200, exploratoryIterations = 250, simulatedAnnealingIterations = 250, exploratoryInterval = 200, exploratoryAlpha = 0, omegaTau = 0.95, errorModelTau = 0.95, variability = c(\"none\", \"firstStage\", \"decreasing\"), runCommand = getOption(\"babelmixr2.monolix\", \"\"), rxControl = NULL, sumProd = FALSE, optExpression = TRUE, calcTables = TRUE, compress = TRUE, ci = 0.95, sigdigTable = NULL, absolutePath = FALSE, modelName = NULL, muRefCovAlg = TRUE, run = TRUE, ... )"},{"path":"/reference/monolixControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Monolix Controller for nlmixr2 — monolixControl","text":"nbSSDoses Number steady state doses (default 7) useLinearization Use linearization log likelihood fim. stiff boolean using stiff ODE solver addProp specifies type additive plus proportional errors, one standard deviations add (combined1) type variances add (combined2). combined1 error type can described following equation: $$y = f + (+ b\\times f^c) \\times \\varepsilon$$ combined2 error model can described following equation: $$y = f + \\sqrt{^2 + b^2\\times f^{2\\times c}} \\times \\varepsilon$$ : - y represents observed value - f represents predicted value - additive standard deviation - b proportional/power standard deviation - c power exponent (proportional case c=1) exploratoryAutoStop logical turn exploratory phase auto-stop SAEM (default 250) smoothingAutoStop Boolean indicating smoothing automatically stop (default FALSE) burnInIterations Number burn iterations smoothingIterations Number smoothing iterations exploratoryIterations Number iterations exploratory phase (default 250) simulatedAnnealingIterations Number simulating annealing iterations exploratoryInterval Minimum number iterations exploratory phase (default 200) exploratoryAlpha Convergence memory exploratory phase (used exploratoryAutoStop TRUE) omegaTau Proportional rate variance simulated annealing errorModelTau Proportional rate error model simulated annealing variability describes methodology parameters without variability. : - Fixed throughout (none) - Variability first stage (firstStage) - Decreasing reaches fixed value (decreasing) runCommand shell command function run monolix; can specify default options(\"babelmixr2.monolix\"=\"runMonolix\"). empty 'lixoftConnectors' available, use lixoftConnectors run monolix. See details function usage. rxControl `rxode2` ODE solving options fitting, created `rxControl()` sumProd boolean indicating model change multiplication high precision multiplication sums high precision sums using PreciseSums package. default FALSE. optExpression Optimize rxode2 expression speed calculation. default turned . calcTables boolean determine foceiFit calculate tables. default TRUE compress object compressed items ci Confidence level tables. default 0.95 95% confidence. sigdigTable Significant digits final output table. specified, matches significant digits `sigdig` optimization algorithm. `sigdig` NULL, use 3. absolutePath Boolean indicating absolute path used monolix runs modelName Model name used generate NONMEM output. NULL try infer model name (x clear). Otherwise use character outputs. muRefCovAlg controls algebraic expressions can mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#` algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model. Therefore, default `TRUE`. run monolix run results imported nlmixr2? (Default TRUE) ... Ignored parameters","code":""},{"path":"/reference/monolixControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Monolix Controller for nlmixr2 — monolixControl","text":"monolix control object","code":""},{"path":"/reference/monolixControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Monolix Controller for nlmixr2 — monolixControl","text":"runCommand given string, called system() command like: runCommand mlxtran. example, runCommand=\"'/path//monolix/mlxbsub2021' -p \" command line used look like following: '/path//monolix/mlxbsub2021' monolix.mlxtran runCommand given function, called FUN(mlxtran, directory, ui) run Monolix. allows run Monolix way may need, long can write R. babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting Monolix. Note can get translated monolix components parsed/compiled rxode2 ui object ui$monolixModel ui$mlxtran","code":""},{"path":"/reference/monolixControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Matthew Fidler","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Estimate starting parameters using PKNCA","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"","code":"# S3 method for class 'pknca' nlmixr2Est(env, ...)"},{"path":"/reference/nlmixr2Est.pknca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"env Environment nlmixr2 estimation routines. needs : - rxode2 ui object `$ui` - data fit estimation routine `$data` - control estimation routine's control options `$ui` ... arguments provided `nlmixr2Est()` provided flexibility currently used inside nlmixr","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"model updated starting parameters. model new element named \"nca\" available includes PKNCA results used calculation.","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Parameters estimated follows: ka 4 half-lives Tmax higher 3: log(2)/(tmax/4) vc Inverse dose-normalized Cmax cl Estimated median clearance vp,vp22- 4-fold vc, respectively default, controlled vpMult vp2Mult arguments pkncaControl q,q2 0.5- 0.25-fold cl, respectively default, controlled qMult q2Mult arguments pkncaControl bounds parameter estimates set 10% first percentile 10 times 99th percentile. (ka, lower bound set lower 10% first percentile 0.03 upper bound modified 10 times 99th percentile.) Parameter estimation methods may changed future version.","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"","code":"nmGetDistributionMonolixLines(line) # S3 method for class 'rxUi' nmGetDistributionMonolixLines(line) # S3 method for class 'norm' nmGetDistributionMonolixLines(line)"},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Lines estimation monolix","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Matthew Fidler","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"","code":"nmGetDistributionNonmemLines(line) # S3 method for class 'rxUi' nmGetDistributionNonmemLines(line) # S3 method for class 'norm' nmGetDistributionNonmemLines(line)"},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Lines estimation nonmem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Matthew Fidler","code":""},{"path":"/reference/nonmemControl.html","id":null,"dir":"Reference","previous_headings":"","what":"NONMEM estimation control — nonmemControl","title":"NONMEM estimation control — nonmemControl","text":"NONMEM estimation control","code":""},{"path":"/reference/nonmemControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl( est = c(\"focei\", \"imp\", \"its\", \"posthoc\"), advanOde = c(\"advan13\", \"advan8\", \"advan6\"), cov = c(\"r,s\", \"r\", \"s\", \"\"), maxeval = 1e+05, tol = 6, atol = 12, sstol = 6, ssatol = 12, sigl = 12, sigdig = 3, print = 1, extension = getOption(\"babelmixr2.nmModelExtension\", \".nmctl\"), outputExtension = getOption(\"babelmixr2.nmOutputExtension\", \".lst\"), runCommand = getOption(\"babelmixr2.nonmem\", \"\"), iniSigDig = 5, protectZeros = FALSE, muRef = TRUE, addProp = c(\"combined2\", \"combined1\"), rxControl = NULL, sumProd = FALSE, optExpression = TRUE, calcTables = TRUE, compress = TRUE, ci = 0.95, sigdigTable = NULL, readRounding = FALSE, readBadOpt = FALSE, niter = 100L, isample = 1000L, iaccept = 0.4, iscaleMin = 0.1, iscaleMax = 10, df = 4, seed = 14456, mapiter = 1, mapinter = 0, noabort = TRUE, modelName = NULL, muRefCovAlg = TRUE, run = TRUE, ... )"},{"path":"/reference/nonmemControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NONMEM estimation control — nonmemControl","text":"est NONMEM estimation method advanOde ODE solving method NONMEM cov NONMEM covariance method maxeval NONMEM's maxeval (non posthoc methods) tol NONMEM tolerance ODE solving advan atol NONMEM absolute tolerance ODE solving sstol NONMEM tolerance steady state ODE solving ssatol NONMEM absolute tolerance steady state ODE solving sigl NONMEM sigl estimation option sigdig significant digits NONMEM print print number NONMEM extension NONMEM file extensions outputExtension Extension use NONMEM output listing runCommand Command run NONMEM (typically path \"nmfe75\") function. See details information. iniSigDig many significant digits printed $THETA $OMEGA estimate zero. Also controls zero protection numbers protectZeros Add methods protect divide zero muRef Automatically mu-reference control stream addProp, sumProd, optExpression, calcTables, compress, ci, sigdigTable Passed nlmixr2est::foceiControl rxControl Options pass rxode2::rxControl simulations readRounding Try read NONMEM output NONMEM terminated due rounding errors readBadOpt Try read NONMEM output NONMEM terminated due apparent failed optimization niter number iterations NONMEM estimation methods isample Isample argument NONMEM estimation method iaccept Iaccept NONMEM estimation methods iscaleMin parameter IMP NONMEM method (ISCALE_MIN) iscaleMax parameter IMP NONMEM method (ISCALE_MAX) df degrees freedom IMP method seed seed NONMEM methods mapiter number map iterations IMP method mapinter MAPINTER parameter IMP method noabort Add NOABORT option $EST modelName Model name used generate NONMEM output. NULL try infer model name (x clear). Otherwise use character outputs. muRefCovAlg controls algebraic expressions can mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#` algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model. Therefore, default `TRUE`. run NONMEM run (files imported nlmixr2); default TRUE, FALSE simply create NONMEM control stream data file. ... optional genRxControl argument controlling automatic rxControl generation.","code":""},{"path":"/reference/nonmemControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NONMEM estimation control — nonmemControl","text":"babelmixr2 control option generating NONMEM control stream reading back babelmixr2/nlmixr2","code":""},{"path":"/reference/nonmemControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NONMEM estimation control — nonmemControl","text":"runCommand given string, called system() command like: runCommand controlFile outputFile. example, runCommand=\"'/path//nmfe75'\" command line used look like following: '/path//nmfe75' one.cmt.nmctl one.cmt.lst runCommand given function, called FUN(ctl, directory, ui) run NONMEM. allows run NONMEM way may need, long can write R. babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting NONMEM.","code":""},{"path":"/reference/nonmemControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"NONMEM estimation control — nonmemControl","text":"Matthew L. Fidler","code":""},{"path":"/reference/nonmemControl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl() #> $est #> [1] \"focei\" #> #> $cov #> [1] \"r,s\" #> #> $advanOde #> [1] \"advan13\" #> #> $maxeval #> [1] 1e+05 #> #> $print #> [1] 1 #> #> $noabort #> [1] TRUE #> #> $iniSigDig #> [1] 5 #> #> $tol #> [1] 6 #> #> $atol #> [1] 12 #> #> $sstol #> [1] 6 #> #> $ssatol #> [1] 12 #> #> $sigl #> [1] 12 #> #> $muRef #> [1] TRUE #> #> $sigdig #> [1] 3 #> #> $protectZeros #> [1] FALSE #> #> $runCommand #> [1] \"\" #> #> $outputExtension #> [1] \".lst\" #> #> $addProp #> [1] \"combined2\" #> #> $rxControl #> $scale #> NULL #> #> $method #> liblsoda #> 2 #> #> $atol #> [1] 1e-12 #> #> $rtol #> [1] 1e-06 #> #> $maxsteps #> [1] 70000 #> #> $hmin #> [1] 0 #> #> $hmax #> [1] NA #> #> $hini #> [1] 0 #> #> $maxordn #> [1] 12 #> #> $maxords #> [1] 5 #> #> $covsInterpolation #> nocb #> 2 #> #> $addCov #> [1] TRUE #> #> $returnType #> rxSolve #> 0 #> #> $sigma #> NULL #> #> $sigmaDf #> NULL #> #> $nCoresRV #> [1] 1 #> #> $sigmaIsChol #> [1] FALSE #> #> $sigmaSeparation #> [1] \"auto\" #> #> $sigmaXform #> identity #> 4 #> #> $nDisplayProgress #> [1] 10000 #> #> $amountUnits #> [1] NA #> #> $timeUnits #> [1] \"hours\" #> #> $addDosing #> [1] FALSE #> #> $stateTrim #> [1] Inf #> #> $updateObject #> [1] FALSE #> #> $omega #> NULL #> #> $omegaDf #> NULL #> #> $omegaIsChol #> [1] FALSE #> #> $omegaSeparation #> [1] \"auto\" #> #> $omegaXform #> variance #> 6 #> #> $nSub #> [1] 1 #> #> $thetaMat #> NULL #> #> $thetaDf #> NULL #> #> $thetaIsChol #> [1] FALSE #> #> $nStud #> [1] 1 #> #> $dfSub #> [1] 0 #> #> $dfObs #> [1] 0 #> #> $seed #> NULL #> #> $nsim #> NULL #> #> $minSS #> [1] 10 #> #> $maxSS #> [1] 1000 #> #> $strictSS #> [1] 1 #> #> $infSSstep #> [1] 12 #> #> $istateReset #> [1] TRUE #> #> $subsetNonmem #> [1] TRUE #> #> $hmaxSd #> [1] 0 #> #> $maxAtolRtolFactor #> [1] 0.1 #> #> $from #> NULL #> #> $to #> NULL #> #> $by #> NULL #> #> $length.out #> NULL #> #> $iCov #> NULL #> #> $keep #> NULL #> #> $keepF #> character(0) #> #> $drop #> NULL #> #> $warnDrop #> [1] TRUE #> #> $omegaLower #> [1] -Inf #> #> $omegaUpper #> [1] Inf #> #> $sigmaLower #> [1] -Inf #> #> $sigmaUpper #> [1] Inf #> #> $thetaLower #> [1] -Inf #> #> $thetaUpper #> [1] Inf #> #> $indLinPhiM #> [1] 0 #> #> $indLinPhiTol #> [1] 1e-07 #> #> $indLinMatExpType #> expokit #> 2 #> #> $indLinMatExpOrder #> [1] 6 #> #> $idFactor #> [1] TRUE #> #> $mxhnil #> [1] 0 #> #> $hmxi #> [1] 0 #> #> $warnIdSort #> [1] TRUE #> #> $ssAtol #> [1] 1e-12 #> #> $ssRtol #> [1] 1e-06 #> #> $safeZero #> [1] 0 #> #> $sumType #> pairwise #> 1 #> #> $prodType #> long double #> 1 #> #> $sensType #> advan #> 4 #> #> $linDiff #> tlag f rate dur tlag2 f2 rate2 dur2 #> 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 #> #> $linDiffCentral #> tlag f rate dur tlag2 f2 rate2 dur2 #> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> #> $resample #> NULL #> #> $resampleID #> [1] TRUE #> #> $maxwhile #> [1] 100000 #> #> $cores #> [1] 0 #> #> $atolSens #> [1] 1e-08 #> #> $rtolSens #> [1] 1e-06 #> #> $ssAtolSens #> [1] 1e-08 #> #> $ssRtolSens #> [1] 1e-06 #> #> $simVariability #> [1] NA #> #> $nLlikAlloc #> NULL #> #> $useStdPow #> [1] 0 #> #> $naTimeHandle #> ignore #> 1 #> #> $addlKeepsCov #> [1] FALSE #> #> $addlDropSs #> [1] TRUE #> #> $ssAtDoseTime #> [1] TRUE #> #> $ss2cancelAllPending #> [1] FALSE #> #> $naInterpolation #> locf #> 1 #> #> $keepInterpolation #> na #> 2 #> #> $safeLog #> [1] 1 #> #> $safePow #> [1] 1 #> #> $.zeros #> NULL #> #> attr(,\"class\") #> [1] \"rxControl\" #> #> $sumProd #> [1] FALSE #> #> $optExpression #> [1] TRUE #> #> $calcTables #> [1] TRUE #> #> $compress #> [1] TRUE #> #> $ci #> [1] 0.95 #> #> $sigdigTable #> NULL #> #> $readRounding #> [1] FALSE #> #> $readBadOpt #> [1] FALSE #> #> $genRxControl #> [1] TRUE #> #> $niter #> [1] 100 #> #> $isample #> [1] 1000 #> #> $iaccept #> [1] 0.4 #> #> $iscaleMin #> [1] 0.1 #> #> $iscaleMax #> [1] 10 #> #> $df #> [1] 4 #> #> $seed #> [1] 14456 #> #> $mapiter #> [1] 1 #> #> $modelName #> NULL #> #> $muRefCovAlg #> [1] TRUE #> #> $run #> [1] TRUE #> #> attr(,\"class\") #> [1] \"nonmemControl\""},{"path":"/reference/pkncaControl.html","id":null,"dir":"Reference","previous_headings":"","what":"PKNCA estimation control — pkncaControl","title":"PKNCA estimation control — pkncaControl","text":"PKNCA estimation control","code":""},{"path":"/reference/pkncaControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PKNCA estimation control — pkncaControl","text":"","code":"pkncaControl( concu = NA_character_, doseu = NA_character_, timeu = NA_character_, volumeu = NA_character_, vpMult = 2, qMult = 1/2, vp2Mult = 4, q2Mult = 1/4, dvParam = \"cp\", groups = character(), sparse = FALSE, ncaData = NULL, ncaResults = NULL, rxControl = rxode2::rxControl() )"},{"path":"/reference/pkncaControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PKNCA estimation control — pkncaControl","text":"concu, doseu, timeu concentration, dose, time units source data (passed PKNCA::pknca_units_table()). volumeu compartment volume model (NULL, simplified units source data used) vpMult, qMult, vp2Mult, q2Mult Multipliers vc cl provide initial estimates vp, q, vp2, q2 dvParam parameter name model modified concentration unit conversions. must assigned line , separate residual error model line. groups Grouping columns NCA summaries group (required sparse = TRUE) sparse concentration-time data sparse PK (commonly used small nonclinical species terminal difficult sampling) dense PK (commonly used clinical studies larger nonclinical species)? ncaData Data use calculating NCA parameters. Typical use subset original data informative NCA. ncaResults Already computed NCA results (PKNCAresults object) bypass automatic calculations. least following parameters must calculated NCA: tmax, cmax.dn, cl.last rxControl Control options sent rxode2::rxControl()","code":""},{"path":"/reference/pkncaControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PKNCA estimation control — pkncaControl","text":"list parameters","code":""},{"path":"/reference/popedControl.html","id":null,"dir":"Reference","previous_headings":"","what":"Control for a PopED design task — popedControl","title":"Control for a PopED design task — popedControl","text":"Control PopED design task","code":""},{"path":"/reference/popedControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control for a PopED design task — popedControl","text":"","code":"popedControl( stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), maxn = NULL, rxControl = NULL, sigdig = 4, important = NULL, unimportant = NULL, iFIMCalculationType = c(\"reduced\", \"full\", \"weighted\", \"loc\", \"reducedPFIM\", \"fullABC\", \"largeMat\", \"reducedFIMABC\"), iApproximationMethod = c(\"fo\", \"foce\", \"focei\", \"foi\"), iFOCENumInd = 1000, prior_fim = matrix(0, 0, 1), d_switch = c(\"d\", \"ed\"), ofv_calc_type = c(\"lnD\", \"d\", \"a\", \"Ds\", \"inverse\"), strEDPenaltyFile = \"\", ofv_fun = NULL, iEDCalculationType = c(\"mc\", \"laplace\", \"bfgs-laplace\"), ED_samp_size = 45, bLHS = c(\"hypercube\", \"random\"), bUseRandomSearch = TRUE, bUseStochasticGradient = TRUE, bUseLineSearch = TRUE, bUseExchangeAlgorithm = FALSE, bUseBFGSMinimizer = FALSE, bUseGrouped_xt = FALSE, EACriteria = c(\"modified\", \"fedorov\"), strRunFile = \"\", poped_version = NULL, modtit = \"PopED babelmixr2 model\", output_file = \"PopED_output_summary\", output_function_file = \"PopED_output_\", strIterationFileName = \"PopED_current.R\", user_data = NULL, ourzero = 1e-05, dSeed = NULL, line_opta = NULL, line_optx = NULL, bShowGraphs = FALSE, use_logfile = FALSE, m1_switch = c(\"central\", \"complex\", \"analytic\", \"ad\"), m2_switch = c(\"central\", \"complex\", \"analytic\", \"ad\"), hle_switch = c(\"central\", \"complex\", \"ad\"), gradff_switch = c(\"central\", \"complex\", \"analytic\", \"ad\"), gradfg_switch = c(\"central\", \"complex\", \"analytic\", \"ad\"), grad_all_switch = c(\"central\", \"complex\"), rsit_output = 5, sgit_output = 1, hm1 = 1e-05, hlf = 1e-05, hlg = 1e-05, hm2 = 1e-05, hgd = 1e-05, hle = 1e-05, AbsTol = 1e-06, RelTol = 1e-06, iDiffSolverMethod = NULL, bUseMemorySolver = FALSE, rsit = 300, sgit = 150, intrsit = 250, intsgit = 50, maxrsnullit = 50, convergence_eps = 1e-08, rslxt = 10, rsla = 10, cfaxt = 0.001, cfaa = 0.001, bGreedyGroupOpt = FALSE, EAStepSize = 0.01, EANumPoints = FALSE, EAConvergenceCriteria = 1e-20, bEANoReplicates = FALSE, BFGSProjectedGradientTol = 1e-04, BFGSTolerancef = 0.001, BFGSToleranceg = 0.9, BFGSTolerancex = 0.1, ED_diff_it = 30, ED_diff_percent = 10, line_search_it = 50, Doptim_iter = 1, iCompileOption = c(\"none\", \"full\", \"mcc\", \"mpi\"), compileOnly = FALSE, iUseParallelMethod = c(\"mpi\", \"matlab\"), MCC_Dep = NULL, strExecuteName = \"calc_fim.exe\", iNumProcesses = 2, iNumChunkDesignEvals = -2, Mat_Out_Pre = \"parallel_output\", strExtraRunOptions = \"\", dPollResultTime = 0.1, strFunctionInputName = \"function_input\", bParallelRS = FALSE, bParallelSG = FALSE, bParallelMFEA = FALSE, bParallelLS = FALSE, groupsize = NULL, time = \"time\", timeLow = \"low\", timeHi = \"high\", id = \"id\", m = NULL, x = NULL, ni = NULL, maxni = NULL, minni = NULL, maxtotni = NULL, mintotni = NULL, maxgroupsize = NULL, mingroupsize = NULL, maxtotgroupsize = NULL, mintotgroupsize = NULL, xt_space = NULL, a = NULL, maxa = NULL, mina = NULL, a_space = NULL, x_space = NULL, use_grouped_xt = FALSE, grouped_xt = NULL, use_grouped_a = FALSE, grouped_a = NULL, use_grouped_x = FALSE, grouped_x = NULL, our_zero = NULL, auto_pointer = \"\", user_distribution_pointer = \"\", minxt = NULL, maxxt = NULL, discrete_xt = NULL, discrete_a = NULL, fixRes = FALSE, script = NULL, overwrite = TRUE, literalFix = TRUE, opt_xt = FALSE, opt_a = FALSE, opt_x = FALSE, opt_samps = FALSE, optTime = TRUE, ... )"},{"path":"/reference/popedControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control for a PopED design task — popedControl","text":"stickyRecalcN number bad ODE solves reducing atol/rtol rest problem. maxOdeRecalc Maximum number times reduce ODE tolerances try resolve system bad ODE solve. odeRecalcFactor ODE recalculation factor ODE solving goes bad, factor rtol/atol reduced maxn Maximum number design points optimization; default declared maximum number design points babelmixr2 dataset (NULL) rxControl `rxode2` ODE solving options fitting, created `rxControl()` sigdig Optimization significant digits. controls: tolerance inner outer optimization 10^-sigdig tolerance ODE solvers 0.5*10^(-sigdig-2); sensitivity equations steady-state solutions default 0.5*10^(-sigdig-1.5) (sensitivity changes applicable liblsoda) tolerance boundary check 5 * 10 ^ (-sigdig + 1) important character vector important parameters NULL default. used Ds-optimality unimportant character vector unimportant parameters NULL default. used Ds-optimality iFIMCalculationType can either integer named value Fisher Information Matrix type: 0/\"full\" = Full FIM 1/\"reduced\" = Reduced FIM 2/\"weighted\" = weighted models 3/\"loc\" = Loc models 4/\"reducedPFIM\" = reduced FIM derivative SD sigma PFIM 5/\"fullABC\" = FULL FIM parameterized ,B,C matrices & derivative variance 6/\"largeMat\" = Calculate one model switch time, good large matrices 7/\"reducedFIMABC\" = =Reduced FIM parameterized ,B,C matrices & derivative variance iApproximationMethod Approximation method model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI iFOCENumInd integer; number individuals focei solve prior_fim matrix; prior FIM d_switch integer character option: 0/\"ed\" = ED design 1/\"d\" = D design ofv_calc_type objective calculation type: 1/\"d\" = D-optimality\". Determinant FIM: det(FIM) 2/\"\" = \"-optimality\". Inverse sum expected parameter variances: 1/trace_matrix(inv(FIM)) 4/\"lnD\" = \"lnD-optimality\". Natural logarithm determinant FIM: log(det(FIM)) 6/\"Ds\" = \"Ds-optimality\". Ratio Determinant FIM Determinant uninteresting rows columns FIM: det(FIM)/det(FIM_u) 7/\"inverse\" = Inverse sum expected parameter RSE: 1/sum(get_rse(FIM,poped.db,use_percent=FALSE)) strEDPenaltyFile Penalty function name path filename, empty string means penalty. User defined criterion can defined way. ofv_fun User defined function used compute objective function. function must poped database object first argument \"...\" argument list. Can referenced function file name function defined file name file. e.g. \"cost.txt\" function named \"cost\" . iEDCalculationType ED Integral Calculation type: 0/\"mc\" = Monte-Carlo-Integration 1/\"laplace\" = Laplace Approximation 2/\"bfgs-laplace\" = BFGS Laplace Approximation ED_samp_size Sample size E-family sampling bLHS sample distributions E-family calculations. 0=Random Sampling, 1=LatinHyperCube – bUseRandomSearch ******START Optimization algorithm SPECIFICATION OPTIONS********** Use random search (1=TRUE, 0=FALSE) bUseStochasticGradient Use Stochastic Gradient search (1=TRUE, 0=FALSE) bUseLineSearch Use Line search (1=TRUE, 0=FALSE) bUseExchangeAlgorithm Use Exchange algorithm (1=TRUE, 0=FALSE) bUseBFGSMinimizer Use BFGS Minimizer (1=TRUE, 0=FALSE) bUseGrouped_xt Use grouped time points (1=TRUE, 0=FALSE). EACriteria Exchange Algorithm Criteria: 1/\"modified\" = Modified 2/\"fedorov\" = Fedorov strRunFile Filename path, function name, run file used instead regular PopED call. poped_version ******START Labeling file names SPECIFICATION OPTIONS********** current PopED version modtit model title output_file Filename path output file search output_function_file Filename suffix result function file strIterationFileName Filename path storage current optimal design user_data ******START Miscellaneous SPECIFICATION OPTIONS********** User defined data structure , example used send data model ourzero Value interpret zero design dSeed seed number used optimization sampling – integer -1 creates random seed .integer(Sys.time()) NULL. line_opta Vector line search continuous design variables (1=TRUE,0=FALSE) line_optx Vector line search discrete design variables (1=TRUE,0=FALSE) bShowGraphs Use graph output search use_logfile log file used (0=FALSE, 1=TRUE) m1_switch Method used calculate M1: 1/\"central\" = Central difference 0/\"complex\" = Complex difference 20/\"analytic\" = Analytic derivative 30/\"ad\" = Automatic differentiation m2_switch Method used calculate M2: 1/\"central\" = Central difference 0/\"complex\" = Complex difference 20/\"analytic\" = Analytic derivative 30/\"ad\" = Automatic differentiation hle_switch Method used calculate linearization residual error: 1/\"central\" = Central difference 0/\"complex\" = Complex difference 30/\"ad\" = Automatic differentiation gradff_switch Method used calculate gradient model: 1/\"central\" = Central difference 0/\"complex\" = Complex difference 20/\"analytic\" = Analytic derivative 30/\"ad\" = Automatic differentiation gradfg_switch Method used calculate gradient parameter vector g: 1/\"central\" = Central difference 0/\"complex\" = Complex difference 20/\"analytic\" = Analytic derivative 30/\"ad\" = Automatic differentiation grad_all_switch Method used calculate gradients: 1/\"central\" = Central difference 0/\"complex\" = Complex difference rsit_output Number iterations random search screen output sgit_output Number iterations stochastic gradient search screen output hm1 Step length derivative linearized model w.r.t. typical values hlf Step length derivative model w.r.t. g hlg Step length derivative g w.r.t. b hm2 Step length derivative variance w.r.t. typical values hgd Step length derivative OFV w.r.t. time hle Step length derivative model w.r.t. sigma AbsTol absolute tolerance diff equation solver RelTol relative tolerance diff equation solver iDiffSolverMethod diff equation solver method, NULL default. bUseMemorySolver differential equation results stored memory (1) (0) rsit Number Random search iterations sgit Number stochastic gradient iterations intrsit Number Random search iterations discrete optimization. intsgit Number Stochastic Gradient search iterations discrete optimization maxrsnullit Iterations adaptive narrowing random search convergence_eps Stochastic Gradient convergence value, (difference OFV D-optimal, difference gradient ED-optimal) rslxt Random search locality factor sample times rsla Random search locality factor covariates cfaxt Stochastic Gradient search first step factor sample times cfaa Stochastic Gradient search first step factor covariates bGreedyGroupOpt Use greedy algorithm group assignment optimization EAStepSize Exchange Algorithm StepSize EANumPoints Exchange Algorithm NumPoints EAConvergenceCriteria Exchange Algorithm Convergence Limit/Criteria bEANoReplicates Avoid replicate samples using Exchange Algorithm BFGSProjectedGradientTol BFGS Minimizer Convergence Criteria Normalized Projected Gradient Tolerance BFGSTolerancef BFGS Minimizer Line Search Tolerance f BFGSToleranceg BFGS Minimizer Line Search Tolerance g BFGSTolerancex BFGS Minimizer Line Search Tolerance x ED_diff_it Number iterations ED-optimal design calculate convergence criteria ED_diff_percent ED-optimal design convergence criteria percent line_search_it Number grid points line search Doptim_iter Number iterations full Random search full Stochastic Gradient line search used iCompileOption Compile options PopED \"none\"/-1 = compilation \"full/0 3 = Full compilation \"mcc\"/1 4 = using MCC (shared lib) \"mpi\"/2 5 = MPI, using numbers, option 0,1,2 runs PopED option 3,4,5 stops compilation. using characters, option compileOnly determines model compiled (PopED run). compileOnly logical; compile model, run PopED (conjunction iCompileOption) iUseParallelMethod Parallel method use 0/\"matlab\"= Matlab PCT 1/\"mpi\" = MPI MCC_Dep Additional dependencies used MCC compilation (mat-files), several space separated strExecuteName Compilation output executable name iNumProcesses Number processes use running parallel (e.g. 3 = 2 workers, 1 job manager) iNumChunkDesignEvals Number design evaluations evaluated process getting new work job manager Mat_Out_Pre prefix output mat file communicate executable strExtraRunOptions Extra options send e$g. MPI executable batch script, see execute_parallel$m information options dPollResultTime Polling time check parallel execution finished strFunctionInputName file containing popedInput structure used evaluate designs bParallelRS random search going executed parallel bParallelSG stochastic gradient search going executed parallel bParallelMFEA modified exchange algorithm going executed parallel bParallelLS line search going executed parallel groupsize Vector defining size different groups (num individuals group). one number number every group. time string represents time dataset (ie xt) timeLow string represents lower design time (ie minxt) timeHi string represents upper design time (ie maxmt) id id variable m Number groups study. individual group design. x matrix defining initial discrete values model row group/individual. ni Vector defining number samples group. maxni ******START DESIGN SPACE OPTIONS********** Max number samples per group/individual minni Min number samples per group/individual maxtotni Number defining maximum number samples allowed experiment. mintotni Number defining minimum number samples allowed experiment. maxgroupsize Vector defining max size different groups (max number individuals group) mingroupsize Vector defining min size different groups (min num individuals group) – maxtotgroupsize total maximal groupsize groups mintotgroupsize total minimal groupsize groups xt_space Cell array cell defining discrete variables allowed xt value. Can also vector values c(1:10) (values allowed xt), list lists list(1:10, 2:23, 4:6) (one value xt row major order just one row xt, rows duplicated). Matrix defining initial continuous covariate values. n_rows=number groups, n_cols=number covariates. number rows one number groups > 1 groups assigned values. maxa Vector defining max value covariate. single value supplied values given max value mina Vector defining min value covariate. single value supplied values given max value a_space Cell array cell defining discrete variables allowed value. Can also list values list(1:10) (values allowed ), list lists list(1:10, 2:23, 4:6) (one value ). x_space Cell array cell defining discrete variables x value. use_grouped_xt Group sampling times groups group values (TRUE FALSE). grouped_xt Matrix defining grouping sample points. Matching integers mean points matched. Allows finer control use_grouped_xt use_grouped_a Group continuous design variables groups group values (TRUE FALSE). grouped_a Matrix defining grouping continuous design variables. Matching integers mean values matched. Allows finer control use_grouped_a. use_grouped_x Group discrete design variables groups group values (TRUE FALSE). grouped_x Matrix defining grouping discrete design variables. Matching integers mean values matched. Allows finer control use_grouped_x. our_zero Value interpret zero design. auto_pointer Filename path, function name, Autocorrelation function, empty string means autocorrelation user_distribution_pointer Filename path, function name, user defined distributions E-family designs minxt Matrix single value defining minimum value xt sample. single value supplied xt values given minimum value maxxt Matrix single value defining maximum value xt sample. single value supplied xt values given maximum value. discrete_xt Cell array cell defining discrete variables allowed xt value. Can also list values list(1:10) (values allowed xt), list lists list(1:10, 2:23, 4:6) (one value xt). See examples create_design_space. discrete_a Cell array cell defining discrete variables allowed value. Can also list values list(1:10) (values allowed ), list lists list(1:10, 2:23, 4:6) (one value ). See examples create_design_space. fixRes boolean; Fix residuals specified model script write PopED/rxode2 script can modified fine control. default NULL. script TRUE, script returned lines written file class babelmixr2popedScript. allows printed script screen. script file name (R extension), script written file. overwrite [logical(1)] TRUE, existing file place allowed readable writable. Default FALSE. literalFix boolean, substitute fixed population values literals re-adjust ui parameter estimates optimization; Default `TRUE`. opt_xt boolean indicate meant optimizing times opt_a boolean indicate meant optimizing covariates opt_x boolean indicate discrete design variables optimized opt_samps boolean indicate sample optimizer used (implemented yet PopED) optTime boolean indicate global time indexer inside babelmixr2 reset times different. default TRUE. FALSE can get slightly better run times possibly slightly different results. optTime FALSE global indexer reset every time PopED rxode2 setup problem poped dataset created. can manually reset popedMultipleEndpointResetTimeIndex() ... parameters PopED control","code":""},{"path":"/reference/popedControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control for a PopED design task — popedControl","text":"popedControl object","code":""},{"path":"/reference/popedControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Control for a PopED design task — popedControl","text":"Matthew L. Fidler","code":""},{"path":"/reference/popedGetMultipleEndpointModelingTimes.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","title":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","text":"function takes vector times corresponding vector IDs, groups times IDs, initializes internal C++ global TimeIndexer, used efficiently lookup final output rxode2 solve returns sorted unique times. popedMultipleEndpointIndexDataFrame() function can used visualize internal data structure inside R, show indexes case time ties given ID. Rather shows one indexs total number indexes data.frame","code":""},{"path":"/reference/popedGetMultipleEndpointModelingTimes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","text":"","code":"popedGetMultipleEndpointModelingTimes(times, modelSwitch, sorted = FALSE) popedMultipleEndpointIndexDataFrame(print = FALSE)"},{"path":"/reference/popedGetMultipleEndpointModelingTimes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","text":"times numeric vector times. modelSwitch integer vector model switch indicator corresponding times sorted boolean indicating returned times sorted print boolean popedMultipleEndpointIndexDataFrame() TRUE show id/index per time even though may reflect returned data.frame","code":""},{"path":"/reference/popedGetMultipleEndpointModelingTimes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","text":"numeric vector unique times.","code":""},{"path":"/reference/popedGetMultipleEndpointModelingTimes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","text":"","code":"# \\donttest{ times <- c(1.1, 1.2, 1.3, 2.1, 2.2, 3.1) modelSwitch <- c(1, 1, 1, 2, 2, 3) sortedTimes <- popedGetMultipleEndpointModelingTimes(times, modelSwitch, TRUE) print(sortedTimes) #> [1] 1.1 1.2 1.3 2.1 2.2 3.1 # now show the output of the data frame representing the model # switch to endpoint index popedMultipleEndpointIndexDataFrame() #> time MS:1 N:1 MS:3 N:3 MS:5 N:5 #> 1 1.1 1 1 NA NA NA NA #> 2 1.2 2 1 NA NA NA NA #> 3 1.3 3 1 NA NA NA NA #> 4 2.1 NA NA 4 1 NA NA #> 5 2.2 NA NA 5 1 NA NA #> 6 3.1 NA NA NA NA 6 1 # now show a more complex example with overlaps etc. times <- c(1.1, 1.2, 1.3, 0.5, 2.2, 1.1, 0.75,0.75) modelSwitch <- c(1, 1, 1, 2, 2, 2, 3, 3) sortedTimes <- popedGetMultipleEndpointModelingTimes(times, modelSwitch, TRUE) print(sortedTimes) #> [1] 0.50 0.75 1.10 1.20 1.30 2.20 popedMultipleEndpointIndexDataFrame(TRUE) # Print to show individual matching #> modelSwitch: 2 time: 0.500000: 4 #> modelSwitch: 3 time: 0.750000: 7, 8 #> modelSwitch: 2 time: 1.100000: 6 #> modelSwitch: 1 time: 1.100000: 1 #> modelSwitch: 1 time: 1.200000: 2 #> modelSwitch: 1 time: 1.300000: 3 #> modelSwitch: 2 time: 2.200000: 5 #> time MS:1 N:1 MS:3 N:3 MS:5 N:5 #> 1 0.50 NA NA 4 1 NA NA #> 2 0.75 NA NA NA NA 7 2 #> 3 1.10 1 1 6 1 NA NA #> 4 1.20 2 1 NA NA NA NA #> 5 1.30 3 1 NA NA NA NA #> 6 2.20 NA NA 5 1 NA NA # }"},{"path":"/reference/popedMultipleEndpointParam.html","id":null,"dir":"Reference","previous_headings":"","what":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"function populates numeric vector parameters unique times also populates internal C++ global index","code":""},{"path":"/reference/popedMultipleEndpointParam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"","code":"popedMultipleEndpointParam(p, times, modelSwitch, maxMT, optTime = TRUE)"},{"path":"/reference/popedMultipleEndpointParam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"p numeric vector parameters times numeric vector times modelSwitch integer vector indicating model switches PopED maxMT integer specifying maximum number time points mtimes model","code":""},{"path":"/reference/popedMultipleEndpointParam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"numeric vector containing parameters followed unique times, maximum number times greater input append maximum observed times input. assumes first parameter id dropped fro output.","code":""},{"path":"/reference/popedMultipleEndpointParam.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"function first uses input times model switches global time indexer. creates new numeric vector combines input parameters unique times. number times less maxMT, remaining elements filled maximum time.","code":""},{"path":"/reference/popedMultipleEndpointParam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"Matthew L. Fidler","code":""},{"path":"/reference/popedMultipleEndpointParam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"","code":"# \\donttest{ p <- c(1.0, 2.0, 3.0) times <- c(0.5, 1.5, 2.5) modelSwitch <- c(1, 2, 3) maxMT <- 5 popedMultipleEndpointParam(p, times, modelSwitch, maxMT) #> [1] 2.0 3.0 0.5 1.5 2.5 2.5 2.5 # }"},{"path":"/reference/popedMultipleEndpointResetTimeIndex.html","id":null,"dir":"Reference","previous_headings":"","what":"Reset the Global Time Indexer for Multiple Endpoint Modeling — popedMultipleEndpointResetTimeIndex","title":"Reset the Global Time Indexer for Multiple Endpoint Modeling — popedMultipleEndpointResetTimeIndex","text":"clears memory resets global time indexer used multiple endpoint modeling.","code":""},{"path":"/reference/popedMultipleEndpointResetTimeIndex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reset the Global Time Indexer for Multiple Endpoint Modeling — popedMultipleEndpointResetTimeIndex","text":"","code":"popedMultipleEndpointResetTimeIndex()"},{"path":"/reference/popedMultipleEndpointResetTimeIndex.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reset the Global Time Indexer for Multiple Endpoint Modeling — popedMultipleEndpointResetTimeIndex","text":"NULL, called side effects","code":""},{"path":"/reference/popedMultipleEndpointResetTimeIndex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reset the Global Time Indexer for Multiple Endpoint Modeling — popedMultipleEndpointResetTimeIndex","text":"","code":"# \\donttest{ popedMultipleEndpointResetTimeIndex() #> NULL # }"},{"path":"/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. monolix2rx mlxtran, monolix2rx nlmixr2est getValidNlmixrCtl, nlmixr2Est, nmObjGetControl, nmObjGetFoceiControl, nmObjHandleControlObject nonmem2rx .nonmem2rx, nmcov, nmext, nminfo, nmtab, nmxml, nonmem2rx rxode2 .minfo, rxModelVars, rxUiGet","code":""},{"path":"/reference/rxToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to monolix syntax — rxToMonolix","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Convert RxODE syntax monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"","code":"rxToMonolix(x, ui)"},{"path":"/reference/rxToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Matthew Fidler","code":""},{"path":"/reference/rxToNonmem.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Convert RxODE syntax NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"","code":"rxToNonmem(x, ui)"},{"path":"/reference/rxToNonmem.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToNonmem.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Matthew Fidler","code":""},{"path":"/reference/simplifyUnit.html","id":null,"dir":"Reference","previous_headings":"","what":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"Simplify units removing repeated units numerator denominator","code":""},{"path":"/reference/simplifyUnit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(numerator = \"\", denominator = \"\")"},{"path":"/reference/simplifyUnit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"numerator numerator units (whole unit specification) denominator denominator units (NULL numerator whole unit specification)","code":""},{"path":"/reference/simplifyUnit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"units specified units numerator denominator cancelled.","code":""},{"path":"/reference/simplifyUnit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"NA \"\" numerator denominator considered unitless.","code":""},{"path":[]},{"path":"/reference/simplifyUnit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(\"kg\", \"kg/mL\") #> [1] \"mL\" # units that don't match exactly are not cancelled simplifyUnit(\"kg\", \"g/mL\") #> [1] \"kg*mL/g\""},{"path":"/news/index.html","id":"babelmixr2-015","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.5","title":"babelmixr2 0.1.5","text":"Fix bug PopED error certain dvid values Fix bug /else clauses model cause model predict values correctly. Fix bug shrinkage() calculation works Fix bug can mix 2 different PopED data bases analysis without crashing R. didn’t occur every database clash, frequently occurred interleaved PopED code two different PopED databases, like issue #131. Added new function babelBpopIdx(poped.db, \"par\") get poped index model generated babelmixr2, useful calculating power (example 11).","code":""},{"path":"/news/index.html","id":"babelmixr2-014","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.4","title":"babelmixr2 0.1.4","text":"CRAN release: 2024-09-23 Added experimental PopED integration Removed dependence rxode2parse Imported monolix2rx monolix2rx package Also allow conversion model imported monolix nlmixr2 fit.","code":""},{"path":"/news/index.html","id":"babelmixr2-013","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.3","title":"babelmixr2 0.1.3","text":"Changed default NONMEM rounding protection FALSE Added run option monolixControl() nonemControl() case want export modeling files run models.","code":""},{"path":"/news/index.html","id":"babelmixr2-012","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.2","title":"babelmixr2 0.1.2","text":"CRAN release: 2023-12-12 Handle algebraic mu expressions PKNCA controller now contains rxControl since used translation options revision load pruned ui model query compartment properties (.e. bioavailability, lag time, etc) writing NONMEM model. fix issues PK block define variables larger calculated variable can used model instead. nonmem2rx different lst file, long nonmem2rx::nminfo(file) works, successful conversion nlmixr2 fit object occur. Fix save parameter history $parHistData accommodate changes focei’s output ($parHist now derived). Changed solving options match new steady state options rxode2 NONMEM implements . Also changed iwres model account rxerr. instead err. updated rxode2 well.","code":""},{"path":"/news/index.html","id":"babelmixr2-011","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.1","title":"babelmixr2 0.1.1","text":"CRAN release: 2023-05-27 Add new method .nlmixr2 convert nonmem2rx methods nlmixr fits Dropped pmxTools favor nonmem2rx conserve methods","code":""},{"path":"/news/index.html","id":"babelmixr2-010","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.0","title":"babelmixr2 0.1.0","text":"CRAN release: 2022-10-28 Babelmixr support “monolix”, “nonmem”, “pknca” methods release. Added NEWS.md file track changes package.","code":""}] +[{"path":"/articles/PopED.html","id":"introduction-using-babelmixr2-with-poped","dir":"Articles","previous_headings":"","what":"Introduction – using babelmixr2 with PopED","title":"PopED","text":"babelmixr2 now introduces new method takes rxode2/nlmixr2 models converts PopED database help optimal design. PopED vignette comparing ODE solvers (speeds), section : take model described adapt two different rxode2 model functions, solved ode cases (done nlmixr() call creates PopED database) compare examples pharmacometric solvers PopED vignette (mrgsolve PKPDsim)","code":""},{"path":"/articles/PopED.html","id":"babelmixr2-ode-solution","dir":"Articles","previous_headings":"","what":"babelmixr2 ODE solution","title":"PopED","text":"","code":"library(babelmixr2) library(PopED) e <- et(amt=1, ii=24, until=250) %>% et(list(c(0, 10), c(0, 10), c(0, 10), c(240, 248), c(240, 248))) %>% dplyr::mutate(time =c(0, 1,2,8,240,245)) # model f <- function() { ini({ tKA <- 0.25 tCL <- 3.75 tV <- 72.8 Favail <- fix(0.9) eta.ka ~ 0.09 eta.cl ~ 0.25 ^ 2 eta.v ~ 0.09 prop.sd <- sqrt(0.04) add.sd <- sqrt(0.0025) }) model({ ka <- tKA * exp(eta.ka) v <- tV * exp(eta.v) cl <- tCL * exp(eta.cl) d/dt(depot) <- -ka * depot d/dt(central) <- ka * depot - cl / v * central cp <- central / v f(depot) <- DOSE * Favail cp ~ add(add.sd) + prop(prop.sd) }) } poped_db_ode_babelmixr2 <- nlmixr(f, e, popedControl(a=list(c(DOSE=20), c(DOSE=40)), maxa=c(DOSE=200), mina=c(DOSE=0))) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ infer estimation `poped` from control #> ℹ groupsize should be specified; but for now assuming 20 #> ℹ assuming group size m=2 #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ e <- et(amt=1, ii=24, until=250) %>% et(list(c(0, 10), c(0, 10), c(0, 10), c(240, 248), c(240, 248))) %>% dplyr::mutate(time =c(0, 1,2,8,240,245)) # model f <- function() { ini({ tKA <- 0.25 tCL <- 3.75 tV <- 72.8 eta.ka ~ 0.09 eta.cl ~ 0.25 ^ 2 eta.v ~ 0.09 prop.sd <- sqrt(0.04) add.sd <- sqrt(0.0025) }) model({ ka <- tKA * exp(eta.ka) v <- tV * exp(eta.v) cl <- tCL * exp(eta.cl) d/dt(depot) <- -ka * depot d/dt(central) <- ka * depot - cl / v * central cp <- central / v f(depot) <- DOSE cp ~ add(add.sd) + prop(prop.sd) }) } poped_db_ode_babelmixr2 <- nlmixr(f, e, popedControl(a=list(c(DOSE=20), c(DOSE=40)), maxa=c(DOSE=200), mina=c(DOSE=0))) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ infer estimation `poped` from control #> ℹ groupsize should be specified; but for now assuming 20 #> ℹ assuming group size m=2 #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’"},{"path":"/articles/PopED.html","id":"linear-compartment-solution","dir":"Articles","previous_headings":"","what":"Linear compartment solution","title":"PopED","text":"","code":"f2 <- function() { ini({ tV <- 72.8 tKA <- 0.25 tCL <- 3.75 Favail <- fix(0.9) eta.ka ~ 0.09 eta.cl ~ 0.25 ^ 2 eta.v ~ 0.09 prop.sd <- sqrt(0.04) add.sd <- fix(sqrt(5e-6)) }) model({ ka <- tKA * exp(eta.ka) v <- tV * exp(eta.v) cl <- tCL * exp(eta.cl) cp <- linCmt() f(depot) <- DOSE cp ~ add(add.sd) + prop(prop.sd) }) } poped_db_analytic_babelmixr2 <- nlmixr(f, e, popedControl(a=list(c(DOSE=20), c(DOSE=40)), maxa=c(DOSE=200), mina=c(DOSE=0))) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ infer estimation `poped` from control #> ℹ groupsize should be specified; but for now assuming 20 #> ℹ assuming group size m=2"},{"path":"/articles/PopED.html","id":"comparing-method-to-the-speed-of-other-methods","dir":"Articles","previous_headings":"","what":"Comparing method to the speed of other methods","title":"PopED","text":"Note babelmixr2 ode solver fastest ode solver comparison. Among things, model loaded memory need setup time. (benchmarks, mrgsolve, PKPDsim implementations PopED’s website included). Also , speed tools reasonable. opinion, benefit babelmixr2 interface PopED simplicity using nlmixr2 / rxode2 functional models fits directly PopED without relying conversions. interface bit different traditional PopED interface, requires design data-set well popedControl() setup PopED database run PopED tasks. traditionally nlmixr2 takes dataset, “estimation” method controls change estimation method options. babelmixr2 adopts paradigm model, data, control applied PopED. allow easy translation systems. easier translation, hopefully optimal design clinical trials easier achieve.","code":"library(ggplot2) library(microbenchmark) compare <- microbenchmark( evaluate_design(poped_db_analytic), evaluate_design(poped_db_analytic_babelmixr2), evaluate_design(poped_db_ode_babelmixr2), evaluate_design(poped_db_ode_mrg), evaluate_design(poped_db_ode_pkpdsim), times = 100L) autoplot(compare) + theme_bw()"},{"path":"/articles/new-estimation.html","id":"create-a-nlmixr2est-method","dir":"Articles","previous_headings":"","what":"Create a nlmixr2Est() method","title":"Creating a New Estimation Method","text":"method input environment nlmixr2est UI object (see ?nlmixr2Est). output fit object.","code":""},{"path":"/articles/new-estimation.html","id":"create-a-control-method","dir":"Articles","previous_headings":"","what":"Create a control method","title":"Creating a New Estimation Method","text":"control method gives access controls required estimation.","code":""},{"path":"/articles/running-monlix.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-monolix-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run Monolix from a nlmixr2 model","title":"Running Monolix","text":"use Monolix nlmixr2, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run Monolix. setup lixoftConnectors package Monolix, setup needed. Instead run Monolix command line grid processing (example) can figure command run Monolix (often useful use full command path set options, ie options(\"babelmixr2.monolix\"=\"monolix\") use monolixControl(runCommand=\"monolix\"). needed, prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-monlix.html","id":"step-1-run-a-nlmixr2-in-monolix","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in Monolix","title":"Running Monolix","text":"Lets take classic warfarin example. model use nlmixr2 vignettes : monolix run, can run nlmixr2 model using Monolix new estimation method: fit issues informational tidbit - monolix parameter history needs exported charts, please export charts automatically generated well lixoftConnectors package generated recent version Monolix. don’t information important parameter history plots imported see plots. Just like NONMEM translation, monolixControl() modelName helps control output directory Monolix (specified babelmixr2 tries guess based model name based input). Printing nlmixr2 fit see: particular interest comparison Monolix predictions nlmixr predictions. case, believe also imply models predicting thing. Note model predictions close NONMEM Monolix use lsoda ODE solver. Hence small deviation expected, still gives validated Monolix model.","code":"pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } fit <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"monolix\", monolixControl(modelName=\"pk.turnover.emax3\")) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ assuming monolix is running because 'pk.turnover.emax3-monolix.txt' is present #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> ℹ monolix parameter history needs exported charts, please export charts fit #> ── nlmixr² monolix ver 2021R1 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> monolix 1522.704 2448.398 2527.819 -1205.199 2203.836 #> Condition#(Cor) #> monolix 2.697324 #> #> ── Time (sec fit$time): ── #> #> setup table compress other #> elapsed 0.002762 0.134 0.006 4.450238 #> #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV% or SD) #> tktr 0.218 0.179 82 1.24 (0.876, 1.77) 84.0 #> tka 0.00533 0.117 2.19e+03 1.01 (0.8, 1.26) 48.6 #> tcl -2.01 0.0518 2.58 0.135 (0.122, 0.149) 28.5 #> tv 2.04 0.0438 2.14 7.73 (7.09, 8.42) 22.6 #> prop.err 0.0986 0.0986 #> pkadd.err 0.533 0.533 #> temax 4.46 0.527 11.8 0.989 (0.969, 0.996) 0.380 #> tec50 0.0786 0.0889 113 1.08 (0.909, 1.29) 47.8 #> tkout -2.94 0.0261 0.888 0.053 (0.0503, 0.0558) 7.87 #> te0 4.57 0.0114 0.249 96.7 (94.5, 98.9) 5.08 #> pdadd.err 3.79 3.79 #> Shrink(SD)% #> tktr 47.9% #> tka 48.9% #> tcl 1.25% #> tv 6.09% #> prop.err #> pkadd.err #> temax 91.9% #> tec50 6.29% #> tkout 36.6% #> te0 19.9% #> pdadd.err #> #> Covariance Type (fit$covMethod): MonolixLin #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink #> Censoring (fit$censInformation): No censoring #> Minimization message (fit$message): #> IPRED relative difference compared to Monolix IPRED: 0.09%; 95% percentile: (0.01%,0.49%); rtol=0.000941 #> PRED relative difference compared to Monolix PRED: 0.04%; 95% percentile: (0%,0.2%); rtol=0.000428 #> IPRED absolute difference compared to Monolix IPRED: atol=0.00911; 95% percentile: (0.000493, 0.0928) #> PRED absolute difference compared to Monolix PRED: atol=0.000428; 95% percentile: (3.14e-07, 0.203) #> monolix model: 'pk.turnover.emax3-monolix.mlxtran' #> #> ── Fit Data (object fit is a modified tibble): ── #> # A tibble: 483 × 35 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.ktr eta.ka eta.cl #> #> 1 1 0.5 cp 0 1.40 -1.40 0.500 -0.500 -0.934 -0.638 -0.447 0.689 #> 2 1 1 cp 1.9 3.94 -2.04 1.62 0.284 0.511 -0.638 -0.447 0.689 #> 3 1 2 cp 3.3 8.30 -5.00 4.29 -0.987 -1.45 -0.638 -0.447 0.689 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v , eta.emax , eta.ec50 , #> # eta.kout , eta.e0 , cp , depot , gut , #> # center , effect , ktr , ka , cl , v , #> # emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum "},{"path":"/articles/running-monlix.html","id":"optional-step-2-add-conditional-weighted-residualsfocei-objf-to-monolix","dir":"Articles","previous_headings":"","what":"Optional Step 2: Add conditional weighted residuals/focei objf to Monolix","title":"Running Monolix","text":"case NONMEM, gives things available Monolix, like adding conditional weighted residuals: add nlmixr’s CWRES well adding nlmixr2 FOCEi objective function now objective function compared based assumptions, compare performance Monolix NONMEM based objective function. fair, objective function values must always used caution. model performs predicts data far valuable.","code":"fit <- addCwres(fit) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(R²)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done"},{"path":"/articles/running-monlix.html","id":"optional-step-3-use-nlmixr2-for-vpc-reporting-etc-","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 for vpc, reporting, etc.","title":"Running Monolix","text":"Also since nlmixr2 object easy perform VPC :","code":"v1s <- vpcPlot(fit, show=list(obs_dv=TRUE), scales=\"free_y\") + ylab(\"Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ v2s <- vpcPlot(fit, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") + ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") v1s v2s"},{"path":"/articles/running-monlix.html","id":"notes-about-monolix-data-translation","dir":"Articles","previous_headings":"","what":"Notes about Monolix data translation","title":"Running Monolix","text":"input dataset expected compatible rxode2 nlmixr2. dataset converted Monolix format: combination CMT Dose type creates unique ADM variable. ADM definition saved monolix model file babelmixr2 creates macro describing compartment, ie compartment(cmt=#, amount=stateName) babelmixr2 also creates macro type dosing: Bolus/infusion uses depot() adds modeled lag time (Tlag) bioavailability (p) specified Modeled rate uses depot() Tk0=amtDose/rate. babelmixr2 also adds modeled lag time (Tlag) bioavailability (p) specified Modeled duration uses depot() Tk0=dur, also add adds modeled lag time (Tlag) bioavailability (p) specified Turning compartment uses empty macro","code":""},{"path":"/articles/running-nonmem.html","id":"step-0-what-do-you-need-to-do-to-have-nlmixr2-run-nonmem-from-a-nlmixr2-model","dir":"Articles","previous_headings":"","what":"Step 0: What do you need to do to have nlmixr2 run NONMEM from a nlmixr2 model","title":"Running NONMEM with nlmixr2","text":"use NONMEM nlmixr, need change data nlmixr2 dataset. babelmixr2 heavy lifting . need setup run NONMEM. many cases easy; simply figure command run NONMEM (often useful use full command path). can set options(\"babelmixr2.nonmem\"=\"nmfe743\") use nonmemControl(runCommand=\"nmfe743\"). prefer options() method since need set . also function prefer (cover using function ).","code":""},{"path":"/articles/running-nonmem.html","id":"step-1-run-a-nlmixr2-in-nonmem","dir":"Articles","previous_headings":"","what":"Step 1: Run a nlmixr2 in NONMEM","title":"Running NONMEM with nlmixr2","text":"Lets take classic warfarin example start comparison. model use nlmixr2 vignettes : Now can run nlmixr2 model using NONMEM simply can run directly: way run ordinary nlmixr2 model, simply new estimation method \"nonmem\" new controller (nonmemControl()) setup options estimation. options nonmemControl() modelName helps control output directory NONMEM (specified babelmixr2 tries guess based model name based input). try , see NONMEM fails rounding errors. standard approach changing sigdig, sigl, tol etc, get successful NONMEM model convergence, course supported. babelmixr2 can .","code":"library(babelmixr2) pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } try(nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\", nonmemControl(readRounding=FALSE, modelName=\"pk.turnover.emax3\")), silent=TRUE) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION TERMINATED #> DUE TO ROUNDING ERRORS (ERROR=134) #> NO. OF FUNCTION EVALUATIONS USED: 1088 #> NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #> #> nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl' #> → terminated with rounding errors, can force nlmixr2/rxode2 to read with nonmemControl(readRounding=TRUE) #> Error : nonmem minimization not successful"},{"path":"/articles/running-nonmem.html","id":"optional-step-2-recover-a-failed-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 2: Recover a failed NONMEM run","title":"Running NONMEM with nlmixr2","text":"One approaches ignore rounding errors occurred read nlmixr2 anyway: may see work happening expected need already completed model. reading NONMEM model, babelmixr2 grabs: NONMEM’s objective function value NONMEM’s covariance (available) NONMEM’s optimization history NONMEM’s final parameter estimates (including ETAs) NONMEM’s PRED IPRED values (validation purposes) used solve ODEs came nlmixr2 optimization procedure. means can compare IPRED PRED values nlmixr2/rxode2 know immediately model validates. similar procedure Kyle Baron advocates validating NONMEM model mrgsolve model (see https://mrgsolve.org/blog/posts/2022-05-validate-translation/ https://mrgsolve.org/blog/posts/2023-update-validation.html), advantage method need simply write one model get validated roxde2/nlmixr2 model. case can see validation print fit object: shows preds ipreds match NONMEM nlmixr2 quite well.","code":"# Can still load the model to get information (possibly pipe) and create a new model f <- nlmixr(pk.turnover.emax3, nlmixr2data::warfarin, \"nonmem\", nonmemControl(readRounding=TRUE, modelName=\"pk.turnover.emax3\")) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 3.0.2 using 2 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 5536 print(f) #> ── nlmixr² nonmem ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem focei 1326.91 2252.605 2332.025 -1107.302 NA #> Condition#(Cor) #> nonmem focei NA #> #> ── Time (sec $time): ── #> #> setup table compress NONMEM #> elapsed 0.033752 0.094 0.01 320.27 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> #> Est. Back-transformed BSV(CV% or SD) Shrink(SD)% #> tktr 6.24e-07 1 86.5 59.8% #> tka -3.01e-06 1 86.5 59.8% #> tcl -2 0.135 28.6 1.34% #> tv 2.05 7.78 22.8 6.44% #> prop.err 0.0986 0.0986 #> pkadd.err 0.512 0.512 #> temax 6.42 0.998 0.00707 100.% #> tec50 0.141 1.15 45.0 6.06% #> tkout -2.95 0.0522 9.16 32.4% #> te0 4.57 96.6 5.24 18.1% #> pdadd.err 3.72 3.72 #> #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink #> Information about run found ($runInfo): #> • NONMEM terminated due to rounding errors, but reading into nlmixr2/rxode2 anyway #> Censoring ($censInformation): No censoring #> Minimization message ($message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION TERMINATED #> DUE TO ROUNDING ERRORS (ERROR=134) #> NO. OF FUNCTION EVALUATIONS USED: 1088 #> NO. OF SIG. DIGITS UNREPORTABLE #> 0PARAMETER ESTIMATE IS NEAR ITS BOUNDARY #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.36e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.08e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.53e-06, 0.000502); atol=7.15e-05 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (3.79e-07,0.00321); atol=6.08e-06 #> there are solving errors during optimization (see '$prderr') #> nonmem model: 'pk.turnover.emax3-nonmem/pk.turnover.emax3.nmctl' #> #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 483 × 35 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.ktr eta.ka eta.cl #> #> 1 1 0.5 cp 0 1.16 -1.16 0.444 -0.444 -0.864 -0.506 -0.506 0.699 #> 2 1 1 cp 1.9 3.37 -1.47 1.45 0.446 0.840 -0.506 -0.506 0.699 #> 3 1 2 cp 3.3 7.51 -4.21 3.96 -0.660 -1.03 -0.506 -0.506 0.699 #> # ℹ 480 more rows #> # ℹ 23 more variables: eta.v , eta.emax , eta.ec50 , #> # eta.kout , eta.e0 , cp , depot , gut , #> # center , effect , ktr , ka , cl , v , #> # emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum "},{"path":"/articles/running-nonmem.html","id":"optional-step-3-use-nlmixr2-to-help-understand-why-nonmem-failed","dir":"Articles","previous_headings":"","what":"Optional Step 3: Use nlmixr2 to help understand why NONMEM failed","title":"Running NONMEM with nlmixr2","text":"Since nlmixr2 fit, can interesting things fit couldn’t NONMEM even another translator. example, wanted add covariance step can getVarCov(): nlmixr2 generous constitutes covariance step. r,s covariance matrix “” successful covariance step focei, system fall back methods necessary. covariance matrix r,s, regarded caution, can still give us clues things working NONMEM. examining fit, can see shrinkage high temax, tktr tka, dropped, making things likely converge NONMEM.","code":"getVarCov(f) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> calculating covariance matrix #> [====|====|====|====|====|====|====|====|====|====] 0:00:06 #> Warning in foceiFitCpp_(.ret): using R matrix to calculate covariance, can #> check sandwich or S matrix with $covRS and $covS #> Warning in foceiFitCpp_(.ret): gradient problems with covariance; see #> $scaleInfo #> → compress origData in nlmixr2 object, save 27560 #> Updated original fit object f #> tktr tka tcl tv temax #> tktr 1.821078e-02 -1.512272e-02 -2.550343e-05 3.216116e-04 0.0015410335 #> tka -1.512272e-02 1.815814e-02 -1.992622e-05 3.175474e-04 0.0010345827 #> tcl -2.550343e-05 -1.992622e-05 2.477225e-04 1.181659e-05 -0.0008009162 #> tv 3.216116e-04 3.175474e-04 1.181659e-05 3.184497e-04 0.0010914727 #> temax 1.541033e-03 1.034583e-03 -8.009162e-04 1.091473e-03 7.5815740647 #> tec50 1.410716e-04 1.273505e-04 -3.578298e-04 1.229707e-04 0.0483191718 #> tkout 1.023011e-04 1.011022e-04 -9.757882e-05 1.188260e-04 -0.0189641465 #> te0 1.310259e-05 1.399880e-05 -9.833068e-06 1.232683e-05 -0.0004365713 #> tec50 tkout te0 #> tktr 0.0001410716 1.023011e-04 1.310259e-05 #> tka 0.0001273505 1.011022e-04 1.399880e-05 #> tcl -0.0003578298 -9.757882e-05 -9.833068e-06 #> tv 0.0001229707 1.188260e-04 1.232683e-05 #> temax 0.0483191718 -1.896415e-02 -4.365713e-04 #> tec50 0.0018345990 1.544065e-04 -1.357629e-04 #> tkout 0.0001544065 6.320302e-04 5.220487e-05 #> te0 -0.0001357629 5.220487e-05 8.843897e-05"},{"path":"/articles/running-nonmem.html","id":"optional-step-4-use-model-piping-to-get-a-successful-nonmem-run","dir":"Articles","previous_headings":"","what":"Optional Step 4: Use model piping to get a successful NONMEM run","title":"Running NONMEM with nlmixr2","text":"use model piping remove parameters, new run start last model’s best estimates (saving bunch model development time). case, specify output directory pk.turnover.emax4 control get following: can see NONMEM run now successful validates rxode2 model : One thing emphasize: unlike translators, know immediately translation model validate. Hence can start process confidence - know immediately something wrong. related converting NONMEM nlmixr2 fit. Since nlmixr2 object easy perform VPC (true NONMEM models):","code":"f2 <- f %>% model(ktr <- exp(tktr)) %>% model(ka <- exp(tka)) %>% model(emax = expit(temax)) %>% nlmixr(data=nlmixr2data::warfarin, est=\"nonmem\", control=nonmemControl(readRounding=FALSE, modelName=\"pk.turnover.emax4\")) #> ! remove between subject variability `eta.ktr` #> ! remove between subject variability `eta.ka` #> ! remove between subject variability `eta.emax` #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|==== #> ====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 27560 #> → compress parHistData in nlmixr2 object, save 8800 f2 #> ── nlmixr² nonmem ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem focei 1418.923 2338.618 2405.498 -1153.309 1.852796e+16 #> Condition#(Cor) #> nonmem focei 18934770 #> #> ── Time (sec f2$time): ── #> #> setup table compress NONMEM #> elapsed 0.003488 0.103 0.01 505.59 #> #> ── Population Parameters (f2$parFixed or f2$parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV%) #> tktr 6.24e-07 9.05e-05 1.45e+04 1 (1, 1) #> tka -3.57e-06 0.000153 4.29e+03 1 (1, 1) #> tcl -1.99 0.0639 3.2 0.136 (0.12, 0.154) 27.6 #> tv 2.05 2.66 130 7.76 (0.042, 1.44e+03) 23.6 #> prop.err 0.161 0.161 #> pkadd.err 0.571 0.571 #> temax 9.98 4.96 49.7 1 (0.565, 1) #> tec50 0.131 1.61 1.23e+03 1.14 (0.0489, 26.6) 43.6 #> tkout -2.96 28.3 954 0.0517 (4.63e-26, 5.77e+22) 8.63 #> te0 4.57 0.411 9 96.7 (43.2, 217) 5.19 #> pdadd.err 3.59 3.59 #> Shrink(SD)% #> tktr #> tka #> tcl 3.19% #> tv 10.7% #> prop.err #> pkadd.err #> temax #> tec50 7.12% #> tkout 33.8% #> te0 17.2% #> pdadd.err #> #> Covariance Type (f2$covMethod): nonmem.r,s #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance (f2$omega) or correlation (f2$omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in f2$shrink #> Censoring (f2$censInformation): No censoring #> Minimization message (f2$message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION SUCCESSFUL #> HOWEVER, PROBLEMS OCCURRED WITH THE MINIMIZATION. #> REGARD THE RESULTS OF THE ESTIMATION STEP CAREFULLY, AND ACCEPT THEM ONLY #> AFTER CHECKING THAT THE COVARIANCE STEP PRODUCES REASONABLE OUTPUT. #> NO. OF FUNCTION EVALUATIONS USED: 2391 #> NO. OF SIG. DIGITS IN FINAL EST.: 4.1 #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.85e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.45e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.89e-06, 0.000506); atol=7.19e-05 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (5.14e-07,0.00318); atol=6.45e-06 #> nonmem model: 'pk.turnover.emax4-nonmem/pk.turnover.emax4.nmctl' #> #> ── Fit Data (object f2 is a modified tibble): ── #> # A tibble: 483 × 32 #> ID TIME CMT DV PRED RES IPRED IRES IWRES eta.cl eta.v eta.ec50 #> #> 1 1 0.5 cp 0 1.16 -1.16 0.920 -0.920 -1.56 0.689 0.228 0.160 #> 2 1 1 cp 1.9 3.38 -1.48 2.68 -0.780 -1.09 0.689 0.228 0.160 #> 3 1 2 cp 3.3 7.53 -4.23 5.94 -2.64 -2.36 0.689 0.228 0.160 #> # ℹ 480 more rows #> # ℹ 20 more variables: eta.kout , eta.e0 , cp , depot , #> # gut , center , effect , ktr , ka , cl , #> # v , emax , ec50 , kout , e0 , DCP , PD , #> # kin , tad , dosenum v1s <- vpcPlot(f2, show=list(obs_dv=TRUE), scales=\"free_y\") + ylab(\"Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ v2s <- vpcPlot(f2, show=list(obs_dv=TRUE), pred_corr = TRUE, scales=\"free_y\") + ylab(\"Prediction Corrected Warfarin Cp [mg/L] or PCA\") + xlab(\"Time [h]\") library() v1s v2s"},{"path":"/articles/running-pknca.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Obtain initial estimates and unit conversions with PKNCA","text":"Initial estimates compartmental population PK model can obtained using babelmixr2 \"pknca\" estimation method. Also, central compartment scaling factor can auto-generated based units dosing, concentration measurement, desired volume distribution units, time. need perform NCA analysis hand; \"pknca\" estimation method perform NCA analysis using PKNCA package automatically. methods used converting NCA calculations parameter estimates described help nlmixr2Est.pknca().","code":""},{"path":"/articles/running-pknca.html","id":"initial-example","dir":"Articles","previous_headings":"","what":"Initial example","title":"Obtain initial estimates and unit conversions with PKNCA","text":"Initial model setup nlmixr2 model. must load babelmixr2 library nlmixr() function recognizes est = \"pknca\". use PKNCA get initial estimates, use est = \"pknca\" instead one nlmixr2 estimation methods. unit conversions, provide units control = pkncaControl() argument. Unit conversions supported units can automatically converted; mass/volume can converted mass/volume ratio, mass molar molar mass single mass--molar conversion factor. Now, prepared model updated initial estimates NCA results embedded. can see new model PKNCA estimates looking prepared$ui (model interpreted rxode2) prepared$nca (PKNCAresults object). Note new model, fixed effect initial estimates changed original values. residual error -subject variability unchanged. updated model, can perform estimation new model object, model created nlmixr2:","code":"library(babelmixr2) #> Loading required package: nlmixr2 #> Loading required package: nlmixr2data one.compartment <- function() { ini({ tka <- log(1.57); label(\"Ka (1/hr)\") tcl <- log(2.72); label(\"Cl (L/hr)\") tv <- log(31.5); label(\"V (L)\") eta.ka ~ 0.6 eta.cl ~ 0.3 eta.v ~ 0.1 add.sd <- 0.7; label(\"additive residual error (mg/L)\") }) # and a model block with the error specification and model specification model({ ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) vc <- exp(tv + eta.v) d/dt(depot) <- -ka * depot d/dt(center) <- ka * depot - cl / vc * center cp <- center / vc cp ~ add(add.sd) }) } prepared <- nlmixr2( one.compartment, data = theo_sd, est = \"pknca\", control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\") ) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> Loading required namespace: testthat #> ℹ change initial estimate (0.89314878960486) and upper/lower bound (-3.50655789731998 to 3.72508541597241) of `tka` #> → significant model change detected #> → removed from model: '$getSplitModel' #> ℹ change initial estimate (8.41044546236311) and upper/lower bound (5.51439905878865 to 10.899462850803) of `tcl` #> ℹ change initial estimate (10.5377244826318) and upper/lower bound (7.94567233496473 to 13.1050053785005) of `tv` prepared$ui #> ── rxode2-based free-form 2-cmt ODE model ────────────────────────────────────── #> ── Initalization: ── #> Fixed Effects ($theta): #> tka tcl tv add.sd #> 0.8931488 8.4104455 10.5377245 0.7000000 #> #> Omega ($omega): #> eta.ka eta.cl eta.v #> eta.ka 0.6 0.0 0.0 #> eta.cl 0.0 0.3 0.0 #> eta.v 0.0 0.0 0.1 #> #> States ($state or $stateDf): #> Compartment Number Compartment Name #> 1 1 depot #> 2 2 center #> ── μ-referencing ($muRefTable): ── #> theta eta level #> 1 tka eta.ka id #> 2 tcl eta.cl id #> 3 tv eta.v id #> #> ── Model (Normalized Syntax): ── #> function() { #> ini({ #> tka <- c(-3.50655789731998, 0.89314878960486, 3.72508541597241) #> label(\"Ka (1/hr)\") #> tcl <- c(5.51439905878865, 8.41044546236311, 10.899462850803) #> label(\"Cl (L/hr)\") #> tv <- c(7.94567233496473, 10.5377244826318, 13.1050053785005) #> label(\"V (L)\") #> add.sd <- c(0, 0.7) #> label(\"additive residual error (mg/L)\") #> eta.ka ~ 0.6 #> eta.cl ~ 0.3 #> eta.v ~ 0.1 #> }) #> model({ #> ka <- exp(tka + eta.ka) #> cl <- exp(tcl + eta.cl) #> vc <- exp(tv + eta.v) #> d/dt(depot) <- -ka * depot #> d/dt(center) <- ka * depot - cl/vc * center #> cp <- 1000 * center/vc #> cp ~ add(add.sd) #> }) #> } knitr::knit_print( summary(prepared$nca) ) #> Interval Start Interval End N AUClast (hr*ng/mL) Cmax (ng/mL) #> 0 24 12 74.6 [24.3] . #> 0 Inf 12 . 8.65 [17.0] #> Tmax (hr) CL (based on AUClast) (mg/(hr*ng/mL)) #> . 4.22 [23.0] #> 1.14 [0.630, 3.55] . #> Vss (based on AUClast) (mg/(ng/mL)) Half-life (hr) AUCinf,obs (hr*ng/mL) #> 25.0 [18.5] . . #> . 8.18 [2.12] 115 [28.4] #> Cmax (dose-normalized) ((ng/mL)/mg) #> . #> 0.0274 [18.1] #> #> Caption: AUClast, Cmax, CL (based on AUClast), Vss (based on AUClast), AUCinf,obs, Cmax (dose-normalized): geometric mean and geometric coefficient of variation; Tmax: median and range; Half-life: arithmetic mean and standard deviation; N: number of subjects fit <- nlmixr(prepared, data = theo_sd, est = \"focei\", control = list(print = 0)) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → calculate jacobian #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate sensitivities #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(f)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → calculate ∂(R²)/∂(η) #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in inner model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → finding duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in EBE model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling inner model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → finding duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → optimizing duplicate expressions in FD model... #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> → compiling EBE model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> → compiling events FD model... #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 3.0.2 using 2 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> calculating covariance matrix #> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #> done #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 5952 #> → compress parHistData in nlmixr2 object, save 10632 fit #> ── nlmixr² FOCEi (outer: nlminb) ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) Condition#(Cor) #> FOCEi 116.9548 373.5546 393.7342 -179.7773 66.5244 12.97786 #> #> ── Time (sec fit$time): ── #> #> setup optimize covariance table compress other #> elapsed 0.093825 0.490602 0.490603 0.112 0.009 7.52497 #> #> ── Population Parameters (fit$parFixed or fit$parFixedDf): ── #> #> Parameter Est. SE %RSE #> tka Ka (1/hr) 0.469 0.224 47.6 #> tcl Cl (L/hr) 7.92 0.0929 1.17 #> tv V (L) 10.4 0.0602 0.581 #> add.sd additive residual error (mg/L) 0.697 #> Back-transformed(95%CI) BSV(CV%) Shrink(SD)% #> tka 1.6 (1.03, 2.48) 68.0 -0.666% #> tcl 2.75e+03 (2.29e+03, 3.3e+03) 26.1 3.95% #> tv 3.19e+04 (2.83e+04, 3.59e+04) 15.4 14.5% #> add.sd 0.697 #> #> Covariance Type (fit$covMethod): r,s #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink #> Information about run found (fit$runInfo): #> • gradient problems with initial estimate and covariance; see $scaleInfo #> • last objective function was not at minimum, possible problems in optimization #> • ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.)) #> • initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=)) #> Censoring (fit$censInformation): No censoring #> Minimization message (fit$message): #> false convergence (8) #> In an ODE system, false convergence may mean \"useless\" evaluations were performed. #> See https://tinyurl.com/yyrrwkce #> It could also mean the convergence is poor, check results before accepting fit #> You may also try a good derivative free optimization: #> nlmixr2(...,control=list(outerOpt=\"bobyqa\")) #> #> ── Fit Data (object fit is a modified tibble): ── #> # A tibble: 132 × 22 #> ID TIME DV PRED RES WRES IPRED IRES IWRES CPRED CRES CWRES #> #> 1 1 0 0.74 0 0.74 1.06 0 0.74 1.06 0 0.74 1.06 #> 2 1 0.25 2.84 3.27 -0.432 -0.234 3.84 -1.00 -1.44 3.23 -0.389 -0.185 #> 3 1 0.57 6.57 5.84 0.730 0.297 6.78 -0.215 -0.308 5.78 0.786 0.287 #> # ℹ 129 more rows #> # ℹ 10 more variables: eta.ka , eta.cl , eta.v , depot , #> # center , ka , cl , vc , tad , dosenum "},{"path":"/articles/running-pknca.html","id":"give-pknca-a-different-dataset-or-a-completed-nca-analysis","dir":"Articles","previous_headings":"","what":"Give PKNCA a different dataset or a completed NCA analysis","title":"Obtain initial estimates and unit conversions with PKNCA","text":"get initial estimate, babelmixr2 automatically converts modeling dataset format needed PKNCA, NCA automatically performed using data. cases (e.g. studies sparse data), NCA may feasible. cases, can provide different dataset PKNCA compared full modeling dataset. Usually, simplest method provide single-dose, dense-sampling, dose-ranging data (.e. single-ascending dose portion first--human study) estimated. , give data PKNCA using ncaData argument pkncaControl() follows: initial estimates now based NCA calculated dNCA dataset rather full theo_sd dataset. already NCA results calculated PKNCA required parameters (“tmax”, “cmax.dn”, “cllast”), can provide instead using pkncaControl(ncaResults) argument.","code":"# Choose a subset of the full dataset for NCA dNCA <- theo_sd[theo_sd$ID <= 6, ] preparedNcaData <- nlmixr2( one.compartment, data = theo_sd, est = \"pknca\", control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\", ncaData = dNCA) ) #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ change initial estimate (0.929027077269762) and upper/lower bound (-3.50655789731998 to 3.32136703319919) of `tka` #> → significant model change detected #> → removed from model: '$getSplitModel' #> ℹ change initial estimate (8.3955404628088) and upper/lower bound (5.85241523541802 to 10.7637056987378) of `tcl` #> ℹ change initial estimate (10.5377244826318) and upper/lower bound (7.94370069836702 to 13.1024358787022) of `tv` preparedNcaData$ui #> ── rxode2-based free-form 2-cmt ODE model ────────────────────────────────────── #> ── Initalization: ── #> Fixed Effects ($theta): #> tka tcl tv add.sd #> 0.9290271 8.3955405 10.5377245 0.7000000 #> #> Omega ($omega): #> eta.ka eta.cl eta.v #> eta.ka 0.6 0.0 0.0 #> eta.cl 0.0 0.3 0.0 #> eta.v 0.0 0.0 0.1 #> #> States ($state or $stateDf): #> Compartment Number Compartment Name #> 1 1 depot #> 2 2 center #> ── μ-referencing ($muRefTable): ── #> theta eta level #> 1 tka eta.ka id #> 2 tcl eta.cl id #> 3 tv eta.v id #> #> ── Model (Normalized Syntax): ── #> function() { #> ini({ #> tka <- c(-3.50655789731998, 0.929027077269762, 3.32136703319919) #> label(\"Ka (1/hr)\") #> tcl <- c(5.85241523541802, 8.3955404628088, 10.7637056987378) #> label(\"Cl (L/hr)\") #> tv <- c(7.94370069836702, 10.5377244826318, 13.1024358787022) #> label(\"V (L)\") #> add.sd <- c(0, 0.7) #> label(\"additive residual error (mg/L)\") #> eta.ka ~ 0.6 #> eta.cl ~ 0.3 #> eta.v ~ 0.1 #> }) #> model({ #> ka <- exp(tka + eta.ka) #> cl <- exp(tcl + eta.cl) #> vc <- exp(tv + eta.v) #> d/dt(depot) <- -ka * depot #> d/dt(center) <- ka * depot - cl/vc * center #> cp <- 1000 * center/vc #> cp ~ add(add.sd) #> }) #> }"},{"path":"/articles/running-pknca.html","id":"model-requirements","dir":"Articles","previous_headings":"","what":"Model requirements","title":"Obtain initial estimates and unit conversions with PKNCA","text":"update initial estimates, model must parameters model() block names expected est = \"pknca\". expected names : ka vc cl vp vp2 q q2 parameter names found model() block automatically traced back initial conditions (ini() block), parameter values updated. parameter estimated log scale, updated parameter value automatically converted log scale.","code":""},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthew Fidler. Author, maintainer. Bill Denney. Author. Theodoros Papathanasiou. Contributor. Nook Fulloption. Contributor. goldfish art","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Fidler M, Xiong Y, Schoemaker R, Wilkins J, Trame M, Hooijmaijers R, Post T, Wang W (2024). nlmixr: Nonlinear Mixed Effects Models Population Pharmacokinetics Pharmacodynamics. R package version 0.1.5, https://CRAN.R-project.org/package=nlmixr. Fidler M, Wilkins J, Hooijmaijers R, Post T, Schoemaker R, Trame M, Xiong Y, Wang W (2019). “Nonlinear Mixed-Effects Model Development Simulation Using nlmixr Related R Open-Source Packages.” CPT: Pharmacometrics & Systems Pharmacology, 8(9), 621–633. Schoemaker R, Fidler M, Laveille C, Wilkins J, Hooijmaijers R, Post T, Trame M, Xiong Y, Wang W (2019). “Performance SAEM FOCEI Algorithms Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr.” CPT: Pharmacometrics & Systems Pharmacology, 8(12), 923–930.","code":"@Manual{, title = {{nlmixr}: Nonlinear Mixed Effects Models in Population Pharmacokinetics and Pharmacodynamics}, author = {Matthew Fidler and Yuan Xiong and Rik Schoemaker and Justin Wilkins and Mirjam Trame and Richard Hooijmaijers and Teun Post and Wenping Wang}, year = {2024}, note = {R package version 0.1.5}, url = {https://CRAN.R-project.org/package=nlmixr}, } @Article{, title = {Nonlinear Mixed-Effects Model Development and Simulation Using nlmixr and Related R Open-Source Packages}, author = {Matthew Fidler and Justin Wilkins and Richard Hooijmaijers and Teun Post and Rik Schoemaker and Mirjam Trame and Yuan Xiong and Wenping Wang}, journal = {CPT: Pharmacometrics \\& Systems Pharmacology}, year = {2019}, volume = {8}, pages = {621--633}, number = {9}, month = {sep}, abstract = {nlmixr is a free and open-source R package for fitting nonlinear pharmacokinetic (PK), pharmacodynamic (PD), joint PK-PD, and quantitative systems pharmacology mixed-effects models. Currently, nlmixr is capable of fitting both traditional compartmental PK models as well as more complex models implemented using ordinary differential equations. We believe that, over time, it will become a capable, credible alternative to commercial software tools, such as NONMEM, Monolix, and Phoenix NLME.}, address = {Hoboken}, publisher = {John Wiley and Sons Inc.}, } @Article{, title = {Performance of the SAEM and FOCEI Algorithms in the Open-Source, Nonlinear Mixed Effect Modeling Tool nlmixr}, author = {Rik Schoemaker and Matthew Fidler and Christian Laveille and Justin Wilkins and Richard Hooijmaijers and Teun Post and Mirjam Trame and Yuan Xiong and Wenping Wang}, journal = {CPT: Pharmacometrics \\& Systems Pharmacology}, year = {2019}, volume = {8}, pages = {923--930}, number = {12}, month = {dec}, abstract = {The free and open-source package nlmixr implements pharmacometric nonlinear mixed effects model parameter estimation in R. It provides a uniform language to define pharmacometric models using ordinary differential equations. Performances of the stochastic approximation expectation-maximization (SAEM) and first order-conditional estimation with interaction (FOCEI) algorithms in nlmixr were compared with those found in the industry standards, Monolix and NONMEM, using the following two scenarios: a simple model fit to 500 sparsely sampled data sets and a range of more complex compartmental models with linear and nonlinear clearance fit to data sets with rich sampling. Estimation results obtained from nlmixr for FOCEI and SAEM matched the corresponding output from NONMEM/FOCEI and Monolix/SAEM closely both in terms of parameter estimates and associated standard errors. These results indicate that nlmixr may provide a viable alternative to existing tools for pharmacometric parameter estimation.}, }"},{"path":"/index.html","id":"babelmixr2","dir":"","previous_headings":"","what":"Use nlmixr2 to Interact with Open Source and Commercial Software","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"goal babelmixr2 convert nlmixr2 syntax commonly used tools.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"can install released version babelmixr2 CRAN : can install r-universe : Otherwise can always install GitHub:","code":"install.packages(\"babelmixr2\") # Download and install babelmixr2 in R install.packages('babelmixr2', repos = c( nlmixr2 = 'https://nlmixr2.r-universe.dev', CRAN = 'https://cloud.r-project.org'))"},{"path":"/index.html","id":"what-you-can-do-with-babelmixr2","dir":"","previous_headings":"","what":"What you can do with babelmixr2","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"Babelmixr2 can help : Running nlmixr2 model commercial nonlinear mixed effects modeling tool like NONMEM Monolix Convert NONMEM model nlmixr2 model (conjunction nonmem2rx) Convert Monolix model nlmixr2 model (conjunction monolix2rx) Calculate scaling factors automatically add initial conditions based non-compartmental analysis (using PKNCA) Perform Optimal design using nlmixr2 interface PopED","code":""},{"path":"/index.html","id":"monolix-setup","dir":"","previous_headings":"","what":"Monolix Setup","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"required, can get/install R ‘lixoftConnectors’ package ‘Monolix’ installation, described following url https://monolixsuite.slp-software.com/r-functions/2024R1/installation--initialization. ‘lixoftConnectors’ available, R can run ‘Monolix’ directly instead using command line.","code":""},{"path":"/index.html","id":"pknca-example","dir":"","previous_headings":"","what":"PKNCA Example","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"installed, use standard interface, can obtain new initial estimates PKNCA:","code":"mod <- nlmixr2( nlmixrFun, nlmmixrData, est = \"pknca\", control = pkncaControl(concu = \"ng/mL\", doseu = \"mg\", timeu = \"hr\", volumeu = \"L\") )"},{"path":"/index.html","id":"monolix-example","dir":"","previous_headings":"","what":"Monolix example","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"babelmixr2 loaded, can use nlmixr2 convert nlmixr2 model Monolix, run monolix, import back nlmixr2 following:","code":"mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"monolix\")"},{"path":"/index.html","id":"nonmem-example","dir":"","previous_headings":"","what":"NONMEM example","title":"Use nlmixr2 to Interact with Open Source and Commercial Software","text":"babelmixr2 loaded can use nlmixr2 convert nlmixr2 model NONMEM, run NONMEM import back nlmixr2 following:","code":"mod <- nlmixr(nlmixrFun, nlmmixrData, est=\"nonmem\")"},{"path":"/reference/as.nlmixr2.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an object to a nlmixr2 fit object — as.nlmixr2","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Convert object nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"as.nlmixr2( x, ..., table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), ci = 0.95 ) as.nlmixr( x, ..., table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), ci = 0.95 )"},{"path":"/reference/as.nlmixr2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"x Object convert ... arguments table nlmixr2est::tableControl() options rxControl rxode2::rxControl() options, generally needed addl doses handled translation ci confidence interval residual differences calculated (default 0.95)","code":""},{"path":"/reference/as.nlmixr2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"nlmixr2 fit object","code":""},{"path":"/reference/as.nlmixr2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"Matthew L. Fidler","code":""},{"path":"/reference/as.nlmixr2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert an object to a nlmixr2 fit object — as.nlmixr2","text":"","code":"# \\donttest{ # First read in the model (but without residuals) mod <- nonmem2rx(system.file(\"mods/cpt/runODE032.ctl\", package=\"nonmem2rx\"), determineError=FALSE, lst=\".res\", save=FALSE) #> ℹ getting information from '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl' #> ℹ reading in xml file #> ℹ done #> ℹ reading in ext file #> ℹ done #> ℹ reading in phi file #> ℹ done #> ℹ reading in lst file #> ℹ abbreviated list parsing #> ℹ done #> ℹ done #> ℹ splitting control stream by records #> ℹ done #> ℹ Processing record $INPUT #> ℹ Processing record $MODEL #> ℹ Processing record $gTHETA #> ℹ Processing record $OMEGA #> ℹ Processing record $SIGMA #> ℹ Processing record $PROBLEM #> ℹ Processing record $DATA #> ℹ Processing record $SUBROUTINES #> ℹ Processing record $PK #> ℹ Processing record $DES #> ℹ Processing record $ERROR #> ℹ Processing record $ESTIMATION #> ℹ Ignore record $ESTIMATION #> ℹ Processing record $COVARIANCE #> ℹ Ignore record $COVARIANCE #> ℹ Processing record $TABLE #> ℹ change initial estimate of `theta1` to `1.37034036528946` #> ℹ change initial estimate of `theta2` to `4.19814911033061` #> ℹ change initial estimate of `theta3` to `1.38003493562413` #> ℹ change initial estimate of `theta4` to `3.87657341967489` #> ℹ change initial estimate of `theta5` to `0.196446108190896` #> ℹ change initial estimate of `eta1` to `0.101251418415006` #> ℹ change initial estimate of `eta2` to `0.0993872449483344` #> ℹ change initial estimate of `eta3` to `0.101302674763154` #> ℹ change initial estimate of `eta4` to `0.0730497519364148` #> ℹ read in nonmem input data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/Bolus_2CPT.csv #> ℹ ignoring lines that begin with a letter (IGNORE=@)' #> ℹ applying names specified by $INPUT #> ℹ subsetting accept/ignore filters code: .data[-which((.data$SD == 0)),] #> ℹ done #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ read in nonmem IPRED data (for model validation): /home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.csv #> ℹ done #> ℹ changing most variables to lower case #> ℹ done #> ℹ replace theta names #> ℹ done #> ℹ replace eta names #> ℹ done (no labels) #> ℹ renaming compartments #> ℹ done #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done # define the model with residuals (and change the name of the # parameters) In this step you need to be careful to not change the # estimates and make sure the residual estimates are correct (could # have to change var to sd). mod2 <-function() { ini({ lcl <- 1.37034036528946 lvc <- 4.19814911033061 lq <- 1.38003493562413 lvp <- 3.87657341967489 RSV <- c(0, 0.196446108190896, 1) eta.cl ~ 0.101251418415006 eta.v ~ 0.0993872449483344 eta.q ~ 0.101302674763154 eta.v2 ~ 0.0730497519364148 }) model({ cmt(CENTRAL) cmt(PERI) cl <- exp(lcl + eta.cl) v <- exp(lvc + eta.v) q <- exp(lq + eta.q) v2 <- exp(lvp + eta.v2) v1 <- v scale1 <- v k21 <- q/v2 k12 <- q/v d/dt(CENTRAL) <- k21 * PERI - k12 * CENTRAL - cl * CENTRAL/v1 d/dt(PERI) <- -k21 * PERI + k12 * CENTRAL f <- CENTRAL/scale1 f ~ prop(RSV) }) } # now we create another nonmem2rx object that validates the model above: new <- as.nonmem2rx(mod2, mod) #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ℹ copy 'dfSub' to nonmem2rx model #> ℹ copy 'thetaMat' to nonmem2rx model #> ℹ copy 'dfObs' to nonmem2rx model #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ℹ solving ipred problem #> ℹ done #> ℹ solving pred problem #> ℹ done # once that is done, you can translate to a full nlmixr2 fit (if you wish) fit <- as.nlmixr2(new) #> → loading into symengine environment... #> → pruning branches (`if`/`else`) of full model... #> ✔ done #> → finding duplicate expressions in EBE model... #> → optimizing duplicate expressions in EBE model... #> → compiling EBE model... #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> ✔ done #> rxode2 3.0.2 using 2 threads (see ?getRxThreads) #> no cache: create with `rxCreateCache()` #> → Calculating residuals/tables #> ✔ done #> → compress origData in nlmixr2 object, save 204016 #> → compress parHistData in nlmixr2 object, save 2176 print(fit) #> ── nlmixr² nonmem2rx reading NONMEM ver 7.4.3 ── #> #> OBJF AIC BIC Log-likelihood Condition#(Cov) #> nonmem2rx 15977.28 20185.64 20237.23 -10083.82 335.4129 #> Condition#(Cor) #> nonmem2rx 2.096559 #> #> ── Time (sec $time): ── #> #> setup table compress NONMEM as.nlmixr2 #> elapsed 0.038921 0.138 0.019 100.95 2.886 #> #> ── Population Parameters ($parFixed or $parFixedDf): ── #> #> Est. SE %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)% #> lcl 1.37 0.0298 2.17 3.94 (3.71, 4.17) 32.6 1.94% #> lvc 4.2 0.0295 0.703 66.6 (62.8, 70.5) 32.3 2.46% #> lq 1.38 0.0547 3.96 3.98 (3.57, 4.42) 32.7 40.5% #> lvp 3.88 0.0348 0.899 48.3 (45.1, 51.7) 27.5 28.4% #> RSV 0.196 0.196 #> #> Covariance Type ($covMethod): nonmem2rx #> No correlations in between subject variability (BSV) matrix #> Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs) #> Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink #> Censoring ($censInformation): No censoring #> Minimization message ($message): #> #> #> WARNINGS AND ERRORS (IF ANY) FOR PROBLEM 1 #> #> (WARNING 2) NM-TRAN INFERS THAT THE DATA ARE POPULATION. #> #> #> 0MINIMIZATION SUCCESSFUL #> NO. OF FUNCTION EVALUATIONS USED: 320 #> NO. OF SIG. DIGITS IN FINAL EST.: 2.5 #> #> IPRED relative difference compared to Nonmem IPRED: 0%; 95% percentile: (0%,0%); rtol=6.43e-06 #> PRED relative difference compared to Nonmem PRED: 0%; 95% percentile: (0%,0%); rtol=6.41e-06 #> IPRED absolute difference compared to Nonmem IPRED: 95% percentile: (2.25e-05, 0.0418); atol=0.00167 #> PRED absolute difference compared to Nonmem PRED: 95% percentile: (1.41e-07,0.00382); atol=6.41e-06 #> nonmem2rx model file: '/home/runner/work/_temp/Library/nonmem2rx/mods/cpt/runODE032.ctl' #> #> ── Fit Data (object is a modified tibble): ── #> # A tibble: 2,280 × 25 #> ID TIME DV PRED RES IPRED IRES IWRES eta.cl eta.v eta.q eta.v2 #> #> 1 1 0.25 1041. 1750. -710. 1215. -175. -0.732 -0.144 0.375 0.0650 0.241 #> 2 1 0.5 1629 1700. -70.8 1192. 437. 1.87 -0.144 0.375 0.0650 0.241 #> 3 1 0.75 878. 1651. -774. 1169. -291. -1.27 -0.144 0.375 0.0650 0.241 #> # ℹ 2,277 more rows #> # ℹ 13 more variables: f , CENTRAL , PERI , cl , v , #> # q , v2 , v1 , scale1 , k21 , k12 , tad , #> # dosenum # }"},{"path":"/reference/babel.poped.database.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand a babelmixr2 PopED database — babel.poped.database","title":"Expand a babelmixr2 PopED database — babel.poped.database","text":"Expand babelmixr2 PopED database","code":""},{"path":"/reference/babel.poped.database.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand a babelmixr2 PopED database — babel.poped.database","text":"","code":"babel.poped.database(popedInput, ..., optTime = NA)"},{"path":"/reference/babel.poped.database.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand a babelmixr2 PopED database — babel.poped.database","text":"popedInput babelmixr2 generated PopED database ... parameters sent PopED::create.poped.database() optTime boolean indicate global time indexer inside babelmixr2 reset times different. default TRUE. FALSE can get slightly better run times possibly slightly different results. optTime FALSE global indexer reset every time PopED rxode2 setup problem poped dataset created. can manually reset popedMultipleEndpointResetTimeIndex()","code":""},{"path":"/reference/babel.poped.database.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expand a babelmixr2 PopED database — babel.poped.database","text":"babelmixr2 PopED database ($babelmixr2 database)","code":""},{"path":"/reference/babel.poped.database.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Expand a babelmixr2 PopED database — babel.poped.database","text":"Matthew L. Fidler","code":""},{"path":"/reference/babelBpopIdx.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"may work poped databases population parameters named.","code":""},{"path":"/reference/babelBpopIdx.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"","code":"babelBpopIdx(popedInput, var)"},{"path":"/reference/babelBpopIdx.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"popedInput babelmixr2 created database var variable query","code":""},{"path":"/reference/babelBpopIdx.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"index variable","code":""},{"path":"/reference/babelBpopIdx.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"Matthew L. Fidler","code":""},{"path":"/reference/babelBpopIdx.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get the bpop_idx by variable name for a poped database created by babelmixr2 — babelBpopIdx","text":"","code":"if (requireNamespace(\"PopED\", quietly=TRUE)) { f <- function() { ini({ tV <- 72.8 tKa <- 0.25 tCl <- 3.75 tF <- fix(0.9) pedCL <- 0.8 eta.v ~ 0.09 eta.ka ~ 0.09 eta.cl ~0.25^2 prop.sd <- fix(sqrt(0.04)) add.sd <- fix(sqrt(5e-6)) }) model({ V<-tV*exp(eta.v) KA<-tKa*exp(eta.ka) * (pedCL**isPediatric) # add covariate for pediatrics CL<-tCl*exp(eta.cl) Favail <- tF N <- floor(t/TAU)+1 y <- (DOSE*Favail/V)*(KA/(KA - CL/V)) * (exp(-CL/V * (t - (N - 1) * TAU)) * (1 - exp(-N * CL/V * TAU))/(1 - exp(-CL/V * TAU)) - exp(-KA * (t - (N - 1) * TAU)) * (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU))) y ~ prop(prop.sd) + add(add.sd) }) } e <- et(c( 1,8,10,240,245)) babel.db <- nlmixr2(f, e, \"poped\", popedControl(m = 2, groupsize=20, bUseGrouped_xt=TRUE, a=list(c(DOSE=20,TAU=24,isPediatric = 0), c(DOSE=40, TAU=24,isPediatric = 0)))) babelBpopIdx(babel.db, \"pedCL\") } #> #> #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> #> #> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’ #> [1] 4"},{"path":"/reference/bblDatToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Convert nlmixr2-compatible data formats (possible)","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"bblDatToMonolix( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToNonmem( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToRxode( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToMrgsolve( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL ) bblDatToPknca( model, data, table = nlmixr2est::tableControl(), rxControl = rxode2::rxControl(), env = NULL )"},{"path":"/reference/bblDatToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"model rxode2 model conversion data Input dataset. table table control; mostly figure additional columns keep. rxControl rxode2 control options; figure handle addl dosing information. env NULL (default) nothing done. environment, function nlmixr2est::.foceiPreProcessData(data, env, model, rxControl) called provided environment.","code":""},{"path":"/reference/bblDatToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"function bblDatToMonolix() return list : Monolix compatible dataset ($monolix) Monolix ADM information ($adm) function nlmixrDataToNonmem() return dataset compatible NONMEM. function nlmixrDataToMrgsolve() return dataset compatible mrgsolve. Unlike NONMEM, supports replacement events evid=8 (note rxode2 replacement evid 5). function nlmixrDataToRxode() normalize dataset use newer evid definitions closer NONMEM instead classic definitions used lower level","code":""},{"path":"/reference/bblDatToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"Matthew L. Fidler","code":""},{"path":"/reference/bblDatToMonolix.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Convert nlmixr2-compatible data to other formats (if possible) — bblDatToMonolix","text":"","code":"pk.turnover.emax3 <- function() { ini({ tktr <- log(1) tka <- log(1) tcl <- log(0.1) tv <- log(10) ## eta.ktr ~ 1 eta.ka ~ 1 eta.cl ~ 2 eta.v ~ 1 prop.err <- 0.1 pkadd.err <- 0.1 ## temax <- logit(0.8) tec50 <- log(0.5) tkout <- log(0.05) te0 <- log(100) ## eta.emax ~ .5 eta.ec50 ~ .5 eta.kout ~ .5 eta.e0 ~ .5 ## pdadd.err <- 10 }) model({ ktr <- exp(tktr + eta.ktr) ka <- exp(tka + eta.ka) cl <- exp(tcl + eta.cl) v <- exp(tv + eta.v) emax = expit(temax+eta.emax) ec50 = exp(tec50 + eta.ec50) kout = exp(tkout + eta.kout) e0 = exp(te0 + eta.e0) ## DCP = center/v PD=1-emax*DCP/(ec50+DCP) ## effect(0) = e0 kin = e0*kout ## d/dt(depot) = -ktr * depot d/dt(gut) = ktr * depot -ka * gut d/dt(center) = ka * gut - cl / v * center d/dt(effect) = kin*PD -kout*effect ## cp = center / v cp ~ prop(prop.err) + add(pkadd.err) effect ~ add(pdadd.err) | pca }) } bblDatToMonolix(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> $monolix #> ID TIME EVID AMT II DV ADM YTYPE SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 0 1 0 2 #> 3 1 1.0 0 NA 0 1.9 0 1 0 3 #> 4 1 2.0 0 NA 0 3.3 0 1 0 4 #> 5 1 3.0 0 NA 0 6.6 0 1 0 5 #> 6 1 6.0 0 NA 0 9.1 0 1 0 6 #> 7 1 9.0 0 NA 0 10.8 0 1 0 7 #> 8 1 12.0 0 NA 0 8.6 0 1 0 8 #> 9 1 24.0 0 NA 0 5.6 0 1 0 9 #> 10 1 24.0 0 NA 0 44.0 0 2 0 10 #> 11 1 36.0 0 NA 0 4.0 0 1 0 11 #> 12 1 36.0 0 NA 0 27.0 0 2 0 12 #> 13 1 48.0 0 NA 0 2.7 0 1 0 13 #> 14 1 48.0 0 NA 0 28.0 0 2 0 14 #> 15 1 72.0 0 NA 0 0.8 0 1 0 15 #> 16 1 72.0 0 NA 0 31.0 0 2 0 16 #> 17 1 96.0 0 NA 0 60.0 0 2 0 17 #> 18 1 120.0 0 NA 0 65.0 0 2 0 18 #> 19 1 144.0 0 NA 0 71.0 0 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 0 2 0 21 #> 22 2 24.0 0 NA 0 9.2 0 1 0 22 #> 23 2 24.0 0 NA 0 49.0 0 2 0 23 #> 24 2 36.0 0 NA 0 8.5 0 1 0 24 #> 25 2 36.0 0 NA 0 32.0 0 2 0 25 #> 26 2 48.0 0 NA 0 6.4 0 1 0 26 #> 27 2 48.0 0 NA 0 26.0 0 2 0 27 #> 28 2 72.0 0 NA 0 4.8 0 1 0 28 #> 29 2 72.0 0 NA 0 22.0 0 2 0 29 #> 30 2 96.0 0 NA 0 3.1 0 1 0 30 #> 31 2 96.0 0 NA 0 28.0 0 2 0 31 #> 32 2 120.0 0 NA 0 2.5 0 1 0 32 #> 33 2 120.0 0 NA 0 33.0 0 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 0 2 0 35 #> 36 3 0.5 0 NA 0 0.0 0 1 0 36 #> 37 3 2.0 0 NA 0 8.4 0 1 0 37 #> 38 3 3.0 0 NA 0 9.7 0 1 0 38 #> 39 3 6.0 0 NA 0 9.8 0 1 0 39 #> 40 3 12.0 0 NA 0 11.0 0 1 0 40 #> 41 3 24.0 0 NA 0 8.3 0 1 0 41 #> 42 3 24.0 0 NA 0 46.0 0 2 0 42 #> 43 3 36.0 0 NA 0 7.7 0 1 0 43 #> 44 3 36.0 0 NA 0 22.0 0 2 0 44 #> 45 3 48.0 0 NA 0 6.3 0 1 0 45 #> 46 3 48.0 0 NA 0 19.0 0 2 0 46 #> 47 3 72.0 0 NA 0 4.1 0 1 0 47 #> 48 3 72.0 0 NA 0 20.0 0 2 0 48 #> 49 3 96.0 0 NA 0 3.0 0 1 0 49 #> 50 3 96.0 0 NA 0 42.0 0 2 0 50 #> 51 3 120.0 0 NA 0 1.4 0 1 0 51 #> 52 3 120.0 0 NA 0 49.0 0 2 0 52 #> 53 3 144.0 0 NA 0 54.0 0 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 0 2 0 55 #> 56 4 3.0 0 NA 0 12.0 0 1 0 56 #> 57 4 6.0 0 NA 0 13.2 0 1 0 57 #> 58 4 9.0 0 NA 0 14.4 0 1 0 58 #> 59 4 24.0 0 NA 0 9.6 0 1 0 59 #> 60 4 24.0 0 NA 0 30.0 0 2 0 60 #> 61 4 36.0 0 NA 0 8.2 0 1 0 61 #> 62 4 36.0 0 NA 0 24.0 0 2 0 62 #> 63 4 48.0 0 NA 0 7.8 0 1 0 63 #> 64 4 48.0 0 NA 0 13.0 0 2 0 64 #> 65 4 72.0 0 NA 0 5.8 0 1 0 65 #> 66 4 72.0 0 NA 0 9.0 0 2 0 66 #> 67 4 96.0 0 NA 0 4.3 0 1 0 67 #> 68 4 96.0 0 NA 0 9.0 0 2 0 68 #> 69 4 120.0 0 NA 0 3.0 0 1 0 69 #> 70 4 120.0 0 NA 0 11.0 0 2 0 70 #> 71 4 144.0 0 NA 0 12.0 0 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 0 2 0 73 #> 74 5 3.0 0 NA 0 11.1 0 1 0 74 #> 75 5 6.0 0 NA 0 11.9 0 1 0 75 #> 76 5 9.0 0 NA 0 9.8 0 1 0 76 #> 77 5 12.0 0 NA 0 11.0 0 1 0 77 #> 78 5 24.0 0 NA 0 8.5 0 1 0 78 #> 79 5 24.0 0 NA 0 43.0 0 2 0 79 #> 80 5 36.0 0 NA 0 7.6 0 1 0 80 #> 81 5 36.0 0 NA 0 25.0 0 2 0 81 #> 82 5 48.0 0 NA 0 5.4 0 1 0 82 #> 83 5 48.0 0 NA 0 18.0 0 2 0 83 #> 84 5 72.0 0 NA 0 4.5 0 1 0 84 #> 85 5 72.0 0 NA 0 17.0 0 2 0 85 #> 86 5 96.0 0 NA 0 3.3 0 1 0 86 #> 87 5 96.0 0 NA 0 23.0 0 2 0 87 #> 88 5 120.0 0 NA 0 2.3 0 1 0 88 #> 89 5 120.0 0 NA 0 29.0 0 2 0 89 #> 90 5 144.0 0 NA 0 41.0 0 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 0 2 0 92 #> 93 6 6.0 0 NA 0 8.6 0 1 0 93 #> 94 6 12.0 0 NA 0 8.6 0 1 0 94 #> 95 6 24.0 0 NA 0 7.0 0 1 0 95 #> 96 6 24.0 0 NA 0 34.0 0 2 0 96 #> 97 6 36.0 0 NA 0 5.7 0 1 0 97 #> 98 6 36.0 0 NA 0 23.0 0 2 0 98 #> 99 6 48.0 0 NA 0 4.7 0 1 0 99 #> 100 6 48.0 0 NA 0 20.0 0 2 0 100 #> 101 6 72.0 0 NA 0 3.3 0 1 0 101 #> 102 6 72.0 0 NA 0 16.0 0 2 0 102 #> 103 6 96.0 0 NA 0 2.3 0 1 0 103 #> 104 6 96.0 0 NA 0 17.0 0 2 0 104 #> 105 6 120.0 0 NA 0 1.7 0 1 0 105 #> 106 6 120.0 0 NA 0 18.0 0 2 0 106 #> 107 6 144.0 0 NA 0 25.0 0 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 0 1 0 109 #> 110 7 6.0 0 NA 0 12.4 0 1 0 110 #> 111 7 9.0 0 NA 0 12.7 0 1 0 111 #> 112 7 12.0 0 NA 0 8.8 0 1 0 112 #> 113 7 24.0 0 NA 0 6.1 0 1 0 113 #> 114 7 24.0 0 NA 0 36.0 0 2 0 114 #> 115 7 36.0 0 NA 0 3.5 0 1 0 115 #> 116 7 36.0 0 NA 0 33.0 0 2 0 116 #> 117 7 48.0 0 NA 0 1.8 0 1 0 117 #> 118 7 48.0 0 NA 0 28.0 0 2 0 118 #> 119 7 72.0 0 NA 0 1.5 0 1 0 119 #> 120 7 72.0 0 NA 0 52.0 0 2 0 120 #> 121 7 96.0 0 NA 0 1.0 0 1 0 121 #> 122 7 96.0 0 NA 0 80.0 0 2 0 122 #> 123 7 120.0 0 NA 0 90.0 0 2 0 123 #> 124 7 144.0 0 NA 0 100.0 0 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 0 2 0 126 #> 127 8 2.0 0 NA 0 17.6 0 1 0 127 #> 128 8 3.0 0 NA 0 17.3 0 1 0 128 #> 129 8 6.0 0 NA 0 15.0 0 1 0 129 #> 130 8 9.0 0 NA 0 15.0 0 1 0 130 #> 131 8 12.0 0 NA 0 12.4 0 1 0 131 #> 132 8 24.0 0 NA 0 7.9 0 1 0 132 #> 133 8 24.0 0 NA 0 35.0 0 2 0 133 #> 134 8 36.0 0 NA 0 7.9 0 1 0 134 #> 135 8 36.0 0 NA 0 20.0 0 2 0 135 #> 136 8 48.0 0 NA 0 5.1 0 1 0 136 #> 137 8 48.0 0 NA 0 12.0 0 2 0 137 #> 138 8 72.0 0 NA 0 3.6 0 1 0 138 #> 139 8 72.0 0 NA 0 16.0 0 2 0 139 #> 140 8 96.0 0 NA 0 2.4 0 1 0 140 #> 141 8 96.0 0 NA 0 23.0 0 2 0 141 #> 142 8 120.0 0 NA 0 2.0 0 1 0 142 #> 143 8 120.0 0 NA 0 36.0 0 2 0 143 #> 144 8 144.0 0 NA 0 48.0 0 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 0 2 0 146 #> 147 9 0.5 0 NA 0 0.0 0 1 0 147 #> 148 9 1.0 0 NA 0 1.0 0 1 0 148 #> 149 9 2.0 0 NA 0 4.6 0 1 0 149 #> 150 9 3.0 0 NA 0 12.7 0 1 0 150 #> 151 9 3.0 0 NA 0 8.0 0 1 0 151 #> 152 9 6.0 0 NA 0 12.7 0 1 0 152 #> 153 9 6.0 0 NA 0 11.5 0 1 0 153 #> 154 9 9.0 0 NA 0 12.9 0 1 0 154 #> 155 9 9.0 0 NA 0 11.4 0 1 0 155 #> 156 9 12.0 0 NA 0 11.4 0 1 0 156 #> 157 9 12.0 0 NA 0 11.0 0 1 0 157 #> 158 9 24.0 0 NA 0 9.1 0 1 0 158 #> 159 9 24.0 0 NA 0 33.0 0 2 0 159 #> 160 9 36.0 0 NA 0 8.2 0 1 0 160 #> 161 9 36.0 0 NA 0 22.0 0 2 0 161 #> 162 9 48.0 0 NA 0 5.9 0 1 0 162 #> 163 9 48.0 0 NA 0 16.0 0 2 0 163 #> 164 9 72.0 0 NA 0 3.6 0 1 0 164 #> 165 9 72.0 0 NA 0 18.0 0 2 0 165 #> 166 9 96.0 0 NA 0 1.7 0 1 0 166 #> 167 9 96.0 0 NA 0 32.0 0 2 0 167 #> 168 9 120.0 0 NA 0 1.1 0 1 0 168 #> 169 9 120.0 0 NA 0 30.0 0 2 0 169 #> 170 9 144.0 0 NA 0 45.0 0 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 0 2 0 172 #> 173 10 24.0 0 NA 0 8.6 0 1 0 173 #> 174 10 24.0 0 NA 0 39.0 0 2 0 174 #> 175 10 36.0 0 NA 0 8.0 0 1 0 175 #> 176 10 36.0 0 NA 0 22.0 0 2 0 176 #> 177 10 48.0 0 NA 0 6.0 0 1 0 177 #> 178 10 48.0 0 NA 0 17.0 0 2 0 178 #> 179 10 72.0 0 NA 0 4.4 0 1 0 179 #> 180 10 72.0 0 NA 0 17.0 0 2 0 180 #> 181 10 96.0 0 NA 0 3.6 0 1 0 181 #> 182 10 96.0 0 NA 0 22.0 0 2 0 182 #> 183 10 120.0 0 NA 0 2.8 0 1 0 183 #> 184 10 120.0 0 NA 0 25.0 0 2 0 184 #> 185 10 144.0 0 NA 0 33.0 0 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 0 2 0 187 #> 188 11 1.5 0 NA 0 11.4 0 1 0 188 #> 189 11 3.0 0 NA 0 15.4 0 1 0 189 #> 190 11 6.0 0 NA 0 17.5 0 1 0 190 #> 191 11 12.0 0 NA 0 14.0 0 1 0 191 #> 192 11 24.0 0 NA 0 9.0 0 1 0 192 #> 193 11 24.0 0 NA 0 37.0 0 2 0 193 #> 194 11 36.0 0 NA 0 8.9 0 1 0 194 #> 195 11 36.0 0 NA 0 24.0 0 2 0 195 #> 196 11 48.0 0 NA 0 6.6 0 1 0 196 #> 197 11 48.0 0 NA 0 14.0 0 2 0 197 #> 198 11 72.0 0 NA 0 4.2 0 1 0 198 #> 199 11 72.0 0 NA 0 11.0 0 2 0 199 #> 200 11 96.0 0 NA 0 3.6 0 1 0 200 #> 201 11 96.0 0 NA 0 14.0 0 2 0 201 #> 202 11 120.0 0 NA 0 2.6 0 1 0 202 #> 203 11 120.0 0 NA 0 23.0 0 2 0 203 #> 204 11 144.0 0 NA 0 33.0 0 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 0 2 0 206 #> 207 12 1.5 0 NA 0 0.6 0 1 0 207 #> 208 12 3.0 0 NA 0 2.8 0 1 0 208 #> 209 12 6.0 0 NA 0 13.8 0 1 0 209 #> 210 12 9.0 0 NA 0 15.0 0 1 0 210 #> 211 12 24.0 0 NA 0 10.5 0 1 0 211 #> 212 12 24.0 0 NA 0 25.0 0 2 0 212 #> 213 12 36.0 0 NA 0 9.1 0 1 0 213 #> 214 12 36.0 0 NA 0 15.0 0 2 0 214 #> 215 12 48.0 0 NA 0 6.6 0 1 0 215 #> 216 12 48.0 0 NA 0 11.0 0 2 0 216 #> 217 12 72.0 0 NA 0 4.9 0 1 0 217 #> 218 12 96.0 0 NA 0 2.4 0 1 0 218 #> 219 12 120.0 0 NA 0 1.9 0 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 0 2 0 221 #> 222 13 1.5 0 NA 0 3.6 0 1 0 222 #> 223 13 3.0 0 NA 0 12.9 0 1 0 223 #> 224 13 6.0 0 NA 0 12.9 0 1 0 224 #> 225 13 9.0 0 NA 0 10.2 0 1 0 225 #> 226 13 24.0 0 NA 0 6.4 0 1 0 226 #> 227 13 24.0 0 NA 0 41.0 0 2 0 227 #> 228 13 36.0 0 NA 0 6.9 0 1 0 228 #> 229 13 36.0 0 NA 0 23.0 0 2 0 229 #> 230 13 48.0 0 NA 0 4.5 0 1 0 230 #> 231 13 48.0 0 NA 0 16.0 0 2 0 231 #> 232 13 72.0 0 NA 0 3.2 0 1 0 232 #> 233 13 72.0 0 NA 0 14.0 0 2 0 233 #> 234 13 96.0 0 NA 0 2.4 0 1 0 234 #> 235 13 96.0 0 NA 0 18.0 0 2 0 235 #> 236 13 120.0 0 NA 0 1.3 0 1 0 236 #> 237 13 120.0 0 NA 0 22.0 0 2 0 237 #> 238 13 144.0 0 NA 0 35.0 0 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 0 2 0 240 #> 241 14 0.5 0 NA 0 0.0 0 1 0 241 #> 242 14 1.0 0 NA 0 2.7 0 1 0 242 #> 243 14 2.0 0 NA 0 11.6 0 1 0 243 #> 244 14 3.0 0 NA 0 11.6 0 1 0 244 #> 245 14 6.0 0 NA 0 11.3 0 1 0 245 #> 246 14 9.0 0 NA 0 9.7 0 1 0 246 #> 247 14 24.0 0 NA 0 6.5 0 1 0 247 #> 248 14 24.0 0 NA 0 32.0 0 2 0 248 #> 249 14 36.0 0 NA 0 5.2 0 1 0 249 #> 250 14 36.0 0 NA 0 22.0 0 2 0 250 #> 251 14 48.0 0 NA 0 3.6 0 1 0 251 #> 252 14 48.0 0 NA 0 21.0 0 2 0 252 #> 253 14 72.0 0 NA 0 2.4 0 1 0 253 #> 254 14 72.0 0 NA 0 28.0 0 2 0 254 #> 255 14 96.0 0 NA 0 0.9 0 1 0 255 #> 256 14 96.0 0 NA 0 38.0 0 2 0 256 #> 257 14 120.0 0 NA 0 46.0 0 2 0 257 #> 258 14 144.0 0 NA 0 65.0 0 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 0 2 0 260 #> 261 15 1.0 0 NA 0 6.6 0 1 0 261 #> 262 15 3.0 0 NA 0 11.9 0 1 0 262 #> 263 15 6.0 0 NA 0 11.7 0 1 0 263 #> 264 15 9.0 0 NA 0 12.2 0 1 0 264 #> 265 15 24.0 0 NA 0 8.1 0 1 0 265 #> 266 15 24.0 0 NA 0 43.0 0 2 0 266 #> 267 15 36.0 0 NA 0 7.4 0 1 0 267 #> 268 15 36.0 0 NA 0 26.0 0 2 0 268 #> 269 15 48.0 0 NA 0 6.8 0 1 0 269 #> 270 15 48.0 0 NA 0 15.0 0 2 0 270 #> 271 15 72.0 0 NA 0 5.3 0 1 0 271 #> 272 15 72.0 0 NA 0 13.0 0 2 0 272 #> 273 15 96.0 0 NA 0 3.0 0 1 0 273 #> 274 15 96.0 0 NA 0 21.0 0 2 0 274 #> 275 15 120.0 0 NA 0 2.0 0 1 0 275 #> 276 15 120.0 0 NA 0 28.0 0 2 0 276 #> 277 15 144.0 0 NA 0 39.0 0 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 0 2 0 279 #> 280 16 24.0 0 NA 0 10.4 0 1 0 280 #> 281 16 24.0 0 NA 0 42.0 0 2 0 281 #> 282 16 36.0 0 NA 0 8.9 0 1 0 282 #> 283 16 36.0 0 NA 0 32.0 0 2 0 283 #> 284 16 48.0 0 NA 0 7.0 0 1 0 284 #> 285 16 48.0 0 NA 0 26.0 0 2 0 285 #> 286 16 72.0 0 NA 0 4.4 0 1 0 286 #> 287 16 72.0 0 NA 0 31.0 0 2 0 287 #> 288 16 96.0 0 NA 0 3.2 0 1 0 288 #> 289 16 96.0 0 NA 0 33.0 0 2 0 289 #> 290 16 120.0 0 NA 0 2.4 0 1 0 290 #> 291 16 120.0 0 NA 0 54.0 0 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 0 2 0 293 #> 294 17 24.0 0 NA 0 7.6 0 1 0 294 #> 295 17 24.0 0 NA 0 35.0 0 2 0 295 #> 296 17 36.0 0 NA 0 6.4 0 1 0 296 #> 297 17 36.0 0 NA 0 23.0 0 2 0 297 #> 298 17 48.0 0 NA 0 6.0 0 1 0 298 #> 299 17 48.0 0 NA 0 17.0 0 2 0 299 #> 300 17 72.0 0 NA 0 4.0 0 1 0 300 #> 301 17 72.0 0 NA 0 18.0 0 2 0 301 #> 302 17 96.0 0 NA 0 3.1 0 1 0 302 #> 303 17 96.0 0 NA 0 18.0 0 2 0 303 #> 304 17 120.0 0 NA 0 2.0 0 1 0 304 #> 305 17 120.0 0 NA 0 21.0 0 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 0 2 0 307 #> 308 18 24.0 0 NA 0 7.6 0 1 0 308 #> 309 18 24.0 0 NA 0 32.0 0 2 0 309 #> 310 18 36.0 0 NA 0 6.6 0 1 0 310 #> 311 18 36.0 0 NA 0 20.0 0 2 0 311 #> 312 18 48.0 0 NA 0 5.4 0 1 0 312 #> 313 18 48.0 0 NA 0 18.0 0 2 0 313 #> 314 18 72.0 0 NA 0 3.4 0 1 0 314 #> 315 18 72.0 0 NA 0 18.0 0 2 0 315 #> 316 18 96.0 0 NA 0 1.2 0 1 0 316 #> 317 18 96.0 0 NA 0 19.0 0 2 0 317 #> 318 18 120.0 0 NA 0 0.9 0 1 0 318 #> 319 18 120.0 0 NA 0 29.0 0 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 0 2 0 321 #> 322 19 24.0 0 NA 0 6.6 0 1 0 322 #> 323 19 24.0 0 NA 0 33.0 0 2 0 323 #> 324 19 36.0 0 NA 0 5.3 0 1 0 324 #> 325 19 36.0 0 NA 0 28.0 0 2 0 325 #> 326 19 48.0 0 NA 0 3.6 0 1 0 326 #> 327 19 48.0 0 NA 0 18.0 0 2 0 327 #> 328 19 72.0 0 NA 0 2.7 0 1 0 328 #> 329 19 72.0 0 NA 0 18.0 0 2 0 329 #> 330 19 96.0 0 NA 0 1.4 0 1 0 330 #> 331 19 96.0 0 NA 0 17.0 0 2 0 331 #> 332 19 120.0 0 NA 0 1.1 0 1 0 332 #> 333 19 120.0 0 NA 0 26.0 0 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 0 2 0 335 #> 336 20 24.0 0 NA 0 9.6 0 1 0 336 #> 337 20 24.0 0 NA 0 41.0 0 2 0 337 #> 338 20 36.0 0 NA 0 8.0 0 1 0 338 #> 339 20 36.0 0 NA 0 30.0 0 2 0 339 #> 340 20 48.0 0 NA 0 6.6 0 1 0 340 #> 341 20 48.0 0 NA 0 22.0 0 2 0 341 #> 342 20 72.0 0 NA 0 5.6 0 1 0 342 #> 343 20 72.0 0 NA 0 23.0 0 2 0 343 #> 344 20 96.0 0 NA 0 3.5 0 1 0 344 #> 345 20 96.0 0 NA 0 23.0 0 2 0 345 #> 346 20 120.0 0 NA 0 2.3 0 1 0 346 #> 347 20 120.0 0 NA 0 35.0 0 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 0 2 0 349 #> 350 21 24.0 0 NA 0 7.3 0 1 0 350 #> 351 21 24.0 0 NA 0 46.0 0 2 0 351 #> 352 21 36.0 0 NA 0 6.1 0 1 0 352 #> 353 21 36.0 0 NA 0 27.0 0 2 0 353 #> 354 21 48.0 0 NA 0 4.3 0 1 0 354 #> 355 21 48.0 0 NA 0 22.0 0 2 0 355 #> 356 21 72.0 0 NA 0 3.2 0 1 0 356 #> 357 21 72.0 0 NA 0 36.0 0 2 0 357 #> 358 21 96.0 0 NA 0 2.3 0 1 0 358 #> 359 21 96.0 0 NA 0 40.0 0 2 0 359 #> 360 21 120.0 0 NA 0 1.9 0 1 0 360 #> 361 21 120.0 0 NA 0 44.0 0 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 0 2 0 363 #> 364 22 24.0 0 NA 0 8.9 0 1 0 364 #> 365 22 24.0 0 NA 0 35.0 0 2 0 365 #> 366 22 36.0 0 NA 0 8.4 0 1 0 366 #> 367 22 36.0 0 NA 0 27.0 0 2 0 367 #> 368 22 48.0 0 NA 0 8.0 0 1 0 368 #> 369 22 48.0 0 NA 0 23.0 0 2 0 369 #> 370 22 72.0 0 NA 0 4.4 0 1 0 370 #> 371 22 72.0 0 NA 0 27.0 0 2 0 371 #> 372 22 96.0 0 NA 0 3.2 0 1 0 372 #> 373 22 96.0 0 NA 0 43.0 0 2 0 373 #> 374 22 120.0 0 NA 0 1.7 0 1 0 374 #> 375 22 120.0 0 NA 0 43.0 0 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 0 2 0 377 #> 378 23 24.0 0 NA 0 9.8 0 1 0 378 #> 379 23 24.0 0 NA 0 34.0 0 2 0 379 #> 380 23 36.0 0 NA 0 8.4 0 1 0 380 #> 381 23 36.0 0 NA 0 24.0 0 2 0 381 #> 382 23 48.0 0 NA 0 6.6 0 1 0 382 #> 383 23 48.0 0 NA 0 15.0 0 2 0 383 #> 384 23 72.0 0 NA 0 4.8 0 1 0 384 #> 385 23 72.0 0 NA 0 15.0 0 2 0 385 #> 386 23 96.0 0 NA 0 3.2 0 1 0 386 #> 387 23 96.0 0 NA 0 19.0 0 2 0 387 #> 388 23 120.0 0 NA 0 2.4 0 1 0 388 #> 389 23 120.0 0 NA 0 19.0 0 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 0 2 0 391 #> 392 24 24.0 0 NA 0 8.2 0 1 0 392 #> 393 24 24.0 0 NA 0 37.0 0 2 0 393 #> 394 24 36.0 0 NA 0 7.5 0 1 0 394 #> 395 24 36.0 0 NA 0 20.0 0 2 0 395 #> 396 24 48.0 0 NA 0 6.8 0 1 0 396 #> 397 24 48.0 0 NA 0 20.0 0 2 0 397 #> 398 24 72.0 0 NA 0 5.5 0 1 0 398 #> 399 24 72.0 0 NA 0 26.0 0 2 0 399 #> 400 24 96.0 0 NA 0 4.5 0 1 0 400 #> 401 24 96.0 0 NA 0 28.0 0 2 0 401 #> 402 24 120.0 0 NA 0 3.7 0 1 0 402 #> 403 24 120.0 0 NA 0 50.0 0 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 0 2 0 405 #> 406 25 24.0 0 NA 0 11.0 0 1 0 406 #> 407 25 24.0 0 NA 0 32.0 0 2 0 407 #> 408 25 36.0 0 NA 0 10.0 0 1 0 408 #> 409 25 36.0 0 NA 0 20.0 0 2 0 409 #> 410 25 48.0 0 NA 0 8.2 0 1 0 410 #> 411 25 48.0 0 NA 0 17.0 0 2 0 411 #> 412 25 72.0 0 NA 0 6.0 0 1 0 412 #> 413 25 72.0 0 NA 0 19.0 0 2 0 413 #> 414 25 96.0 0 NA 0 3.7 0 1 0 414 #> 415 25 96.0 0 NA 0 21.0 0 2 0 415 #> 416 25 120.0 0 NA 0 2.6 0 1 0 416 #> 417 25 120.0 0 NA 0 30.0 0 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 0 2 0 419 #> 420 26 24.0 0 NA 0 10.0 0 1 0 420 #> 421 26 24.0 0 NA 0 41.0 0 2 0 421 #> 422 26 36.0 0 NA 0 9.0 0 1 0 422 #> 423 26 36.0 0 NA 0 28.0 0 2 0 423 #> 424 26 48.0 0 NA 0 7.3 0 1 0 424 #> 425 26 48.0 0 NA 0 19.0 0 2 0 425 #> 426 26 72.0 0 NA 0 5.2 0 1 0 426 #> 427 26 72.0 0 NA 0 17.0 0 2 0 427 #> 428 26 96.0 0 NA 0 3.7 0 1 0 428 #> 429 26 96.0 0 NA 0 17.0 0 2 0 429 #> 430 26 120.0 0 NA 0 2.7 0 1 0 430 #> 431 26 120.0 0 NA 0 24.0 0 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 0 2 0 433 #> 434 27 24.0 0 NA 0 11.8 0 1 0 434 #> 435 27 24.0 0 NA 0 32.0 0 2 0 435 #> 436 27 36.0 0 NA 0 9.2 0 1 0 436 #> 437 27 36.0 0 NA 0 21.0 0 2 0 437 #> 438 27 48.0 0 NA 0 7.7 0 1 0 438 #> 439 27 48.0 0 NA 0 19.0 0 2 0 439 #> 440 27 72.0 0 NA 0 4.9 0 1 0 440 #> 441 27 72.0 0 NA 0 22.0 0 2 0 441 #> 442 27 96.0 0 NA 0 3.4 0 1 0 442 #> 443 27 96.0 0 NA 0 33.0 0 2 0 443 #> 444 27 120.0 0 NA 0 2.7 0 1 0 444 #> 445 27 120.0 0 NA 0 46.0 0 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 0 2 0 447 #> 448 28 24.0 0 NA 0 10.1 0 1 0 448 #> 449 28 24.0 0 NA 0 39.0 0 2 0 449 #> 450 28 36.0 0 NA 0 8.0 0 1 0 450 #> 451 28 36.0 0 NA 0 25.0 0 2 0 451 #> 452 28 48.0 0 NA 0 6.0 0 1 0 452 #> 453 28 48.0 0 NA 0 16.0 0 2 0 453 #> 454 28 72.0 0 NA 0 4.9 0 1 0 454 #> 455 28 72.0 0 NA 0 14.0 0 2 0 455 #> 456 28 96.0 0 NA 0 3.4 0 1 0 456 #> 457 28 96.0 0 NA 0 15.0 0 2 0 457 #> 458 28 120.0 0 NA 0 2.0 0 1 0 458 #> 459 28 120.0 0 NA 0 20.0 0 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 0 2 0 461 #> 462 29 24.0 0 NA 0 8.3 0 1 0 462 #> 463 29 24.0 0 NA 0 35.0 0 2 0 463 #> 464 29 36.0 0 NA 0 7.0 0 1 0 464 #> 465 29 36.0 0 NA 0 21.0 0 2 0 465 #> 466 29 48.0 0 NA 0 5.6 0 1 0 466 #> 467 29 48.0 0 NA 0 18.0 0 2 0 467 #> 468 29 72.0 0 NA 0 4.1 0 1 0 468 #> 469 29 72.0 0 NA 0 20.0 0 2 0 469 #> 470 29 96.0 0 NA 0 3.1 0 1 0 470 #> 471 29 96.0 0 NA 0 29.0 0 2 0 471 #> 472 29 120.0 0 NA 0 2.2 0 1 0 472 #> 473 29 120.0 0 NA 0 41.0 0 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 0 2 0 475 #> 476 30 24.0 0 NA 0 9.9 0 1 0 476 #> 477 30 24.0 0 NA 0 45.0 0 2 0 477 #> 478 30 36.0 0 NA 0 7.5 0 1 0 478 #> 479 30 36.0 0 NA 0 24.0 0 2 0 479 #> 480 30 48.0 0 NA 0 6.5 0 1 0 480 #> 481 30 48.0 0 NA 0 23.0 0 2 0 481 #> 482 30 72.0 0 NA 0 4.1 0 1 0 482 #> 483 30 72.0 0 NA 0 26.0 0 2 0 483 #> 484 30 96.0 0 NA 0 2.9 0 1 0 484 #> 485 30 96.0 0 NA 0 28.0 0 2 0 485 #> 486 30 120.0 0 NA 0 2.3 0 1 0 486 #> 487 30 120.0 0 NA 0 39.0 0 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 0 2 0 489 #> 490 31 24.0 0 NA 0 9.5 0 1 0 490 #> 491 31 24.0 0 NA 0 45.0 0 2 0 491 #> 492 31 36.0 0 NA 0 7.8 0 1 0 492 #> 493 31 36.0 0 NA 0 30.0 0 2 0 493 #> 494 31 48.0 0 NA 0 6.4 0 1 0 494 #> 495 31 48.0 0 NA 0 24.0 0 2 0 495 #> 496 31 72.0 0 NA 0 4.5 0 1 0 496 #> 497 31 72.0 0 NA 0 22.0 0 2 0 497 #> 498 31 96.0 0 NA 0 3.4 0 1 0 498 #> 499 31 96.0 0 NA 0 28.0 0 2 0 499 #> 500 31 120.0 0 NA 0 2.5 0 1 0 500 #> 501 31 120.0 0 NA 0 42.0 0 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 0 2 0 503 #> 504 32 24.0 0 NA 0 8.9 0 1 0 504 #> 505 32 24.0 0 NA 0 36.0 0 2 0 505 #> 506 32 36.0 0 NA 0 7.7 0 1 0 506 #> 507 32 36.0 0 NA 0 27.0 0 2 0 507 #> 508 32 48.0 0 NA 0 6.9 0 1 0 508 #> 509 32 48.0 0 NA 0 24.0 0 2 0 509 #> 510 32 72.0 0 NA 0 4.4 0 1 0 510 #> 511 32 72.0 0 NA 0 23.0 0 2 0 511 #> 512 32 96.0 0 NA 0 3.5 0 1 0 512 #> 513 32 96.0 0 NA 0 20.0 0 2 0 513 #> 514 32 120.0 0 NA 0 2.5 0 1 0 514 #> 515 32 120.0 0 NA 0 22.0 0 2 0 515 #> #> $adm #> adm cmt type #> 1 1 1 bolus #> bblDatToNonmem(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ID TIME EVID AMT DV CMT DVID nlmixrRowNums #> 1 1 0.0 1 100.0 NA 1 NA 1 #> 2 1 0.5 0 NA 0.0 NA 1 2 #> 3 1 1.0 0 NA 1.9 NA 1 3 #> 4 1 2.0 0 NA 3.3 NA 1 4 #> 5 1 3.0 0 NA 6.6 NA 1 5 #> 6 1 6.0 0 NA 9.1 NA 1 6 #> 7 1 9.0 0 NA 10.8 NA 1 7 #> 8 1 12.0 0 NA 8.6 NA 1 8 #> 9 1 24.0 0 NA 5.6 NA 1 9 #> 10 1 24.0 0 NA 44.0 NA 2 10 #> 11 1 36.0 0 NA 4.0 NA 1 11 #> 12 1 36.0 0 NA 27.0 NA 2 12 #> 13 1 48.0 0 NA 2.7 NA 1 13 #> 14 1 48.0 0 NA 28.0 NA 2 14 #> 15 1 72.0 0 NA 0.8 NA 1 15 #> 16 1 72.0 0 NA 31.0 NA 2 16 #> 17 1 96.0 0 NA 60.0 NA 2 17 #> 18 1 120.0 0 NA 65.0 NA 2 18 #> 19 1 144.0 0 NA 71.0 NA 2 19 #> 20 2 0.0 1 100.0 NA 1 NA 20 #> 21 2 0.0 0 NA 100.0 NA 2 21 #> 22 2 24.0 0 NA 9.2 NA 1 22 #> 23 2 24.0 0 NA 49.0 NA 2 23 #> 24 2 36.0 0 NA 8.5 NA 1 24 #> 25 2 36.0 0 NA 32.0 NA 2 25 #> 26 2 48.0 0 NA 6.4 NA 1 26 #> 27 2 48.0 0 NA 26.0 NA 2 27 #> 28 2 72.0 0 NA 4.8 NA 1 28 #> 29 2 72.0 0 NA 22.0 NA 2 29 #> 30 2 96.0 0 NA 3.1 NA 1 30 #> 31 2 96.0 0 NA 28.0 NA 2 31 #> 32 2 120.0 0 NA 2.5 NA 1 32 #> 33 2 120.0 0 NA 33.0 NA 2 33 #> 34 3 0.0 1 100.0 NA 1 NA 34 #> 35 3 0.0 0 NA 100.0 NA 2 35 #> 36 3 0.5 0 NA 0.0 NA 1 36 #> 37 3 2.0 0 NA 8.4 NA 1 37 #> 38 3 3.0 0 NA 9.7 NA 1 38 #> 39 3 6.0 0 NA 9.8 NA 1 39 #> 40 3 12.0 0 NA 11.0 NA 1 40 #> 41 3 24.0 0 NA 8.3 NA 1 41 #> 42 3 24.0 0 NA 46.0 NA 2 42 #> 43 3 36.0 0 NA 7.7 NA 1 43 #> 44 3 36.0 0 NA 22.0 NA 2 44 #> 45 3 48.0 0 NA 6.3 NA 1 45 #> 46 3 48.0 0 NA 19.0 NA 2 46 #> 47 3 72.0 0 NA 4.1 NA 1 47 #> 48 3 72.0 0 NA 20.0 NA 2 48 #> 49 3 96.0 0 NA 3.0 NA 1 49 #> 50 3 96.0 0 NA 42.0 NA 2 50 #> 51 3 120.0 0 NA 1.4 NA 1 51 #> 52 3 120.0 0 NA 49.0 NA 2 52 #> 53 3 144.0 0 NA 54.0 NA 2 53 #> 54 4 0.0 1 120.0 NA 1 NA 54 #> 55 4 0.0 0 NA 100.0 NA 2 55 #> 56 4 3.0 0 NA 12.0 NA 1 56 #> 57 4 6.0 0 NA 13.2 NA 1 57 #> 58 4 9.0 0 NA 14.4 NA 1 58 #> 59 4 24.0 0 NA 9.6 NA 1 59 #> 60 4 24.0 0 NA 30.0 NA 2 60 #> 61 4 36.0 0 NA 8.2 NA 1 61 #> 62 4 36.0 0 NA 24.0 NA 2 62 #> 63 4 48.0 0 NA 7.8 NA 1 63 #> 64 4 48.0 0 NA 13.0 NA 2 64 #> 65 4 72.0 0 NA 5.8 NA 1 65 #> 66 4 72.0 0 NA 9.0 NA 2 66 #> 67 4 96.0 0 NA 4.3 NA 1 67 #> 68 4 96.0 0 NA 9.0 NA 2 68 #> 69 4 120.0 0 NA 3.0 NA 1 69 #> 70 4 120.0 0 NA 11.0 NA 2 70 #> 71 4 144.0 0 NA 12.0 NA 2 71 #> 72 5 0.0 1 60.0 NA 1 NA 72 #> 73 5 0.0 0 NA 82.0 NA 2 73 #> 74 5 3.0 0 NA 11.1 NA 1 74 #> 75 5 6.0 0 NA 11.9 NA 1 75 #> 76 5 9.0 0 NA 9.8 NA 1 76 #> 77 5 12.0 0 NA 11.0 NA 1 77 #> 78 5 24.0 0 NA 8.5 NA 1 78 #> 79 5 24.0 0 NA 43.0 NA 2 79 #> 80 5 36.0 0 NA 7.6 NA 1 80 #> 81 5 36.0 0 NA 25.0 NA 2 81 #> 82 5 48.0 0 NA 5.4 NA 1 82 #> 83 5 48.0 0 NA 18.0 NA 2 83 #> 84 5 72.0 0 NA 4.5 NA 1 84 #> 85 5 72.0 0 NA 17.0 NA 2 85 #> 86 5 96.0 0 NA 3.3 NA 1 86 #> 87 5 96.0 0 NA 23.0 NA 2 87 #> 88 5 120.0 0 NA 2.3 NA 1 88 #> 89 5 120.0 0 NA 29.0 NA 2 89 #> 90 5 144.0 0 NA 41.0 NA 2 90 #> 91 6 0.0 1 113.0 NA 1 NA 91 #> 92 6 0.0 0 NA 100.0 NA 2 92 #> 93 6 6.0 0 NA 8.6 NA 1 93 #> 94 6 12.0 0 NA 8.6 NA 1 94 #> 95 6 24.0 0 NA 7.0 NA 1 95 #> 96 6 24.0 0 NA 34.0 NA 2 96 #> 97 6 36.0 0 NA 5.7 NA 1 97 #> 98 6 36.0 0 NA 23.0 NA 2 98 #> 99 6 48.0 0 NA 4.7 NA 1 99 #> 100 6 48.0 0 NA 20.0 NA 2 100 #> 101 6 72.0 0 NA 3.3 NA 1 101 #> 102 6 72.0 0 NA 16.0 NA 2 102 #> 103 6 96.0 0 NA 2.3 NA 1 103 #> 104 6 96.0 0 NA 17.0 NA 2 104 #> 105 6 120.0 0 NA 1.7 NA 1 105 #> 106 6 120.0 0 NA 18.0 NA 2 106 #> 107 6 144.0 0 NA 25.0 NA 2 107 #> 108 7 0.0 1 90.0 NA 1 NA 108 #> 109 7 3.0 0 NA 13.4 NA 1 109 #> 110 7 6.0 0 NA 12.4 NA 1 110 #> 111 7 9.0 0 NA 12.7 NA 1 111 #> 112 7 12.0 0 NA 8.8 NA 1 112 #> 113 7 24.0 0 NA 6.1 NA 1 113 #> 114 7 24.0 0 NA 36.0 NA 2 114 #> 115 7 36.0 0 NA 3.5 NA 1 115 #> 116 7 36.0 0 NA 33.0 NA 2 116 #> 117 7 48.0 0 NA 1.8 NA 1 117 #> 118 7 48.0 0 NA 28.0 NA 2 118 #> 119 7 72.0 0 NA 1.5 NA 1 119 #> 120 7 72.0 0 NA 52.0 NA 2 120 #> 121 7 96.0 0 NA 1.0 NA 1 121 #> 122 7 96.0 0 NA 80.0 NA 2 122 #> 123 7 120.0 0 NA 90.0 NA 2 123 #> 124 7 144.0 0 NA 100.0 NA 2 124 #> 125 8 0.0 1 135.0 NA 1 NA 125 #> 126 8 0.0 0 NA 88.0 NA 2 126 #> 127 8 2.0 0 NA 17.6 NA 1 127 #> 128 8 3.0 0 NA 17.3 NA 1 128 #> 129 8 6.0 0 NA 15.0 NA 1 129 #> 130 8 9.0 0 NA 15.0 NA 1 130 #> 131 8 12.0 0 NA 12.4 NA 1 131 #> 132 8 24.0 0 NA 7.9 NA 1 132 #> 133 8 24.0 0 NA 35.0 NA 2 133 #> 134 8 36.0 0 NA 7.9 NA 1 134 #> 135 8 36.0 0 NA 20.0 NA 2 135 #> 136 8 48.0 0 NA 5.1 NA 1 136 #> 137 8 48.0 0 NA 12.0 NA 2 137 #> 138 8 72.0 0 NA 3.6 NA 1 138 #> 139 8 72.0 0 NA 16.0 NA 2 139 #> 140 8 96.0 0 NA 2.4 NA 1 140 #> 141 8 96.0 0 NA 23.0 NA 2 141 #> 142 8 120.0 0 NA 2.0 NA 1 142 #> 143 8 120.0 0 NA 36.0 NA 2 143 #> 144 8 144.0 0 NA 48.0 NA 2 144 #> 145 9 0.0 1 75.0 NA 1 NA 145 #> 146 9 0.0 0 NA 92.0 NA 2 146 #> 147 9 0.5 0 NA 0.0 NA 1 147 #> 148 9 1.0 0 NA 1.0 NA 1 148 #> 149 9 2.0 0 NA 4.6 NA 1 149 #> 150 9 3.0 0 NA 12.7 NA 1 150 #> 151 9 3.0 0 NA 8.0 NA 1 151 #> 152 9 6.0 0 NA 12.7 NA 1 152 #> 153 9 6.0 0 NA 11.5 NA 1 153 #> 154 9 9.0 0 NA 12.9 NA 1 154 #> 155 9 9.0 0 NA 11.4 NA 1 155 #> 156 9 12.0 0 NA 11.4 NA 1 156 #> 157 9 12.0 0 NA 11.0 NA 1 157 #> 158 9 24.0 0 NA 9.1 NA 1 158 #> 159 9 24.0 0 NA 33.0 NA 2 159 #> 160 9 36.0 0 NA 8.2 NA 1 160 #> 161 9 36.0 0 NA 22.0 NA 2 161 #> 162 9 48.0 0 NA 5.9 NA 1 162 #> 163 9 48.0 0 NA 16.0 NA 2 163 #> 164 9 72.0 0 NA 3.6 NA 1 164 #> 165 9 72.0 0 NA 18.0 NA 2 165 #> 166 9 96.0 0 NA 1.7 NA 1 166 #> 167 9 96.0 0 NA 32.0 NA 2 167 #> 168 9 120.0 0 NA 1.1 NA 1 168 #> 169 9 120.0 0 NA 30.0 NA 2 169 #> 170 9 144.0 0 NA 45.0 NA 2 170 #> 171 10 0.0 1 105.0 NA 1 NA 171 #> 172 10 0.0 0 NA 90.0 NA 2 172 #> 173 10 24.0 0 NA 8.6 NA 1 173 #> 174 10 24.0 0 NA 39.0 NA 2 174 #> 175 10 36.0 0 NA 8.0 NA 1 175 #> 176 10 36.0 0 NA 22.0 NA 2 176 #> 177 10 48.0 0 NA 6.0 NA 1 177 #> 178 10 48.0 0 NA 17.0 NA 2 178 #> 179 10 72.0 0 NA 4.4 NA 1 179 #> 180 10 72.0 0 NA 17.0 NA 2 180 #> 181 10 96.0 0 NA 3.6 NA 1 181 #> 182 10 96.0 0 NA 22.0 NA 2 182 #> 183 10 120.0 0 NA 2.8 NA 1 183 #> 184 10 120.0 0 NA 25.0 NA 2 184 #> 185 10 144.0 0 NA 33.0 NA 2 185 #> 186 11 0.0 1 123.0 NA 1 NA 186 #> 187 11 0.0 0 NA 100.0 NA 2 187 #> 188 11 1.5 0 NA 11.4 NA 1 188 #> 189 11 3.0 0 NA 15.4 NA 1 189 #> 190 11 6.0 0 NA 17.5 NA 1 190 #> 191 11 12.0 0 NA 14.0 NA 1 191 #> 192 11 24.0 0 NA 9.0 NA 1 192 #> 193 11 24.0 0 NA 37.0 NA 2 193 #> 194 11 36.0 0 NA 8.9 NA 1 194 #> 195 11 36.0 0 NA 24.0 NA 2 195 #> 196 11 48.0 0 NA 6.6 NA 1 196 #> 197 11 48.0 0 NA 14.0 NA 2 197 #> 198 11 72.0 0 NA 4.2 NA 1 198 #> 199 11 72.0 0 NA 11.0 NA 2 199 #> 200 11 96.0 0 NA 3.6 NA 1 200 #> 201 11 96.0 0 NA 14.0 NA 2 201 #> 202 11 120.0 0 NA 2.6 NA 1 202 #> 203 11 120.0 0 NA 23.0 NA 2 203 #> 204 11 144.0 0 NA 33.0 NA 2 204 #> 205 12 0.0 1 113.0 NA 1 NA 205 #> 206 12 0.0 0 NA 85.0 NA 2 206 #> 207 12 1.5 0 NA 0.6 NA 1 207 #> 208 12 3.0 0 NA 2.8 NA 1 208 #> 209 12 6.0 0 NA 13.8 NA 1 209 #> 210 12 9.0 0 NA 15.0 NA 1 210 #> 211 12 24.0 0 NA 10.5 NA 1 211 #> 212 12 24.0 0 NA 25.0 NA 2 212 #> 213 12 36.0 0 NA 9.1 NA 1 213 #> 214 12 36.0 0 NA 15.0 NA 2 214 #> 215 12 48.0 0 NA 6.6 NA 1 215 #> 216 12 48.0 0 NA 11.0 NA 2 216 #> 217 12 72.0 0 NA 4.9 NA 1 217 #> 218 12 96.0 0 NA 2.4 NA 1 218 #> 219 12 120.0 0 NA 1.9 NA 1 219 #> 220 13 0.0 1 113.0 NA 1 NA 220 #> 221 13 0.0 0 NA 88.0 NA 2 221 #> 222 13 1.5 0 NA 3.6 NA 1 222 #> 223 13 3.0 0 NA 12.9 NA 1 223 #> 224 13 6.0 0 NA 12.9 NA 1 224 #> 225 13 9.0 0 NA 10.2 NA 1 225 #> 226 13 24.0 0 NA 6.4 NA 1 226 #> 227 13 24.0 0 NA 41.0 NA 2 227 #> 228 13 36.0 0 NA 6.9 NA 1 228 #> 229 13 36.0 0 NA 23.0 NA 2 229 #> 230 13 48.0 0 NA 4.5 NA 1 230 #> 231 13 48.0 0 NA 16.0 NA 2 231 #> 232 13 72.0 0 NA 3.2 NA 1 232 #> 233 13 72.0 0 NA 14.0 NA 2 233 #> 234 13 96.0 0 NA 2.4 NA 1 234 #> 235 13 96.0 0 NA 18.0 NA 2 235 #> 236 13 120.0 0 NA 1.3 NA 1 236 #> 237 13 120.0 0 NA 22.0 NA 2 237 #> 238 13 144.0 0 NA 35.0 NA 2 238 #> 239 14 0.0 1 75.0 NA 1 NA 239 #> 240 14 0.0 0 NA 85.0 NA 2 240 #> 241 14 0.5 0 NA 0.0 NA 1 241 #> 242 14 1.0 0 NA 2.7 NA 1 242 #> 243 14 2.0 0 NA 11.6 NA 1 243 #> 244 14 3.0 0 NA 11.6 NA 1 244 #> 245 14 6.0 0 NA 11.3 NA 1 245 #> 246 14 9.0 0 NA 9.7 NA 1 246 #> 247 14 24.0 0 NA 6.5 NA 1 247 #> 248 14 24.0 0 NA 32.0 NA 2 248 #> 249 14 36.0 0 NA 5.2 NA 1 249 #> 250 14 36.0 0 NA 22.0 NA 2 250 #> 251 14 48.0 0 NA 3.6 NA 1 251 #> 252 14 48.0 0 NA 21.0 NA 2 252 #> 253 14 72.0 0 NA 2.4 NA 1 253 #> 254 14 72.0 0 NA 28.0 NA 2 254 #> 255 14 96.0 0 NA 0.9 NA 1 255 #> 256 14 96.0 0 NA 38.0 NA 2 256 #> 257 14 120.0 0 NA 46.0 NA 2 257 #> 258 14 144.0 0 NA 65.0 NA 2 258 #> 259 15 0.0 1 85.0 NA 1 NA 259 #> 260 15 0.0 0 NA 100.0 NA 2 260 #> 261 15 1.0 0 NA 6.6 NA 1 261 #> 262 15 3.0 0 NA 11.9 NA 1 262 #> 263 15 6.0 0 NA 11.7 NA 1 263 #> 264 15 9.0 0 NA 12.2 NA 1 264 #> 265 15 24.0 0 NA 8.1 NA 1 265 #> 266 15 24.0 0 NA 43.0 NA 2 266 #> 267 15 36.0 0 NA 7.4 NA 1 267 #> 268 15 36.0 0 NA 26.0 NA 2 268 #> 269 15 48.0 0 NA 6.8 NA 1 269 #> 270 15 48.0 0 NA 15.0 NA 2 270 #> 271 15 72.0 0 NA 5.3 NA 1 271 #> 272 15 72.0 0 NA 13.0 NA 2 272 #> 273 15 96.0 0 NA 3.0 NA 1 273 #> 274 15 96.0 0 NA 21.0 NA 2 274 #> 275 15 120.0 0 NA 2.0 NA 1 275 #> 276 15 120.0 0 NA 28.0 NA 2 276 #> 277 15 144.0 0 NA 39.0 NA 2 277 #> 278 16 0.0 1 87.0 NA 1 NA 278 #> 279 16 0.0 0 NA 100.0 NA 2 279 #> 280 16 24.0 0 NA 10.4 NA 1 280 #> 281 16 24.0 0 NA 42.0 NA 2 281 #> 282 16 36.0 0 NA 8.9 NA 1 282 #> 283 16 36.0 0 NA 32.0 NA 2 283 #> 284 16 48.0 0 NA 7.0 NA 1 284 #> 285 16 48.0 0 NA 26.0 NA 2 285 #> 286 16 72.0 0 NA 4.4 NA 1 286 #> 287 16 72.0 0 NA 31.0 NA 2 287 #> 288 16 96.0 0 NA 3.2 NA 1 288 #> 289 16 96.0 0 NA 33.0 NA 2 289 #> 290 16 120.0 0 NA 2.4 NA 1 290 #> 291 16 120.0 0 NA 54.0 NA 2 291 #> 292 17 0.0 1 117.0 NA 1 NA 292 #> 293 17 0.0 0 NA 100.0 NA 2 293 #> 294 17 24.0 0 NA 7.6 NA 1 294 #> 295 17 24.0 0 NA 35.0 NA 2 295 #> 296 17 36.0 0 NA 6.4 NA 1 296 #> 297 17 36.0 0 NA 23.0 NA 2 297 #> 298 17 48.0 0 NA 6.0 NA 1 298 #> 299 17 48.0 0 NA 17.0 NA 2 299 #> 300 17 72.0 0 NA 4.0 NA 1 300 #> 301 17 72.0 0 NA 18.0 NA 2 301 #> 302 17 96.0 0 NA 3.1 NA 1 302 #> 303 17 96.0 0 NA 18.0 NA 2 303 #> 304 17 120.0 0 NA 2.0 NA 1 304 #> 305 17 120.0 0 NA 21.0 NA 2 305 #> 306 18 0.0 1 112.0 NA 1 NA 306 #> 307 18 0.0 0 NA 100.0 NA 2 307 #> 308 18 24.0 0 NA 7.6 NA 1 308 #> 309 18 24.0 0 NA 32.0 NA 2 309 #> 310 18 36.0 0 NA 6.6 NA 1 310 #> 311 18 36.0 0 NA 20.0 NA 2 311 #> 312 18 48.0 0 NA 5.4 NA 1 312 #> 313 18 48.0 0 NA 18.0 NA 2 313 #> 314 18 72.0 0 NA 3.4 NA 1 314 #> 315 18 72.0 0 NA 18.0 NA 2 315 #> 316 18 96.0 0 NA 1.2 NA 1 316 #> 317 18 96.0 0 NA 19.0 NA 2 317 #> 318 18 120.0 0 NA 0.9 NA 1 318 #> 319 18 120.0 0 NA 29.0 NA 2 319 #> 320 19 0.0 1 95.5 NA 1 NA 320 #> 321 19 0.0 0 NA 100.0 NA 2 321 #> 322 19 24.0 0 NA 6.6 NA 1 322 #> 323 19 24.0 0 NA 33.0 NA 2 323 #> 324 19 36.0 0 NA 5.3 NA 1 324 #> 325 19 36.0 0 NA 28.0 NA 2 325 #> 326 19 48.0 0 NA 3.6 NA 1 326 #> 327 19 48.0 0 NA 18.0 NA 2 327 #> 328 19 72.0 0 NA 2.7 NA 1 328 #> 329 19 72.0 0 NA 18.0 NA 2 329 #> 330 19 96.0 0 NA 1.4 NA 1 330 #> 331 19 96.0 0 NA 17.0 NA 2 331 #> 332 19 120.0 0 NA 1.1 NA 1 332 #> 333 19 120.0 0 NA 26.0 NA 2 333 #> 334 20 0.0 1 88.5 NA 1 NA 334 #> 335 20 0.0 0 NA 100.0 NA 2 335 #> 336 20 24.0 0 NA 9.6 NA 1 336 #> 337 20 24.0 0 NA 41.0 NA 2 337 #> 338 20 36.0 0 NA 8.0 NA 1 338 #> 339 20 36.0 0 NA 30.0 NA 2 339 #> 340 20 48.0 0 NA 6.6 NA 1 340 #> 341 20 48.0 0 NA 22.0 NA 2 341 #> 342 20 72.0 0 NA 5.6 NA 1 342 #> 343 20 72.0 0 NA 23.0 NA 2 343 #> 344 20 96.0 0 NA 3.5 NA 1 344 #> 345 20 96.0 0 NA 23.0 NA 2 345 #> 346 20 120.0 0 NA 2.3 NA 1 346 #> 347 20 120.0 0 NA 35.0 NA 2 347 #> 348 21 0.0 1 93.0 NA 1 NA 348 #> 349 21 0.0 0 NA 100.0 NA 2 349 #> 350 21 24.0 0 NA 7.3 NA 1 350 #> 351 21 24.0 0 NA 46.0 NA 2 351 #> 352 21 36.0 0 NA 6.1 NA 1 352 #> 353 21 36.0 0 NA 27.0 NA 2 353 #> 354 21 48.0 0 NA 4.3 NA 1 354 #> 355 21 48.0 0 NA 22.0 NA 2 355 #> 356 21 72.0 0 NA 3.2 NA 1 356 #> 357 21 72.0 0 NA 36.0 NA 2 357 #> 358 21 96.0 0 NA 2.3 NA 1 358 #> 359 21 96.0 0 NA 40.0 NA 2 359 #> 360 21 120.0 0 NA 1.9 NA 1 360 #> 361 21 120.0 0 NA 44.0 NA 2 361 #> 362 22 0.0 1 87.0 NA 1 NA 362 #> 363 22 0.0 0 NA 100.0 NA 2 363 #> 364 22 24.0 0 NA 8.9 NA 1 364 #> 365 22 24.0 0 NA 35.0 NA 2 365 #> 366 22 36.0 0 NA 8.4 NA 1 366 #> 367 22 36.0 0 NA 27.0 NA 2 367 #> 368 22 48.0 0 NA 8.0 NA 1 368 #> 369 22 48.0 0 NA 23.0 NA 2 369 #> 370 22 72.0 0 NA 4.4 NA 1 370 #> 371 22 72.0 0 NA 27.0 NA 2 371 #> 372 22 96.0 0 NA 3.2 NA 1 372 #> 373 22 96.0 0 NA 43.0 NA 2 373 #> 374 22 120.0 0 NA 1.7 NA 1 374 #> 375 22 120.0 0 NA 43.0 NA 2 375 #> 376 23 0.0 1 110.0 NA 1 NA 376 #> 377 23 0.0 0 NA 100.0 NA 2 377 #> 378 23 24.0 0 NA 9.8 NA 1 378 #> 379 23 24.0 0 NA 34.0 NA 2 379 #> 380 23 36.0 0 NA 8.4 NA 1 380 #> 381 23 36.0 0 NA 24.0 NA 2 381 #> 382 23 48.0 0 NA 6.6 NA 1 382 #> 383 23 48.0 0 NA 15.0 NA 2 383 #> 384 23 72.0 0 NA 4.8 NA 1 384 #> 385 23 72.0 0 NA 15.0 NA 2 385 #> 386 23 96.0 0 NA 3.2 NA 1 386 #> 387 23 96.0 0 NA 19.0 NA 2 387 #> 388 23 120.0 0 NA 2.4 NA 1 388 #> 389 23 120.0 0 NA 19.0 NA 2 389 #> 390 24 0.0 1 115.0 NA 1 NA 390 #> 391 24 0.0 0 NA 88.0 NA 2 391 #> 392 24 24.0 0 NA 8.2 NA 1 392 #> 393 24 24.0 0 NA 37.0 NA 2 393 #> 394 24 36.0 0 NA 7.5 NA 1 394 #> 395 24 36.0 0 NA 20.0 NA 2 395 #> 396 24 48.0 0 NA 6.8 NA 1 396 #> 397 24 48.0 0 NA 20.0 NA 2 397 #> 398 24 72.0 0 NA 5.5 NA 1 398 #> 399 24 72.0 0 NA 26.0 NA 2 399 #> 400 24 96.0 0 NA 4.5 NA 1 400 #> 401 24 96.0 0 NA 28.0 NA 2 401 #> 402 24 120.0 0 NA 3.7 NA 1 402 #> 403 24 120.0 0 NA 50.0 NA 2 403 #> 404 25 0.0 1 112.0 NA 1 NA 404 #> 405 25 0.0 0 NA 100.0 NA 2 405 #> 406 25 24.0 0 NA 11.0 NA 1 406 #> 407 25 24.0 0 NA 32.0 NA 2 407 #> 408 25 36.0 0 NA 10.0 NA 1 408 #> 409 25 36.0 0 NA 20.0 NA 2 409 #> 410 25 48.0 0 NA 8.2 NA 1 410 #> 411 25 48.0 0 NA 17.0 NA 2 411 #> 412 25 72.0 0 NA 6.0 NA 1 412 #> 413 25 72.0 0 NA 19.0 NA 2 413 #> 414 25 96.0 0 NA 3.7 NA 1 414 #> 415 25 96.0 0 NA 21.0 NA 2 415 #> 416 25 120.0 0 NA 2.6 NA 1 416 #> 417 25 120.0 0 NA 30.0 NA 2 417 #> 418 26 0.0 1 120.0 NA 1 NA 418 #> 419 26 0.0 0 NA 100.0 NA 2 419 #> 420 26 24.0 0 NA 10.0 NA 1 420 #> 421 26 24.0 0 NA 41.0 NA 2 421 #> 422 26 36.0 0 NA 9.0 NA 1 422 #> 423 26 36.0 0 NA 28.0 NA 2 423 #> 424 26 48.0 0 NA 7.3 NA 1 424 #> 425 26 48.0 0 NA 19.0 NA 2 425 #> 426 26 72.0 0 NA 5.2 NA 1 426 #> 427 26 72.0 0 NA 17.0 NA 2 427 #> 428 26 96.0 0 NA 3.7 NA 1 428 #> 429 26 96.0 0 NA 17.0 NA 2 429 #> 430 26 120.0 0 NA 2.7 NA 1 430 #> 431 26 120.0 0 NA 24.0 NA 2 431 #> 432 27 0.0 1 120.0 NA 1 NA 432 #> 433 27 0.0 0 NA 100.0 NA 2 433 #> 434 27 24.0 0 NA 11.8 NA 1 434 #> 435 27 24.0 0 NA 32.0 NA 2 435 #> 436 27 36.0 0 NA 9.2 NA 1 436 #> 437 27 36.0 0 NA 21.0 NA 2 437 #> 438 27 48.0 0 NA 7.7 NA 1 438 #> 439 27 48.0 0 NA 19.0 NA 2 439 #> 440 27 72.0 0 NA 4.9 NA 1 440 #> 441 27 72.0 0 NA 22.0 NA 2 441 #> 442 27 96.0 0 NA 3.4 NA 1 442 #> 443 27 96.0 0 NA 33.0 NA 2 443 #> 444 27 120.0 0 NA 2.7 NA 1 444 #> 445 27 120.0 0 NA 46.0 NA 2 445 #> 446 28 0.0 1 120.0 NA 1 NA 446 #> 447 28 0.0 0 NA 100.0 NA 2 447 #> 448 28 24.0 0 NA 10.1 NA 1 448 #> 449 28 24.0 0 NA 39.0 NA 2 449 #> 450 28 36.0 0 NA 8.0 NA 1 450 #> 451 28 36.0 0 NA 25.0 NA 2 451 #> 452 28 48.0 0 NA 6.0 NA 1 452 #> 453 28 48.0 0 NA 16.0 NA 2 453 #> 454 28 72.0 0 NA 4.9 NA 1 454 #> 455 28 72.0 0 NA 14.0 NA 2 455 #> 456 28 96.0 0 NA 3.4 NA 1 456 #> 457 28 96.0 0 NA 15.0 NA 2 457 #> 458 28 120.0 0 NA 2.0 NA 1 458 #> 459 28 120.0 0 NA 20.0 NA 2 459 #> 460 29 0.0 1 153.0 NA 1 NA 460 #> 461 29 0.0 0 NA 86.0 NA 2 461 #> 462 29 24.0 0 NA 8.3 NA 1 462 #> 463 29 24.0 0 NA 35.0 NA 2 463 #> 464 29 36.0 0 NA 7.0 NA 1 464 #> 465 29 36.0 0 NA 21.0 NA 2 465 #> 466 29 48.0 0 NA 5.6 NA 1 466 #> 467 29 48.0 0 NA 18.0 NA 2 467 #> 468 29 72.0 0 NA 4.1 NA 1 468 #> 469 29 72.0 0 NA 20.0 NA 2 469 #> 470 29 96.0 0 NA 3.1 NA 1 470 #> 471 29 96.0 0 NA 29.0 NA 2 471 #> 472 29 120.0 0 NA 2.2 NA 1 472 #> 473 29 120.0 0 NA 41.0 NA 2 473 #> 474 30 0.0 1 105.0 NA 1 NA 474 #> 475 30 0.0 0 NA 100.0 NA 2 475 #> 476 30 24.0 0 NA 9.9 NA 1 476 #> 477 30 24.0 0 NA 45.0 NA 2 477 #> 478 30 36.0 0 NA 7.5 NA 1 478 #> 479 30 36.0 0 NA 24.0 NA 2 479 #> 480 30 48.0 0 NA 6.5 NA 1 480 #> 481 30 48.0 0 NA 23.0 NA 2 481 #> 482 30 72.0 0 NA 4.1 NA 1 482 #> 483 30 72.0 0 NA 26.0 NA 2 483 #> 484 30 96.0 0 NA 2.9 NA 1 484 #> 485 30 96.0 0 NA 28.0 NA 2 485 #> 486 30 120.0 0 NA 2.3 NA 1 486 #> 487 30 120.0 0 NA 39.0 NA 2 487 #> 488 31 0.0 1 125.0 NA 1 NA 488 #> 489 31 0.0 0 NA 100.0 NA 2 489 #> 490 31 24.0 0 NA 9.5 NA 1 490 #> 491 31 24.0 0 NA 45.0 NA 2 491 #> 492 31 36.0 0 NA 7.8 NA 1 492 #> 493 31 36.0 0 NA 30.0 NA 2 493 #> 494 31 48.0 0 NA 6.4 NA 1 494 #> 495 31 48.0 0 NA 24.0 NA 2 495 #> 496 31 72.0 0 NA 4.5 NA 1 496 #> 497 31 72.0 0 NA 22.0 NA 2 497 #> 498 31 96.0 0 NA 3.4 NA 1 498 #> 499 31 96.0 0 NA 28.0 NA 2 499 #> 500 31 120.0 0 NA 2.5 NA 1 500 #> 501 31 120.0 0 NA 42.0 NA 2 501 #> 502 32 0.0 1 93.0 NA 1 NA 502 #> 503 32 0.0 0 NA 100.0 NA 2 503 #> 504 32 24.0 0 NA 8.9 NA 1 504 #> 505 32 24.0 0 NA 36.0 NA 2 505 #> 506 32 36.0 0 NA 7.7 NA 1 506 #> 507 32 36.0 0 NA 27.0 NA 2 507 #> 508 32 48.0 0 NA 6.9 NA 1 508 #> 509 32 48.0 0 NA 24.0 NA 2 509 #> 510 32 72.0 0 NA 4.4 NA 1 510 #> 511 32 72.0 0 NA 23.0 NA 2 511 #> 512 32 96.0 0 NA 3.5 NA 1 512 #> 513 32 96.0 0 NA 20.0 NA 2 513 #> 514 32 120.0 0 NA 2.5 NA 1 514 #> 515 32 120.0 0 NA 22.0 NA 2 515 bblDatToMrgsolve(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ID TIME EVID AMT II DV CMT DVID SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 5 1 0 2 #> 3 1 1.0 0 NA 0 1.9 5 1 0 3 #> 4 1 2.0 0 NA 0 3.3 5 1 0 4 #> 5 1 3.0 0 NA 0 6.6 5 1 0 5 #> 6 1 6.0 0 NA 0 9.1 5 1 0 6 #> 7 1 9.0 0 NA 0 10.8 5 1 0 7 #> 8 1 12.0 0 NA 0 8.6 5 1 0 8 #> 9 1 24.0 0 NA 0 5.6 5 1 0 9 #> 10 1 24.0 0 NA 0 44.0 6 2 0 10 #> 11 1 36.0 0 NA 0 4.0 5 1 0 11 #> 12 1 36.0 0 NA 0 27.0 6 2 0 12 #> 13 1 48.0 0 NA 0 2.7 5 1 0 13 #> 14 1 48.0 0 NA 0 28.0 6 2 0 14 #> 15 1 72.0 0 NA 0 0.8 5 1 0 15 #> 16 1 72.0 0 NA 0 31.0 6 2 0 16 #> 17 1 96.0 0 NA 0 60.0 6 2 0 17 #> 18 1 120.0 0 NA 0 65.0 6 2 0 18 #> 19 1 144.0 0 NA 0 71.0 6 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 6 2 0 21 #> 22 2 24.0 0 NA 0 9.2 5 1 0 22 #> 23 2 24.0 0 NA 0 49.0 6 2 0 23 #> 24 2 36.0 0 NA 0 8.5 5 1 0 24 #> 25 2 36.0 0 NA 0 32.0 6 2 0 25 #> 26 2 48.0 0 NA 0 6.4 5 1 0 26 #> 27 2 48.0 0 NA 0 26.0 6 2 0 27 #> 28 2 72.0 0 NA 0 4.8 5 1 0 28 #> 29 2 72.0 0 NA 0 22.0 6 2 0 29 #> 30 2 96.0 0 NA 0 3.1 5 1 0 30 #> 31 2 96.0 0 NA 0 28.0 6 2 0 31 #> 32 2 120.0 0 NA 0 2.5 5 1 0 32 #> 33 2 120.0 0 NA 0 33.0 6 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 6 2 0 35 #> 36 3 0.5 0 NA 0 0.0 5 1 0 36 #> 37 3 2.0 0 NA 0 8.4 5 1 0 37 #> 38 3 3.0 0 NA 0 9.7 5 1 0 38 #> 39 3 6.0 0 NA 0 9.8 5 1 0 39 #> 40 3 12.0 0 NA 0 11.0 5 1 0 40 #> 41 3 24.0 0 NA 0 8.3 5 1 0 41 #> 42 3 24.0 0 NA 0 46.0 6 2 0 42 #> 43 3 36.0 0 NA 0 7.7 5 1 0 43 #> 44 3 36.0 0 NA 0 22.0 6 2 0 44 #> 45 3 48.0 0 NA 0 6.3 5 1 0 45 #> 46 3 48.0 0 NA 0 19.0 6 2 0 46 #> 47 3 72.0 0 NA 0 4.1 5 1 0 47 #> 48 3 72.0 0 NA 0 20.0 6 2 0 48 #> 49 3 96.0 0 NA 0 3.0 5 1 0 49 #> 50 3 96.0 0 NA 0 42.0 6 2 0 50 #> 51 3 120.0 0 NA 0 1.4 5 1 0 51 #> 52 3 120.0 0 NA 0 49.0 6 2 0 52 #> 53 3 144.0 0 NA 0 54.0 6 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 6 2 0 55 #> 56 4 3.0 0 NA 0 12.0 5 1 0 56 #> 57 4 6.0 0 NA 0 13.2 5 1 0 57 #> 58 4 9.0 0 NA 0 14.4 5 1 0 58 #> 59 4 24.0 0 NA 0 9.6 5 1 0 59 #> 60 4 24.0 0 NA 0 30.0 6 2 0 60 #> 61 4 36.0 0 NA 0 8.2 5 1 0 61 #> 62 4 36.0 0 NA 0 24.0 6 2 0 62 #> 63 4 48.0 0 NA 0 7.8 5 1 0 63 #> 64 4 48.0 0 NA 0 13.0 6 2 0 64 #> 65 4 72.0 0 NA 0 5.8 5 1 0 65 #> 66 4 72.0 0 NA 0 9.0 6 2 0 66 #> 67 4 96.0 0 NA 0 4.3 5 1 0 67 #> 68 4 96.0 0 NA 0 9.0 6 2 0 68 #> 69 4 120.0 0 NA 0 3.0 5 1 0 69 #> 70 4 120.0 0 NA 0 11.0 6 2 0 70 #> 71 4 144.0 0 NA 0 12.0 6 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 6 2 0 73 #> 74 5 3.0 0 NA 0 11.1 5 1 0 74 #> 75 5 6.0 0 NA 0 11.9 5 1 0 75 #> 76 5 9.0 0 NA 0 9.8 5 1 0 76 #> 77 5 12.0 0 NA 0 11.0 5 1 0 77 #> 78 5 24.0 0 NA 0 8.5 5 1 0 78 #> 79 5 24.0 0 NA 0 43.0 6 2 0 79 #> 80 5 36.0 0 NA 0 7.6 5 1 0 80 #> 81 5 36.0 0 NA 0 25.0 6 2 0 81 #> 82 5 48.0 0 NA 0 5.4 5 1 0 82 #> 83 5 48.0 0 NA 0 18.0 6 2 0 83 #> 84 5 72.0 0 NA 0 4.5 5 1 0 84 #> 85 5 72.0 0 NA 0 17.0 6 2 0 85 #> 86 5 96.0 0 NA 0 3.3 5 1 0 86 #> 87 5 96.0 0 NA 0 23.0 6 2 0 87 #> 88 5 120.0 0 NA 0 2.3 5 1 0 88 #> 89 5 120.0 0 NA 0 29.0 6 2 0 89 #> 90 5 144.0 0 NA 0 41.0 6 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 6 2 0 92 #> 93 6 6.0 0 NA 0 8.6 5 1 0 93 #> 94 6 12.0 0 NA 0 8.6 5 1 0 94 #> 95 6 24.0 0 NA 0 7.0 5 1 0 95 #> 96 6 24.0 0 NA 0 34.0 6 2 0 96 #> 97 6 36.0 0 NA 0 5.7 5 1 0 97 #> 98 6 36.0 0 NA 0 23.0 6 2 0 98 #> 99 6 48.0 0 NA 0 4.7 5 1 0 99 #> 100 6 48.0 0 NA 0 20.0 6 2 0 100 #> 101 6 72.0 0 NA 0 3.3 5 1 0 101 #> 102 6 72.0 0 NA 0 16.0 6 2 0 102 #> 103 6 96.0 0 NA 0 2.3 5 1 0 103 #> 104 6 96.0 0 NA 0 17.0 6 2 0 104 #> 105 6 120.0 0 NA 0 1.7 5 1 0 105 #> 106 6 120.0 0 NA 0 18.0 6 2 0 106 #> 107 6 144.0 0 NA 0 25.0 6 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 5 1 0 109 #> 110 7 6.0 0 NA 0 12.4 5 1 0 110 #> 111 7 9.0 0 NA 0 12.7 5 1 0 111 #> 112 7 12.0 0 NA 0 8.8 5 1 0 112 #> 113 7 24.0 0 NA 0 6.1 5 1 0 113 #> 114 7 24.0 0 NA 0 36.0 6 2 0 114 #> 115 7 36.0 0 NA 0 3.5 5 1 0 115 #> 116 7 36.0 0 NA 0 33.0 6 2 0 116 #> 117 7 48.0 0 NA 0 1.8 5 1 0 117 #> 118 7 48.0 0 NA 0 28.0 6 2 0 118 #> 119 7 72.0 0 NA 0 1.5 5 1 0 119 #> 120 7 72.0 0 NA 0 52.0 6 2 0 120 #> 121 7 96.0 0 NA 0 1.0 5 1 0 121 #> 122 7 96.0 0 NA 0 80.0 6 2 0 122 #> 123 7 120.0 0 NA 0 90.0 6 2 0 123 #> 124 7 144.0 0 NA 0 100.0 6 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 6 2 0 126 #> 127 8 2.0 0 NA 0 17.6 5 1 0 127 #> 128 8 3.0 0 NA 0 17.3 5 1 0 128 #> 129 8 6.0 0 NA 0 15.0 5 1 0 129 #> 130 8 9.0 0 NA 0 15.0 5 1 0 130 #> 131 8 12.0 0 NA 0 12.4 5 1 0 131 #> 132 8 24.0 0 NA 0 7.9 5 1 0 132 #> 133 8 24.0 0 NA 0 35.0 6 2 0 133 #> 134 8 36.0 0 NA 0 7.9 5 1 0 134 #> 135 8 36.0 0 NA 0 20.0 6 2 0 135 #> 136 8 48.0 0 NA 0 5.1 5 1 0 136 #> 137 8 48.0 0 NA 0 12.0 6 2 0 137 #> 138 8 72.0 0 NA 0 3.6 5 1 0 138 #> 139 8 72.0 0 NA 0 16.0 6 2 0 139 #> 140 8 96.0 0 NA 0 2.4 5 1 0 140 #> 141 8 96.0 0 NA 0 23.0 6 2 0 141 #> 142 8 120.0 0 NA 0 2.0 5 1 0 142 #> 143 8 120.0 0 NA 0 36.0 6 2 0 143 #> 144 8 144.0 0 NA 0 48.0 6 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 6 2 0 146 #> 147 9 0.5 0 NA 0 0.0 5 1 0 147 #> 148 9 1.0 0 NA 0 1.0 5 1 0 148 #> 149 9 2.0 0 NA 0 4.6 5 1 0 149 #> 150 9 3.0 0 NA 0 12.7 5 1 0 150 #> 151 9 3.0 0 NA 0 8.0 5 1 0 151 #> 152 9 6.0 0 NA 0 12.7 5 1 0 152 #> 153 9 6.0 0 NA 0 11.5 5 1 0 153 #> 154 9 9.0 0 NA 0 12.9 5 1 0 154 #> 155 9 9.0 0 NA 0 11.4 5 1 0 155 #> 156 9 12.0 0 NA 0 11.4 5 1 0 156 #> 157 9 12.0 0 NA 0 11.0 5 1 0 157 #> 158 9 24.0 0 NA 0 9.1 5 1 0 158 #> 159 9 24.0 0 NA 0 33.0 6 2 0 159 #> 160 9 36.0 0 NA 0 8.2 5 1 0 160 #> 161 9 36.0 0 NA 0 22.0 6 2 0 161 #> 162 9 48.0 0 NA 0 5.9 5 1 0 162 #> 163 9 48.0 0 NA 0 16.0 6 2 0 163 #> 164 9 72.0 0 NA 0 3.6 5 1 0 164 #> 165 9 72.0 0 NA 0 18.0 6 2 0 165 #> 166 9 96.0 0 NA 0 1.7 5 1 0 166 #> 167 9 96.0 0 NA 0 32.0 6 2 0 167 #> 168 9 120.0 0 NA 0 1.1 5 1 0 168 #> 169 9 120.0 0 NA 0 30.0 6 2 0 169 #> 170 9 144.0 0 NA 0 45.0 6 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 6 2 0 172 #> 173 10 24.0 0 NA 0 8.6 5 1 0 173 #> 174 10 24.0 0 NA 0 39.0 6 2 0 174 #> 175 10 36.0 0 NA 0 8.0 5 1 0 175 #> 176 10 36.0 0 NA 0 22.0 6 2 0 176 #> 177 10 48.0 0 NA 0 6.0 5 1 0 177 #> 178 10 48.0 0 NA 0 17.0 6 2 0 178 #> 179 10 72.0 0 NA 0 4.4 5 1 0 179 #> 180 10 72.0 0 NA 0 17.0 6 2 0 180 #> 181 10 96.0 0 NA 0 3.6 5 1 0 181 #> 182 10 96.0 0 NA 0 22.0 6 2 0 182 #> 183 10 120.0 0 NA 0 2.8 5 1 0 183 #> 184 10 120.0 0 NA 0 25.0 6 2 0 184 #> 185 10 144.0 0 NA 0 33.0 6 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 6 2 0 187 #> 188 11 1.5 0 NA 0 11.4 5 1 0 188 #> 189 11 3.0 0 NA 0 15.4 5 1 0 189 #> 190 11 6.0 0 NA 0 17.5 5 1 0 190 #> 191 11 12.0 0 NA 0 14.0 5 1 0 191 #> 192 11 24.0 0 NA 0 9.0 5 1 0 192 #> 193 11 24.0 0 NA 0 37.0 6 2 0 193 #> 194 11 36.0 0 NA 0 8.9 5 1 0 194 #> 195 11 36.0 0 NA 0 24.0 6 2 0 195 #> 196 11 48.0 0 NA 0 6.6 5 1 0 196 #> 197 11 48.0 0 NA 0 14.0 6 2 0 197 #> 198 11 72.0 0 NA 0 4.2 5 1 0 198 #> 199 11 72.0 0 NA 0 11.0 6 2 0 199 #> 200 11 96.0 0 NA 0 3.6 5 1 0 200 #> 201 11 96.0 0 NA 0 14.0 6 2 0 201 #> 202 11 120.0 0 NA 0 2.6 5 1 0 202 #> 203 11 120.0 0 NA 0 23.0 6 2 0 203 #> 204 11 144.0 0 NA 0 33.0 6 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 6 2 0 206 #> 207 12 1.5 0 NA 0 0.6 5 1 0 207 #> 208 12 3.0 0 NA 0 2.8 5 1 0 208 #> 209 12 6.0 0 NA 0 13.8 5 1 0 209 #> 210 12 9.0 0 NA 0 15.0 5 1 0 210 #> 211 12 24.0 0 NA 0 10.5 5 1 0 211 #> 212 12 24.0 0 NA 0 25.0 6 2 0 212 #> 213 12 36.0 0 NA 0 9.1 5 1 0 213 #> 214 12 36.0 0 NA 0 15.0 6 2 0 214 #> 215 12 48.0 0 NA 0 6.6 5 1 0 215 #> 216 12 48.0 0 NA 0 11.0 6 2 0 216 #> 217 12 72.0 0 NA 0 4.9 5 1 0 217 #> 218 12 96.0 0 NA 0 2.4 5 1 0 218 #> 219 12 120.0 0 NA 0 1.9 5 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 6 2 0 221 #> 222 13 1.5 0 NA 0 3.6 5 1 0 222 #> 223 13 3.0 0 NA 0 12.9 5 1 0 223 #> 224 13 6.0 0 NA 0 12.9 5 1 0 224 #> 225 13 9.0 0 NA 0 10.2 5 1 0 225 #> 226 13 24.0 0 NA 0 6.4 5 1 0 226 #> 227 13 24.0 0 NA 0 41.0 6 2 0 227 #> 228 13 36.0 0 NA 0 6.9 5 1 0 228 #> 229 13 36.0 0 NA 0 23.0 6 2 0 229 #> 230 13 48.0 0 NA 0 4.5 5 1 0 230 #> 231 13 48.0 0 NA 0 16.0 6 2 0 231 #> 232 13 72.0 0 NA 0 3.2 5 1 0 232 #> 233 13 72.0 0 NA 0 14.0 6 2 0 233 #> 234 13 96.0 0 NA 0 2.4 5 1 0 234 #> 235 13 96.0 0 NA 0 18.0 6 2 0 235 #> 236 13 120.0 0 NA 0 1.3 5 1 0 236 #> 237 13 120.0 0 NA 0 22.0 6 2 0 237 #> 238 13 144.0 0 NA 0 35.0 6 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 6 2 0 240 #> 241 14 0.5 0 NA 0 0.0 5 1 0 241 #> 242 14 1.0 0 NA 0 2.7 5 1 0 242 #> 243 14 2.0 0 NA 0 11.6 5 1 0 243 #> 244 14 3.0 0 NA 0 11.6 5 1 0 244 #> 245 14 6.0 0 NA 0 11.3 5 1 0 245 #> 246 14 9.0 0 NA 0 9.7 5 1 0 246 #> 247 14 24.0 0 NA 0 6.5 5 1 0 247 #> 248 14 24.0 0 NA 0 32.0 6 2 0 248 #> 249 14 36.0 0 NA 0 5.2 5 1 0 249 #> 250 14 36.0 0 NA 0 22.0 6 2 0 250 #> 251 14 48.0 0 NA 0 3.6 5 1 0 251 #> 252 14 48.0 0 NA 0 21.0 6 2 0 252 #> 253 14 72.0 0 NA 0 2.4 5 1 0 253 #> 254 14 72.0 0 NA 0 28.0 6 2 0 254 #> 255 14 96.0 0 NA 0 0.9 5 1 0 255 #> 256 14 96.0 0 NA 0 38.0 6 2 0 256 #> 257 14 120.0 0 NA 0 46.0 6 2 0 257 #> 258 14 144.0 0 NA 0 65.0 6 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 6 2 0 260 #> 261 15 1.0 0 NA 0 6.6 5 1 0 261 #> 262 15 3.0 0 NA 0 11.9 5 1 0 262 #> 263 15 6.0 0 NA 0 11.7 5 1 0 263 #> 264 15 9.0 0 NA 0 12.2 5 1 0 264 #> 265 15 24.0 0 NA 0 8.1 5 1 0 265 #> 266 15 24.0 0 NA 0 43.0 6 2 0 266 #> 267 15 36.0 0 NA 0 7.4 5 1 0 267 #> 268 15 36.0 0 NA 0 26.0 6 2 0 268 #> 269 15 48.0 0 NA 0 6.8 5 1 0 269 #> 270 15 48.0 0 NA 0 15.0 6 2 0 270 #> 271 15 72.0 0 NA 0 5.3 5 1 0 271 #> 272 15 72.0 0 NA 0 13.0 6 2 0 272 #> 273 15 96.0 0 NA 0 3.0 5 1 0 273 #> 274 15 96.0 0 NA 0 21.0 6 2 0 274 #> 275 15 120.0 0 NA 0 2.0 5 1 0 275 #> 276 15 120.0 0 NA 0 28.0 6 2 0 276 #> 277 15 144.0 0 NA 0 39.0 6 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 6 2 0 279 #> 280 16 24.0 0 NA 0 10.4 5 1 0 280 #> 281 16 24.0 0 NA 0 42.0 6 2 0 281 #> 282 16 36.0 0 NA 0 8.9 5 1 0 282 #> 283 16 36.0 0 NA 0 32.0 6 2 0 283 #> 284 16 48.0 0 NA 0 7.0 5 1 0 284 #> 285 16 48.0 0 NA 0 26.0 6 2 0 285 #> 286 16 72.0 0 NA 0 4.4 5 1 0 286 #> 287 16 72.0 0 NA 0 31.0 6 2 0 287 #> 288 16 96.0 0 NA 0 3.2 5 1 0 288 #> 289 16 96.0 0 NA 0 33.0 6 2 0 289 #> 290 16 120.0 0 NA 0 2.4 5 1 0 290 #> 291 16 120.0 0 NA 0 54.0 6 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 6 2 0 293 #> 294 17 24.0 0 NA 0 7.6 5 1 0 294 #> 295 17 24.0 0 NA 0 35.0 6 2 0 295 #> 296 17 36.0 0 NA 0 6.4 5 1 0 296 #> 297 17 36.0 0 NA 0 23.0 6 2 0 297 #> 298 17 48.0 0 NA 0 6.0 5 1 0 298 #> 299 17 48.0 0 NA 0 17.0 6 2 0 299 #> 300 17 72.0 0 NA 0 4.0 5 1 0 300 #> 301 17 72.0 0 NA 0 18.0 6 2 0 301 #> 302 17 96.0 0 NA 0 3.1 5 1 0 302 #> 303 17 96.0 0 NA 0 18.0 6 2 0 303 #> 304 17 120.0 0 NA 0 2.0 5 1 0 304 #> 305 17 120.0 0 NA 0 21.0 6 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 6 2 0 307 #> 308 18 24.0 0 NA 0 7.6 5 1 0 308 #> 309 18 24.0 0 NA 0 32.0 6 2 0 309 #> 310 18 36.0 0 NA 0 6.6 5 1 0 310 #> 311 18 36.0 0 NA 0 20.0 6 2 0 311 #> 312 18 48.0 0 NA 0 5.4 5 1 0 312 #> 313 18 48.0 0 NA 0 18.0 6 2 0 313 #> 314 18 72.0 0 NA 0 3.4 5 1 0 314 #> 315 18 72.0 0 NA 0 18.0 6 2 0 315 #> 316 18 96.0 0 NA 0 1.2 5 1 0 316 #> 317 18 96.0 0 NA 0 19.0 6 2 0 317 #> 318 18 120.0 0 NA 0 0.9 5 1 0 318 #> 319 18 120.0 0 NA 0 29.0 6 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 6 2 0 321 #> 322 19 24.0 0 NA 0 6.6 5 1 0 322 #> 323 19 24.0 0 NA 0 33.0 6 2 0 323 #> 324 19 36.0 0 NA 0 5.3 5 1 0 324 #> 325 19 36.0 0 NA 0 28.0 6 2 0 325 #> 326 19 48.0 0 NA 0 3.6 5 1 0 326 #> 327 19 48.0 0 NA 0 18.0 6 2 0 327 #> 328 19 72.0 0 NA 0 2.7 5 1 0 328 #> 329 19 72.0 0 NA 0 18.0 6 2 0 329 #> 330 19 96.0 0 NA 0 1.4 5 1 0 330 #> 331 19 96.0 0 NA 0 17.0 6 2 0 331 #> 332 19 120.0 0 NA 0 1.1 5 1 0 332 #> 333 19 120.0 0 NA 0 26.0 6 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 6 2 0 335 #> 336 20 24.0 0 NA 0 9.6 5 1 0 336 #> 337 20 24.0 0 NA 0 41.0 6 2 0 337 #> 338 20 36.0 0 NA 0 8.0 5 1 0 338 #> 339 20 36.0 0 NA 0 30.0 6 2 0 339 #> 340 20 48.0 0 NA 0 6.6 5 1 0 340 #> 341 20 48.0 0 NA 0 22.0 6 2 0 341 #> 342 20 72.0 0 NA 0 5.6 5 1 0 342 #> 343 20 72.0 0 NA 0 23.0 6 2 0 343 #> 344 20 96.0 0 NA 0 3.5 5 1 0 344 #> 345 20 96.0 0 NA 0 23.0 6 2 0 345 #> 346 20 120.0 0 NA 0 2.3 5 1 0 346 #> 347 20 120.0 0 NA 0 35.0 6 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 6 2 0 349 #> 350 21 24.0 0 NA 0 7.3 5 1 0 350 #> 351 21 24.0 0 NA 0 46.0 6 2 0 351 #> 352 21 36.0 0 NA 0 6.1 5 1 0 352 #> 353 21 36.0 0 NA 0 27.0 6 2 0 353 #> 354 21 48.0 0 NA 0 4.3 5 1 0 354 #> 355 21 48.0 0 NA 0 22.0 6 2 0 355 #> 356 21 72.0 0 NA 0 3.2 5 1 0 356 #> 357 21 72.0 0 NA 0 36.0 6 2 0 357 #> 358 21 96.0 0 NA 0 2.3 5 1 0 358 #> 359 21 96.0 0 NA 0 40.0 6 2 0 359 #> 360 21 120.0 0 NA 0 1.9 5 1 0 360 #> 361 21 120.0 0 NA 0 44.0 6 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 6 2 0 363 #> 364 22 24.0 0 NA 0 8.9 5 1 0 364 #> 365 22 24.0 0 NA 0 35.0 6 2 0 365 #> 366 22 36.0 0 NA 0 8.4 5 1 0 366 #> 367 22 36.0 0 NA 0 27.0 6 2 0 367 #> 368 22 48.0 0 NA 0 8.0 5 1 0 368 #> 369 22 48.0 0 NA 0 23.0 6 2 0 369 #> 370 22 72.0 0 NA 0 4.4 5 1 0 370 #> 371 22 72.0 0 NA 0 27.0 6 2 0 371 #> 372 22 96.0 0 NA 0 3.2 5 1 0 372 #> 373 22 96.0 0 NA 0 43.0 6 2 0 373 #> 374 22 120.0 0 NA 0 1.7 5 1 0 374 #> 375 22 120.0 0 NA 0 43.0 6 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 6 2 0 377 #> 378 23 24.0 0 NA 0 9.8 5 1 0 378 #> 379 23 24.0 0 NA 0 34.0 6 2 0 379 #> 380 23 36.0 0 NA 0 8.4 5 1 0 380 #> 381 23 36.0 0 NA 0 24.0 6 2 0 381 #> 382 23 48.0 0 NA 0 6.6 5 1 0 382 #> 383 23 48.0 0 NA 0 15.0 6 2 0 383 #> 384 23 72.0 0 NA 0 4.8 5 1 0 384 #> 385 23 72.0 0 NA 0 15.0 6 2 0 385 #> 386 23 96.0 0 NA 0 3.2 5 1 0 386 #> 387 23 96.0 0 NA 0 19.0 6 2 0 387 #> 388 23 120.0 0 NA 0 2.4 5 1 0 388 #> 389 23 120.0 0 NA 0 19.0 6 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 6 2 0 391 #> 392 24 24.0 0 NA 0 8.2 5 1 0 392 #> 393 24 24.0 0 NA 0 37.0 6 2 0 393 #> 394 24 36.0 0 NA 0 7.5 5 1 0 394 #> 395 24 36.0 0 NA 0 20.0 6 2 0 395 #> 396 24 48.0 0 NA 0 6.8 5 1 0 396 #> 397 24 48.0 0 NA 0 20.0 6 2 0 397 #> 398 24 72.0 0 NA 0 5.5 5 1 0 398 #> 399 24 72.0 0 NA 0 26.0 6 2 0 399 #> 400 24 96.0 0 NA 0 4.5 5 1 0 400 #> 401 24 96.0 0 NA 0 28.0 6 2 0 401 #> 402 24 120.0 0 NA 0 3.7 5 1 0 402 #> 403 24 120.0 0 NA 0 50.0 6 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 6 2 0 405 #> 406 25 24.0 0 NA 0 11.0 5 1 0 406 #> 407 25 24.0 0 NA 0 32.0 6 2 0 407 #> 408 25 36.0 0 NA 0 10.0 5 1 0 408 #> 409 25 36.0 0 NA 0 20.0 6 2 0 409 #> 410 25 48.0 0 NA 0 8.2 5 1 0 410 #> 411 25 48.0 0 NA 0 17.0 6 2 0 411 #> 412 25 72.0 0 NA 0 6.0 5 1 0 412 #> 413 25 72.0 0 NA 0 19.0 6 2 0 413 #> 414 25 96.0 0 NA 0 3.7 5 1 0 414 #> 415 25 96.0 0 NA 0 21.0 6 2 0 415 #> 416 25 120.0 0 NA 0 2.6 5 1 0 416 #> 417 25 120.0 0 NA 0 30.0 6 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 6 2 0 419 #> 420 26 24.0 0 NA 0 10.0 5 1 0 420 #> 421 26 24.0 0 NA 0 41.0 6 2 0 421 #> 422 26 36.0 0 NA 0 9.0 5 1 0 422 #> 423 26 36.0 0 NA 0 28.0 6 2 0 423 #> 424 26 48.0 0 NA 0 7.3 5 1 0 424 #> 425 26 48.0 0 NA 0 19.0 6 2 0 425 #> 426 26 72.0 0 NA 0 5.2 5 1 0 426 #> 427 26 72.0 0 NA 0 17.0 6 2 0 427 #> 428 26 96.0 0 NA 0 3.7 5 1 0 428 #> 429 26 96.0 0 NA 0 17.0 6 2 0 429 #> 430 26 120.0 0 NA 0 2.7 5 1 0 430 #> 431 26 120.0 0 NA 0 24.0 6 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 6 2 0 433 #> 434 27 24.0 0 NA 0 11.8 5 1 0 434 #> 435 27 24.0 0 NA 0 32.0 6 2 0 435 #> 436 27 36.0 0 NA 0 9.2 5 1 0 436 #> 437 27 36.0 0 NA 0 21.0 6 2 0 437 #> 438 27 48.0 0 NA 0 7.7 5 1 0 438 #> 439 27 48.0 0 NA 0 19.0 6 2 0 439 #> 440 27 72.0 0 NA 0 4.9 5 1 0 440 #> 441 27 72.0 0 NA 0 22.0 6 2 0 441 #> 442 27 96.0 0 NA 0 3.4 5 1 0 442 #> 443 27 96.0 0 NA 0 33.0 6 2 0 443 #> 444 27 120.0 0 NA 0 2.7 5 1 0 444 #> 445 27 120.0 0 NA 0 46.0 6 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 6 2 0 447 #> 448 28 24.0 0 NA 0 10.1 5 1 0 448 #> 449 28 24.0 0 NA 0 39.0 6 2 0 449 #> 450 28 36.0 0 NA 0 8.0 5 1 0 450 #> 451 28 36.0 0 NA 0 25.0 6 2 0 451 #> 452 28 48.0 0 NA 0 6.0 5 1 0 452 #> 453 28 48.0 0 NA 0 16.0 6 2 0 453 #> 454 28 72.0 0 NA 0 4.9 5 1 0 454 #> 455 28 72.0 0 NA 0 14.0 6 2 0 455 #> 456 28 96.0 0 NA 0 3.4 5 1 0 456 #> 457 28 96.0 0 NA 0 15.0 6 2 0 457 #> 458 28 120.0 0 NA 0 2.0 5 1 0 458 #> 459 28 120.0 0 NA 0 20.0 6 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 6 2 0 461 #> 462 29 24.0 0 NA 0 8.3 5 1 0 462 #> 463 29 24.0 0 NA 0 35.0 6 2 0 463 #> 464 29 36.0 0 NA 0 7.0 5 1 0 464 #> 465 29 36.0 0 NA 0 21.0 6 2 0 465 #> 466 29 48.0 0 NA 0 5.6 5 1 0 466 #> 467 29 48.0 0 NA 0 18.0 6 2 0 467 #> 468 29 72.0 0 NA 0 4.1 5 1 0 468 #> 469 29 72.0 0 NA 0 20.0 6 2 0 469 #> 470 29 96.0 0 NA 0 3.1 5 1 0 470 #> 471 29 96.0 0 NA 0 29.0 6 2 0 471 #> 472 29 120.0 0 NA 0 2.2 5 1 0 472 #> 473 29 120.0 0 NA 0 41.0 6 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 6 2 0 475 #> 476 30 24.0 0 NA 0 9.9 5 1 0 476 #> 477 30 24.0 0 NA 0 45.0 6 2 0 477 #> 478 30 36.0 0 NA 0 7.5 5 1 0 478 #> 479 30 36.0 0 NA 0 24.0 6 2 0 479 #> 480 30 48.0 0 NA 0 6.5 5 1 0 480 #> 481 30 48.0 0 NA 0 23.0 6 2 0 481 #> 482 30 72.0 0 NA 0 4.1 5 1 0 482 #> 483 30 72.0 0 NA 0 26.0 6 2 0 483 #> 484 30 96.0 0 NA 0 2.9 5 1 0 484 #> 485 30 96.0 0 NA 0 28.0 6 2 0 485 #> 486 30 120.0 0 NA 0 2.3 5 1 0 486 #> 487 30 120.0 0 NA 0 39.0 6 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 6 2 0 489 #> 490 31 24.0 0 NA 0 9.5 5 1 0 490 #> 491 31 24.0 0 NA 0 45.0 6 2 0 491 #> 492 31 36.0 0 NA 0 7.8 5 1 0 492 #> 493 31 36.0 0 NA 0 30.0 6 2 0 493 #> 494 31 48.0 0 NA 0 6.4 5 1 0 494 #> 495 31 48.0 0 NA 0 24.0 6 2 0 495 #> 496 31 72.0 0 NA 0 4.5 5 1 0 496 #> 497 31 72.0 0 NA 0 22.0 6 2 0 497 #> 498 31 96.0 0 NA 0 3.4 5 1 0 498 #> 499 31 96.0 0 NA 0 28.0 6 2 0 499 #> 500 31 120.0 0 NA 0 2.5 5 1 0 500 #> 501 31 120.0 0 NA 0 42.0 6 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 6 2 0 503 #> 504 32 24.0 0 NA 0 8.9 5 1 0 504 #> 505 32 24.0 0 NA 0 36.0 6 2 0 505 #> 506 32 36.0 0 NA 0 7.7 5 1 0 506 #> 507 32 36.0 0 NA 0 27.0 6 2 0 507 #> 508 32 48.0 0 NA 0 6.9 5 1 0 508 #> 509 32 48.0 0 NA 0 24.0 6 2 0 509 #> 510 32 72.0 0 NA 0 4.4 5 1 0 510 #> 511 32 72.0 0 NA 0 23.0 6 2 0 511 #> 512 32 96.0 0 NA 0 3.5 5 1 0 512 #> 513 32 96.0 0 NA 0 20.0 6 2 0 513 #> 514 32 120.0 0 NA 0 2.5 5 1 0 514 #> 515 32 120.0 0 NA 0 22.0 6 2 0 515 bblDatToRxode(pk.turnover.emax3, nlmixr2data::warfarin) #> #> #> ℹ parameter labels from comments are typically ignored in non-interactive mode #> ℹ Need to run with the source intact to parse comments #> ID TIME EVID AMT II DV CMT DVID SS nlmixrRowNums #> 1 1 0.0 1 100.0 0 NA 1 0 0 1 #> 2 1 0.5 0 NA 0 0.0 5 1 0 2 #> 3 1 1.0 0 NA 0 1.9 5 1 0 3 #> 4 1 2.0 0 NA 0 3.3 5 1 0 4 #> 5 1 3.0 0 NA 0 6.6 5 1 0 5 #> 6 1 6.0 0 NA 0 9.1 5 1 0 6 #> 7 1 9.0 0 NA 0 10.8 5 1 0 7 #> 8 1 12.0 0 NA 0 8.6 5 1 0 8 #> 9 1 24.0 0 NA 0 5.6 5 1 0 9 #> 10 1 24.0 0 NA 0 44.0 6 2 0 10 #> 11 1 36.0 0 NA 0 4.0 5 1 0 11 #> 12 1 36.0 0 NA 0 27.0 6 2 0 12 #> 13 1 48.0 0 NA 0 2.7 5 1 0 13 #> 14 1 48.0 0 NA 0 28.0 6 2 0 14 #> 15 1 72.0 0 NA 0 0.8 5 1 0 15 #> 16 1 72.0 0 NA 0 31.0 6 2 0 16 #> 17 1 96.0 0 NA 0 60.0 6 2 0 17 #> 18 1 120.0 0 NA 0 65.0 6 2 0 18 #> 19 1 144.0 0 NA 0 71.0 6 2 0 19 #> 20 2 0.0 1 100.0 0 NA 1 0 0 20 #> 21 2 0.0 0 NA 0 100.0 6 2 0 21 #> 22 2 24.0 0 NA 0 9.2 5 1 0 22 #> 23 2 24.0 0 NA 0 49.0 6 2 0 23 #> 24 2 36.0 0 NA 0 8.5 5 1 0 24 #> 25 2 36.0 0 NA 0 32.0 6 2 0 25 #> 26 2 48.0 0 NA 0 6.4 5 1 0 26 #> 27 2 48.0 0 NA 0 26.0 6 2 0 27 #> 28 2 72.0 0 NA 0 4.8 5 1 0 28 #> 29 2 72.0 0 NA 0 22.0 6 2 0 29 #> 30 2 96.0 0 NA 0 3.1 5 1 0 30 #> 31 2 96.0 0 NA 0 28.0 6 2 0 31 #> 32 2 120.0 0 NA 0 2.5 5 1 0 32 #> 33 2 120.0 0 NA 0 33.0 6 2 0 33 #> 34 3 0.0 1 100.0 0 NA 1 0 0 34 #> 35 3 0.0 0 NA 0 100.0 6 2 0 35 #> 36 3 0.5 0 NA 0 0.0 5 1 0 36 #> 37 3 2.0 0 NA 0 8.4 5 1 0 37 #> 38 3 3.0 0 NA 0 9.7 5 1 0 38 #> 39 3 6.0 0 NA 0 9.8 5 1 0 39 #> 40 3 12.0 0 NA 0 11.0 5 1 0 40 #> 41 3 24.0 0 NA 0 8.3 5 1 0 41 #> 42 3 24.0 0 NA 0 46.0 6 2 0 42 #> 43 3 36.0 0 NA 0 7.7 5 1 0 43 #> 44 3 36.0 0 NA 0 22.0 6 2 0 44 #> 45 3 48.0 0 NA 0 6.3 5 1 0 45 #> 46 3 48.0 0 NA 0 19.0 6 2 0 46 #> 47 3 72.0 0 NA 0 4.1 5 1 0 47 #> 48 3 72.0 0 NA 0 20.0 6 2 0 48 #> 49 3 96.0 0 NA 0 3.0 5 1 0 49 #> 50 3 96.0 0 NA 0 42.0 6 2 0 50 #> 51 3 120.0 0 NA 0 1.4 5 1 0 51 #> 52 3 120.0 0 NA 0 49.0 6 2 0 52 #> 53 3 144.0 0 NA 0 54.0 6 2 0 53 #> 54 4 0.0 1 120.0 0 NA 1 0 0 54 #> 55 4 0.0 0 NA 0 100.0 6 2 0 55 #> 56 4 3.0 0 NA 0 12.0 5 1 0 56 #> 57 4 6.0 0 NA 0 13.2 5 1 0 57 #> 58 4 9.0 0 NA 0 14.4 5 1 0 58 #> 59 4 24.0 0 NA 0 9.6 5 1 0 59 #> 60 4 24.0 0 NA 0 30.0 6 2 0 60 #> 61 4 36.0 0 NA 0 8.2 5 1 0 61 #> 62 4 36.0 0 NA 0 24.0 6 2 0 62 #> 63 4 48.0 0 NA 0 7.8 5 1 0 63 #> 64 4 48.0 0 NA 0 13.0 6 2 0 64 #> 65 4 72.0 0 NA 0 5.8 5 1 0 65 #> 66 4 72.0 0 NA 0 9.0 6 2 0 66 #> 67 4 96.0 0 NA 0 4.3 5 1 0 67 #> 68 4 96.0 0 NA 0 9.0 6 2 0 68 #> 69 4 120.0 0 NA 0 3.0 5 1 0 69 #> 70 4 120.0 0 NA 0 11.0 6 2 0 70 #> 71 4 144.0 0 NA 0 12.0 6 2 0 71 #> 72 5 0.0 1 60.0 0 NA 1 0 0 72 #> 73 5 0.0 0 NA 0 82.0 6 2 0 73 #> 74 5 3.0 0 NA 0 11.1 5 1 0 74 #> 75 5 6.0 0 NA 0 11.9 5 1 0 75 #> 76 5 9.0 0 NA 0 9.8 5 1 0 76 #> 77 5 12.0 0 NA 0 11.0 5 1 0 77 #> 78 5 24.0 0 NA 0 8.5 5 1 0 78 #> 79 5 24.0 0 NA 0 43.0 6 2 0 79 #> 80 5 36.0 0 NA 0 7.6 5 1 0 80 #> 81 5 36.0 0 NA 0 25.0 6 2 0 81 #> 82 5 48.0 0 NA 0 5.4 5 1 0 82 #> 83 5 48.0 0 NA 0 18.0 6 2 0 83 #> 84 5 72.0 0 NA 0 4.5 5 1 0 84 #> 85 5 72.0 0 NA 0 17.0 6 2 0 85 #> 86 5 96.0 0 NA 0 3.3 5 1 0 86 #> 87 5 96.0 0 NA 0 23.0 6 2 0 87 #> 88 5 120.0 0 NA 0 2.3 5 1 0 88 #> 89 5 120.0 0 NA 0 29.0 6 2 0 89 #> 90 5 144.0 0 NA 0 41.0 6 2 0 90 #> 91 6 0.0 1 113.0 0 NA 1 0 0 91 #> 92 6 0.0 0 NA 0 100.0 6 2 0 92 #> 93 6 6.0 0 NA 0 8.6 5 1 0 93 #> 94 6 12.0 0 NA 0 8.6 5 1 0 94 #> 95 6 24.0 0 NA 0 7.0 5 1 0 95 #> 96 6 24.0 0 NA 0 34.0 6 2 0 96 #> 97 6 36.0 0 NA 0 5.7 5 1 0 97 #> 98 6 36.0 0 NA 0 23.0 6 2 0 98 #> 99 6 48.0 0 NA 0 4.7 5 1 0 99 #> 100 6 48.0 0 NA 0 20.0 6 2 0 100 #> 101 6 72.0 0 NA 0 3.3 5 1 0 101 #> 102 6 72.0 0 NA 0 16.0 6 2 0 102 #> 103 6 96.0 0 NA 0 2.3 5 1 0 103 #> 104 6 96.0 0 NA 0 17.0 6 2 0 104 #> 105 6 120.0 0 NA 0 1.7 5 1 0 105 #> 106 6 120.0 0 NA 0 18.0 6 2 0 106 #> 107 6 144.0 0 NA 0 25.0 6 2 0 107 #> 108 7 0.0 1 90.0 0 NA 1 0 0 108 #> 109 7 3.0 0 NA 0 13.4 5 1 0 109 #> 110 7 6.0 0 NA 0 12.4 5 1 0 110 #> 111 7 9.0 0 NA 0 12.7 5 1 0 111 #> 112 7 12.0 0 NA 0 8.8 5 1 0 112 #> 113 7 24.0 0 NA 0 6.1 5 1 0 113 #> 114 7 24.0 0 NA 0 36.0 6 2 0 114 #> 115 7 36.0 0 NA 0 3.5 5 1 0 115 #> 116 7 36.0 0 NA 0 33.0 6 2 0 116 #> 117 7 48.0 0 NA 0 1.8 5 1 0 117 #> 118 7 48.0 0 NA 0 28.0 6 2 0 118 #> 119 7 72.0 0 NA 0 1.5 5 1 0 119 #> 120 7 72.0 0 NA 0 52.0 6 2 0 120 #> 121 7 96.0 0 NA 0 1.0 5 1 0 121 #> 122 7 96.0 0 NA 0 80.0 6 2 0 122 #> 123 7 120.0 0 NA 0 90.0 6 2 0 123 #> 124 7 144.0 0 NA 0 100.0 6 2 0 124 #> 125 8 0.0 1 135.0 0 NA 1 0 0 125 #> 126 8 0.0 0 NA 0 88.0 6 2 0 126 #> 127 8 2.0 0 NA 0 17.6 5 1 0 127 #> 128 8 3.0 0 NA 0 17.3 5 1 0 128 #> 129 8 6.0 0 NA 0 15.0 5 1 0 129 #> 130 8 9.0 0 NA 0 15.0 5 1 0 130 #> 131 8 12.0 0 NA 0 12.4 5 1 0 131 #> 132 8 24.0 0 NA 0 7.9 5 1 0 132 #> 133 8 24.0 0 NA 0 35.0 6 2 0 133 #> 134 8 36.0 0 NA 0 7.9 5 1 0 134 #> 135 8 36.0 0 NA 0 20.0 6 2 0 135 #> 136 8 48.0 0 NA 0 5.1 5 1 0 136 #> 137 8 48.0 0 NA 0 12.0 6 2 0 137 #> 138 8 72.0 0 NA 0 3.6 5 1 0 138 #> 139 8 72.0 0 NA 0 16.0 6 2 0 139 #> 140 8 96.0 0 NA 0 2.4 5 1 0 140 #> 141 8 96.0 0 NA 0 23.0 6 2 0 141 #> 142 8 120.0 0 NA 0 2.0 5 1 0 142 #> 143 8 120.0 0 NA 0 36.0 6 2 0 143 #> 144 8 144.0 0 NA 0 48.0 6 2 0 144 #> 145 9 0.0 1 75.0 0 NA 1 0 0 145 #> 146 9 0.0 0 NA 0 92.0 6 2 0 146 #> 147 9 0.5 0 NA 0 0.0 5 1 0 147 #> 148 9 1.0 0 NA 0 1.0 5 1 0 148 #> 149 9 2.0 0 NA 0 4.6 5 1 0 149 #> 150 9 3.0 0 NA 0 12.7 5 1 0 150 #> 151 9 3.0 0 NA 0 8.0 5 1 0 151 #> 152 9 6.0 0 NA 0 12.7 5 1 0 152 #> 153 9 6.0 0 NA 0 11.5 5 1 0 153 #> 154 9 9.0 0 NA 0 12.9 5 1 0 154 #> 155 9 9.0 0 NA 0 11.4 5 1 0 155 #> 156 9 12.0 0 NA 0 11.4 5 1 0 156 #> 157 9 12.0 0 NA 0 11.0 5 1 0 157 #> 158 9 24.0 0 NA 0 9.1 5 1 0 158 #> 159 9 24.0 0 NA 0 33.0 6 2 0 159 #> 160 9 36.0 0 NA 0 8.2 5 1 0 160 #> 161 9 36.0 0 NA 0 22.0 6 2 0 161 #> 162 9 48.0 0 NA 0 5.9 5 1 0 162 #> 163 9 48.0 0 NA 0 16.0 6 2 0 163 #> 164 9 72.0 0 NA 0 3.6 5 1 0 164 #> 165 9 72.0 0 NA 0 18.0 6 2 0 165 #> 166 9 96.0 0 NA 0 1.7 5 1 0 166 #> 167 9 96.0 0 NA 0 32.0 6 2 0 167 #> 168 9 120.0 0 NA 0 1.1 5 1 0 168 #> 169 9 120.0 0 NA 0 30.0 6 2 0 169 #> 170 9 144.0 0 NA 0 45.0 6 2 0 170 #> 171 10 0.0 1 105.0 0 NA 1 0 0 171 #> 172 10 0.0 0 NA 0 90.0 6 2 0 172 #> 173 10 24.0 0 NA 0 8.6 5 1 0 173 #> 174 10 24.0 0 NA 0 39.0 6 2 0 174 #> 175 10 36.0 0 NA 0 8.0 5 1 0 175 #> 176 10 36.0 0 NA 0 22.0 6 2 0 176 #> 177 10 48.0 0 NA 0 6.0 5 1 0 177 #> 178 10 48.0 0 NA 0 17.0 6 2 0 178 #> 179 10 72.0 0 NA 0 4.4 5 1 0 179 #> 180 10 72.0 0 NA 0 17.0 6 2 0 180 #> 181 10 96.0 0 NA 0 3.6 5 1 0 181 #> 182 10 96.0 0 NA 0 22.0 6 2 0 182 #> 183 10 120.0 0 NA 0 2.8 5 1 0 183 #> 184 10 120.0 0 NA 0 25.0 6 2 0 184 #> 185 10 144.0 0 NA 0 33.0 6 2 0 185 #> 186 11 0.0 1 123.0 0 NA 1 0 0 186 #> 187 11 0.0 0 NA 0 100.0 6 2 0 187 #> 188 11 1.5 0 NA 0 11.4 5 1 0 188 #> 189 11 3.0 0 NA 0 15.4 5 1 0 189 #> 190 11 6.0 0 NA 0 17.5 5 1 0 190 #> 191 11 12.0 0 NA 0 14.0 5 1 0 191 #> 192 11 24.0 0 NA 0 9.0 5 1 0 192 #> 193 11 24.0 0 NA 0 37.0 6 2 0 193 #> 194 11 36.0 0 NA 0 8.9 5 1 0 194 #> 195 11 36.0 0 NA 0 24.0 6 2 0 195 #> 196 11 48.0 0 NA 0 6.6 5 1 0 196 #> 197 11 48.0 0 NA 0 14.0 6 2 0 197 #> 198 11 72.0 0 NA 0 4.2 5 1 0 198 #> 199 11 72.0 0 NA 0 11.0 6 2 0 199 #> 200 11 96.0 0 NA 0 3.6 5 1 0 200 #> 201 11 96.0 0 NA 0 14.0 6 2 0 201 #> 202 11 120.0 0 NA 0 2.6 5 1 0 202 #> 203 11 120.0 0 NA 0 23.0 6 2 0 203 #> 204 11 144.0 0 NA 0 33.0 6 2 0 204 #> 205 12 0.0 1 113.0 0 NA 1 0 0 205 #> 206 12 0.0 0 NA 0 85.0 6 2 0 206 #> 207 12 1.5 0 NA 0 0.6 5 1 0 207 #> 208 12 3.0 0 NA 0 2.8 5 1 0 208 #> 209 12 6.0 0 NA 0 13.8 5 1 0 209 #> 210 12 9.0 0 NA 0 15.0 5 1 0 210 #> 211 12 24.0 0 NA 0 10.5 5 1 0 211 #> 212 12 24.0 0 NA 0 25.0 6 2 0 212 #> 213 12 36.0 0 NA 0 9.1 5 1 0 213 #> 214 12 36.0 0 NA 0 15.0 6 2 0 214 #> 215 12 48.0 0 NA 0 6.6 5 1 0 215 #> 216 12 48.0 0 NA 0 11.0 6 2 0 216 #> 217 12 72.0 0 NA 0 4.9 5 1 0 217 #> 218 12 96.0 0 NA 0 2.4 5 1 0 218 #> 219 12 120.0 0 NA 0 1.9 5 1 0 219 #> 220 13 0.0 1 113.0 0 NA 1 0 0 220 #> 221 13 0.0 0 NA 0 88.0 6 2 0 221 #> 222 13 1.5 0 NA 0 3.6 5 1 0 222 #> 223 13 3.0 0 NA 0 12.9 5 1 0 223 #> 224 13 6.0 0 NA 0 12.9 5 1 0 224 #> 225 13 9.0 0 NA 0 10.2 5 1 0 225 #> 226 13 24.0 0 NA 0 6.4 5 1 0 226 #> 227 13 24.0 0 NA 0 41.0 6 2 0 227 #> 228 13 36.0 0 NA 0 6.9 5 1 0 228 #> 229 13 36.0 0 NA 0 23.0 6 2 0 229 #> 230 13 48.0 0 NA 0 4.5 5 1 0 230 #> 231 13 48.0 0 NA 0 16.0 6 2 0 231 #> 232 13 72.0 0 NA 0 3.2 5 1 0 232 #> 233 13 72.0 0 NA 0 14.0 6 2 0 233 #> 234 13 96.0 0 NA 0 2.4 5 1 0 234 #> 235 13 96.0 0 NA 0 18.0 6 2 0 235 #> 236 13 120.0 0 NA 0 1.3 5 1 0 236 #> 237 13 120.0 0 NA 0 22.0 6 2 0 237 #> 238 13 144.0 0 NA 0 35.0 6 2 0 238 #> 239 14 0.0 1 75.0 0 NA 1 0 0 239 #> 240 14 0.0 0 NA 0 85.0 6 2 0 240 #> 241 14 0.5 0 NA 0 0.0 5 1 0 241 #> 242 14 1.0 0 NA 0 2.7 5 1 0 242 #> 243 14 2.0 0 NA 0 11.6 5 1 0 243 #> 244 14 3.0 0 NA 0 11.6 5 1 0 244 #> 245 14 6.0 0 NA 0 11.3 5 1 0 245 #> 246 14 9.0 0 NA 0 9.7 5 1 0 246 #> 247 14 24.0 0 NA 0 6.5 5 1 0 247 #> 248 14 24.0 0 NA 0 32.0 6 2 0 248 #> 249 14 36.0 0 NA 0 5.2 5 1 0 249 #> 250 14 36.0 0 NA 0 22.0 6 2 0 250 #> 251 14 48.0 0 NA 0 3.6 5 1 0 251 #> 252 14 48.0 0 NA 0 21.0 6 2 0 252 #> 253 14 72.0 0 NA 0 2.4 5 1 0 253 #> 254 14 72.0 0 NA 0 28.0 6 2 0 254 #> 255 14 96.0 0 NA 0 0.9 5 1 0 255 #> 256 14 96.0 0 NA 0 38.0 6 2 0 256 #> 257 14 120.0 0 NA 0 46.0 6 2 0 257 #> 258 14 144.0 0 NA 0 65.0 6 2 0 258 #> 259 15 0.0 1 85.0 0 NA 1 0 0 259 #> 260 15 0.0 0 NA 0 100.0 6 2 0 260 #> 261 15 1.0 0 NA 0 6.6 5 1 0 261 #> 262 15 3.0 0 NA 0 11.9 5 1 0 262 #> 263 15 6.0 0 NA 0 11.7 5 1 0 263 #> 264 15 9.0 0 NA 0 12.2 5 1 0 264 #> 265 15 24.0 0 NA 0 8.1 5 1 0 265 #> 266 15 24.0 0 NA 0 43.0 6 2 0 266 #> 267 15 36.0 0 NA 0 7.4 5 1 0 267 #> 268 15 36.0 0 NA 0 26.0 6 2 0 268 #> 269 15 48.0 0 NA 0 6.8 5 1 0 269 #> 270 15 48.0 0 NA 0 15.0 6 2 0 270 #> 271 15 72.0 0 NA 0 5.3 5 1 0 271 #> 272 15 72.0 0 NA 0 13.0 6 2 0 272 #> 273 15 96.0 0 NA 0 3.0 5 1 0 273 #> 274 15 96.0 0 NA 0 21.0 6 2 0 274 #> 275 15 120.0 0 NA 0 2.0 5 1 0 275 #> 276 15 120.0 0 NA 0 28.0 6 2 0 276 #> 277 15 144.0 0 NA 0 39.0 6 2 0 277 #> 278 16 0.0 1 87.0 0 NA 1 0 0 278 #> 279 16 0.0 0 NA 0 100.0 6 2 0 279 #> 280 16 24.0 0 NA 0 10.4 5 1 0 280 #> 281 16 24.0 0 NA 0 42.0 6 2 0 281 #> 282 16 36.0 0 NA 0 8.9 5 1 0 282 #> 283 16 36.0 0 NA 0 32.0 6 2 0 283 #> 284 16 48.0 0 NA 0 7.0 5 1 0 284 #> 285 16 48.0 0 NA 0 26.0 6 2 0 285 #> 286 16 72.0 0 NA 0 4.4 5 1 0 286 #> 287 16 72.0 0 NA 0 31.0 6 2 0 287 #> 288 16 96.0 0 NA 0 3.2 5 1 0 288 #> 289 16 96.0 0 NA 0 33.0 6 2 0 289 #> 290 16 120.0 0 NA 0 2.4 5 1 0 290 #> 291 16 120.0 0 NA 0 54.0 6 2 0 291 #> 292 17 0.0 1 117.0 0 NA 1 0 0 292 #> 293 17 0.0 0 NA 0 100.0 6 2 0 293 #> 294 17 24.0 0 NA 0 7.6 5 1 0 294 #> 295 17 24.0 0 NA 0 35.0 6 2 0 295 #> 296 17 36.0 0 NA 0 6.4 5 1 0 296 #> 297 17 36.0 0 NA 0 23.0 6 2 0 297 #> 298 17 48.0 0 NA 0 6.0 5 1 0 298 #> 299 17 48.0 0 NA 0 17.0 6 2 0 299 #> 300 17 72.0 0 NA 0 4.0 5 1 0 300 #> 301 17 72.0 0 NA 0 18.0 6 2 0 301 #> 302 17 96.0 0 NA 0 3.1 5 1 0 302 #> 303 17 96.0 0 NA 0 18.0 6 2 0 303 #> 304 17 120.0 0 NA 0 2.0 5 1 0 304 #> 305 17 120.0 0 NA 0 21.0 6 2 0 305 #> 306 18 0.0 1 112.0 0 NA 1 0 0 306 #> 307 18 0.0 0 NA 0 100.0 6 2 0 307 #> 308 18 24.0 0 NA 0 7.6 5 1 0 308 #> 309 18 24.0 0 NA 0 32.0 6 2 0 309 #> 310 18 36.0 0 NA 0 6.6 5 1 0 310 #> 311 18 36.0 0 NA 0 20.0 6 2 0 311 #> 312 18 48.0 0 NA 0 5.4 5 1 0 312 #> 313 18 48.0 0 NA 0 18.0 6 2 0 313 #> 314 18 72.0 0 NA 0 3.4 5 1 0 314 #> 315 18 72.0 0 NA 0 18.0 6 2 0 315 #> 316 18 96.0 0 NA 0 1.2 5 1 0 316 #> 317 18 96.0 0 NA 0 19.0 6 2 0 317 #> 318 18 120.0 0 NA 0 0.9 5 1 0 318 #> 319 18 120.0 0 NA 0 29.0 6 2 0 319 #> 320 19 0.0 1 95.5 0 NA 1 0 0 320 #> 321 19 0.0 0 NA 0 100.0 6 2 0 321 #> 322 19 24.0 0 NA 0 6.6 5 1 0 322 #> 323 19 24.0 0 NA 0 33.0 6 2 0 323 #> 324 19 36.0 0 NA 0 5.3 5 1 0 324 #> 325 19 36.0 0 NA 0 28.0 6 2 0 325 #> 326 19 48.0 0 NA 0 3.6 5 1 0 326 #> 327 19 48.0 0 NA 0 18.0 6 2 0 327 #> 328 19 72.0 0 NA 0 2.7 5 1 0 328 #> 329 19 72.0 0 NA 0 18.0 6 2 0 329 #> 330 19 96.0 0 NA 0 1.4 5 1 0 330 #> 331 19 96.0 0 NA 0 17.0 6 2 0 331 #> 332 19 120.0 0 NA 0 1.1 5 1 0 332 #> 333 19 120.0 0 NA 0 26.0 6 2 0 333 #> 334 20 0.0 1 88.5 0 NA 1 0 0 334 #> 335 20 0.0 0 NA 0 100.0 6 2 0 335 #> 336 20 24.0 0 NA 0 9.6 5 1 0 336 #> 337 20 24.0 0 NA 0 41.0 6 2 0 337 #> 338 20 36.0 0 NA 0 8.0 5 1 0 338 #> 339 20 36.0 0 NA 0 30.0 6 2 0 339 #> 340 20 48.0 0 NA 0 6.6 5 1 0 340 #> 341 20 48.0 0 NA 0 22.0 6 2 0 341 #> 342 20 72.0 0 NA 0 5.6 5 1 0 342 #> 343 20 72.0 0 NA 0 23.0 6 2 0 343 #> 344 20 96.0 0 NA 0 3.5 5 1 0 344 #> 345 20 96.0 0 NA 0 23.0 6 2 0 345 #> 346 20 120.0 0 NA 0 2.3 5 1 0 346 #> 347 20 120.0 0 NA 0 35.0 6 2 0 347 #> 348 21 0.0 1 93.0 0 NA 1 0 0 348 #> 349 21 0.0 0 NA 0 100.0 6 2 0 349 #> 350 21 24.0 0 NA 0 7.3 5 1 0 350 #> 351 21 24.0 0 NA 0 46.0 6 2 0 351 #> 352 21 36.0 0 NA 0 6.1 5 1 0 352 #> 353 21 36.0 0 NA 0 27.0 6 2 0 353 #> 354 21 48.0 0 NA 0 4.3 5 1 0 354 #> 355 21 48.0 0 NA 0 22.0 6 2 0 355 #> 356 21 72.0 0 NA 0 3.2 5 1 0 356 #> 357 21 72.0 0 NA 0 36.0 6 2 0 357 #> 358 21 96.0 0 NA 0 2.3 5 1 0 358 #> 359 21 96.0 0 NA 0 40.0 6 2 0 359 #> 360 21 120.0 0 NA 0 1.9 5 1 0 360 #> 361 21 120.0 0 NA 0 44.0 6 2 0 361 #> 362 22 0.0 1 87.0 0 NA 1 0 0 362 #> 363 22 0.0 0 NA 0 100.0 6 2 0 363 #> 364 22 24.0 0 NA 0 8.9 5 1 0 364 #> 365 22 24.0 0 NA 0 35.0 6 2 0 365 #> 366 22 36.0 0 NA 0 8.4 5 1 0 366 #> 367 22 36.0 0 NA 0 27.0 6 2 0 367 #> 368 22 48.0 0 NA 0 8.0 5 1 0 368 #> 369 22 48.0 0 NA 0 23.0 6 2 0 369 #> 370 22 72.0 0 NA 0 4.4 5 1 0 370 #> 371 22 72.0 0 NA 0 27.0 6 2 0 371 #> 372 22 96.0 0 NA 0 3.2 5 1 0 372 #> 373 22 96.0 0 NA 0 43.0 6 2 0 373 #> 374 22 120.0 0 NA 0 1.7 5 1 0 374 #> 375 22 120.0 0 NA 0 43.0 6 2 0 375 #> 376 23 0.0 1 110.0 0 NA 1 0 0 376 #> 377 23 0.0 0 NA 0 100.0 6 2 0 377 #> 378 23 24.0 0 NA 0 9.8 5 1 0 378 #> 379 23 24.0 0 NA 0 34.0 6 2 0 379 #> 380 23 36.0 0 NA 0 8.4 5 1 0 380 #> 381 23 36.0 0 NA 0 24.0 6 2 0 381 #> 382 23 48.0 0 NA 0 6.6 5 1 0 382 #> 383 23 48.0 0 NA 0 15.0 6 2 0 383 #> 384 23 72.0 0 NA 0 4.8 5 1 0 384 #> 385 23 72.0 0 NA 0 15.0 6 2 0 385 #> 386 23 96.0 0 NA 0 3.2 5 1 0 386 #> 387 23 96.0 0 NA 0 19.0 6 2 0 387 #> 388 23 120.0 0 NA 0 2.4 5 1 0 388 #> 389 23 120.0 0 NA 0 19.0 6 2 0 389 #> 390 24 0.0 1 115.0 0 NA 1 0 0 390 #> 391 24 0.0 0 NA 0 88.0 6 2 0 391 #> 392 24 24.0 0 NA 0 8.2 5 1 0 392 #> 393 24 24.0 0 NA 0 37.0 6 2 0 393 #> 394 24 36.0 0 NA 0 7.5 5 1 0 394 #> 395 24 36.0 0 NA 0 20.0 6 2 0 395 #> 396 24 48.0 0 NA 0 6.8 5 1 0 396 #> 397 24 48.0 0 NA 0 20.0 6 2 0 397 #> 398 24 72.0 0 NA 0 5.5 5 1 0 398 #> 399 24 72.0 0 NA 0 26.0 6 2 0 399 #> 400 24 96.0 0 NA 0 4.5 5 1 0 400 #> 401 24 96.0 0 NA 0 28.0 6 2 0 401 #> 402 24 120.0 0 NA 0 3.7 5 1 0 402 #> 403 24 120.0 0 NA 0 50.0 6 2 0 403 #> 404 25 0.0 1 112.0 0 NA 1 0 0 404 #> 405 25 0.0 0 NA 0 100.0 6 2 0 405 #> 406 25 24.0 0 NA 0 11.0 5 1 0 406 #> 407 25 24.0 0 NA 0 32.0 6 2 0 407 #> 408 25 36.0 0 NA 0 10.0 5 1 0 408 #> 409 25 36.0 0 NA 0 20.0 6 2 0 409 #> 410 25 48.0 0 NA 0 8.2 5 1 0 410 #> 411 25 48.0 0 NA 0 17.0 6 2 0 411 #> 412 25 72.0 0 NA 0 6.0 5 1 0 412 #> 413 25 72.0 0 NA 0 19.0 6 2 0 413 #> 414 25 96.0 0 NA 0 3.7 5 1 0 414 #> 415 25 96.0 0 NA 0 21.0 6 2 0 415 #> 416 25 120.0 0 NA 0 2.6 5 1 0 416 #> 417 25 120.0 0 NA 0 30.0 6 2 0 417 #> 418 26 0.0 1 120.0 0 NA 1 0 0 418 #> 419 26 0.0 0 NA 0 100.0 6 2 0 419 #> 420 26 24.0 0 NA 0 10.0 5 1 0 420 #> 421 26 24.0 0 NA 0 41.0 6 2 0 421 #> 422 26 36.0 0 NA 0 9.0 5 1 0 422 #> 423 26 36.0 0 NA 0 28.0 6 2 0 423 #> 424 26 48.0 0 NA 0 7.3 5 1 0 424 #> 425 26 48.0 0 NA 0 19.0 6 2 0 425 #> 426 26 72.0 0 NA 0 5.2 5 1 0 426 #> 427 26 72.0 0 NA 0 17.0 6 2 0 427 #> 428 26 96.0 0 NA 0 3.7 5 1 0 428 #> 429 26 96.0 0 NA 0 17.0 6 2 0 429 #> 430 26 120.0 0 NA 0 2.7 5 1 0 430 #> 431 26 120.0 0 NA 0 24.0 6 2 0 431 #> 432 27 0.0 1 120.0 0 NA 1 0 0 432 #> 433 27 0.0 0 NA 0 100.0 6 2 0 433 #> 434 27 24.0 0 NA 0 11.8 5 1 0 434 #> 435 27 24.0 0 NA 0 32.0 6 2 0 435 #> 436 27 36.0 0 NA 0 9.2 5 1 0 436 #> 437 27 36.0 0 NA 0 21.0 6 2 0 437 #> 438 27 48.0 0 NA 0 7.7 5 1 0 438 #> 439 27 48.0 0 NA 0 19.0 6 2 0 439 #> 440 27 72.0 0 NA 0 4.9 5 1 0 440 #> 441 27 72.0 0 NA 0 22.0 6 2 0 441 #> 442 27 96.0 0 NA 0 3.4 5 1 0 442 #> 443 27 96.0 0 NA 0 33.0 6 2 0 443 #> 444 27 120.0 0 NA 0 2.7 5 1 0 444 #> 445 27 120.0 0 NA 0 46.0 6 2 0 445 #> 446 28 0.0 1 120.0 0 NA 1 0 0 446 #> 447 28 0.0 0 NA 0 100.0 6 2 0 447 #> 448 28 24.0 0 NA 0 10.1 5 1 0 448 #> 449 28 24.0 0 NA 0 39.0 6 2 0 449 #> 450 28 36.0 0 NA 0 8.0 5 1 0 450 #> 451 28 36.0 0 NA 0 25.0 6 2 0 451 #> 452 28 48.0 0 NA 0 6.0 5 1 0 452 #> 453 28 48.0 0 NA 0 16.0 6 2 0 453 #> 454 28 72.0 0 NA 0 4.9 5 1 0 454 #> 455 28 72.0 0 NA 0 14.0 6 2 0 455 #> 456 28 96.0 0 NA 0 3.4 5 1 0 456 #> 457 28 96.0 0 NA 0 15.0 6 2 0 457 #> 458 28 120.0 0 NA 0 2.0 5 1 0 458 #> 459 28 120.0 0 NA 0 20.0 6 2 0 459 #> 460 29 0.0 1 153.0 0 NA 1 0 0 460 #> 461 29 0.0 0 NA 0 86.0 6 2 0 461 #> 462 29 24.0 0 NA 0 8.3 5 1 0 462 #> 463 29 24.0 0 NA 0 35.0 6 2 0 463 #> 464 29 36.0 0 NA 0 7.0 5 1 0 464 #> 465 29 36.0 0 NA 0 21.0 6 2 0 465 #> 466 29 48.0 0 NA 0 5.6 5 1 0 466 #> 467 29 48.0 0 NA 0 18.0 6 2 0 467 #> 468 29 72.0 0 NA 0 4.1 5 1 0 468 #> 469 29 72.0 0 NA 0 20.0 6 2 0 469 #> 470 29 96.0 0 NA 0 3.1 5 1 0 470 #> 471 29 96.0 0 NA 0 29.0 6 2 0 471 #> 472 29 120.0 0 NA 0 2.2 5 1 0 472 #> 473 29 120.0 0 NA 0 41.0 6 2 0 473 #> 474 30 0.0 1 105.0 0 NA 1 0 0 474 #> 475 30 0.0 0 NA 0 100.0 6 2 0 475 #> 476 30 24.0 0 NA 0 9.9 5 1 0 476 #> 477 30 24.0 0 NA 0 45.0 6 2 0 477 #> 478 30 36.0 0 NA 0 7.5 5 1 0 478 #> 479 30 36.0 0 NA 0 24.0 6 2 0 479 #> 480 30 48.0 0 NA 0 6.5 5 1 0 480 #> 481 30 48.0 0 NA 0 23.0 6 2 0 481 #> 482 30 72.0 0 NA 0 4.1 5 1 0 482 #> 483 30 72.0 0 NA 0 26.0 6 2 0 483 #> 484 30 96.0 0 NA 0 2.9 5 1 0 484 #> 485 30 96.0 0 NA 0 28.0 6 2 0 485 #> 486 30 120.0 0 NA 0 2.3 5 1 0 486 #> 487 30 120.0 0 NA 0 39.0 6 2 0 487 #> 488 31 0.0 1 125.0 0 NA 1 0 0 488 #> 489 31 0.0 0 NA 0 100.0 6 2 0 489 #> 490 31 24.0 0 NA 0 9.5 5 1 0 490 #> 491 31 24.0 0 NA 0 45.0 6 2 0 491 #> 492 31 36.0 0 NA 0 7.8 5 1 0 492 #> 493 31 36.0 0 NA 0 30.0 6 2 0 493 #> 494 31 48.0 0 NA 0 6.4 5 1 0 494 #> 495 31 48.0 0 NA 0 24.0 6 2 0 495 #> 496 31 72.0 0 NA 0 4.5 5 1 0 496 #> 497 31 72.0 0 NA 0 22.0 6 2 0 497 #> 498 31 96.0 0 NA 0 3.4 5 1 0 498 #> 499 31 96.0 0 NA 0 28.0 6 2 0 499 #> 500 31 120.0 0 NA 0 2.5 5 1 0 500 #> 501 31 120.0 0 NA 0 42.0 6 2 0 501 #> 502 32 0.0 1 93.0 0 NA 1 0 0 502 #> 503 32 0.0 0 NA 0 100.0 6 2 0 503 #> 504 32 24.0 0 NA 0 8.9 5 1 0 504 #> 505 32 24.0 0 NA 0 36.0 6 2 0 505 #> 506 32 36.0 0 NA 0 7.7 5 1 0 506 #> 507 32 36.0 0 NA 0 27.0 6 2 0 507 #> 508 32 48.0 0 NA 0 6.9 5 1 0 508 #> 509 32 48.0 0 NA 0 24.0 6 2 0 509 #> 510 32 72.0 0 NA 0 4.4 5 1 0 510 #> 511 32 72.0 0 NA 0 23.0 6 2 0 511 #> 512 32 96.0 0 NA 0 3.5 5 1 0 512 #> 513 32 96.0 0 NA 0 20.0 6 2 0 513 #> 514 32 120.0 0 NA 0 2.5 5 1 0 514 #> 515 32 120.0 0 NA 0 22.0 6 2 0 515"},{"path":"/reference/dot-popedCluster.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","title":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","text":"Internal function use PopED run PopED parallel Windows","code":""},{"path":"/reference/dot-popedCluster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","text":"","code":".popedCluster(babelmixr2)"},{"path":"/reference/dot-popedCluster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","text":"babelmixr2 environment poped environment","code":""},{"path":"/reference/dot-popedCluster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","text":"nothing, called side effects","code":""},{"path":"/reference/dot-popedCluster.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Internal function to use with PopED to run PopED in parallel on Windows — .popedCluster","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedF.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the function value from the rxode2 solve — .popedF","title":"Get the function value from the rxode2 solve — .popedF","text":"called directly","code":""},{"path":"/reference/dot-popedF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the function value from the rxode2 solve — .popedF","text":"","code":".popedF(popedDb)"},{"path":"/reference/dot-popedF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the function value from the rxode2 solve — .popedF","text":"popedDb poped DB babelmixr2 issue","code":""},{"path":"/reference/dot-popedF.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the function value from the rxode2 solve — .popedF","text":"rxode2 weights poped error function","code":""},{"path":"/reference/dot-popedF.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the function value from the rxode2 solve — .popedF","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedFree.html","id":null,"dir":"Reference","previous_headings":"","what":"Free Poped memory (if any is allocated) — .popedFree","title":"Free Poped memory (if any is allocated) — .popedFree","text":"called directly used babelmixr2's poped interface","code":""},{"path":"/reference/dot-popedFree.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Free Poped memory (if any is allocated) — .popedFree","text":"","code":".popedFree()"},{"path":"/reference/dot-popedFree.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Free Poped memory (if any is allocated) — .popedFree","text":"nothing, called side effects","code":""},{"path":"/reference/dot-popedFree.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Free Poped memory (if any is allocated) — .popedFree","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedRxRunSetup.html","id":null,"dir":"Reference","previous_headings":"","what":"Setup poped if needed — .popedRxRunSetup","title":"Setup poped if needed — .popedRxRunSetup","text":"called user","code":""},{"path":"/reference/dot-popedRxRunSetup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Setup poped if needed — .popedRxRunSetup","text":"","code":".popedRxRunSetup(popedDb)"},{"path":"/reference/dot-popedRxRunSetup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Setup poped if needed — .popedRxRunSetup","text":"nothing, called side effects","code":""},{"path":"/reference/dot-popedRxRunSetup.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Setup poped if needed — .popedRxRunSetup","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedSetup.html","id":null,"dir":"Reference","previous_headings":"","what":"Setup the PopED environment — .popedSetup","title":"Setup the PopED environment — .popedSetup","text":"typically called directly","code":""},{"path":"/reference/dot-popedSetup.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Setup the PopED environment — .popedSetup","text":"","code":".popedSetup(e, eglobal, full = FALSE)"},{"path":"/reference/dot-popedSetup.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Setup the PopED environment — .popedSetup","text":"e environment setup information popEd eglobal global environment poped info full setup full model","code":""},{"path":"/reference/dot-popedSetup.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Setup the PopED environment — .popedSetup","text":"nothing, called side effects","code":""},{"path":"/reference/dot-popedSetup.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Setup the PopED environment — .popedSetup","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedSolveIdME.html","id":null,"dir":"Reference","previous_headings":"","what":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","title":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","text":"really called directly (setup correctly can crash R)","code":""},{"path":"/reference/dot-popedSolveIdME.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","text":"","code":".popedSolveIdME(theta, umt, mt, ms, nend, id, totn) .popedSolveIdME2(theta, umt, mt, ms, nend, id, totn)"},{"path":"/reference/dot-popedSolveIdME.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","text":"theta parameters (includes covariates modeling times) umt unique times sampled mt original unsorted time (match f/w ) ms model switch parameter integer starting 1 (related dvid rxode2) nend specifies number endpoints model id design identifier totn total number design points tested","code":""},{"path":"/reference/dot-popedSolveIdME.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","text":"data frame $f $w corresponding function value standard deviation sampling point","code":""},{"path":"/reference/dot-popedSolveIdME.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Solve poped problem for appropriate times with single/multiple endpoint models — .popedSolveIdME","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-popedW.html","id":null,"dir":"Reference","previous_headings":"","what":"Get the weight from the rxode2 solve — .popedW","title":"Get the weight from the rxode2 solve — .popedW","text":"called directly","code":""},{"path":"/reference/dot-popedW.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get the weight from the rxode2 solve — .popedW","text":"","code":".popedW(popedDb)"},{"path":"/reference/dot-popedW.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get the weight from the rxode2 solve — .popedW","text":"popedDb poped DB babelmixr2 issue","code":""},{"path":"/reference/dot-popedW.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get the weight from the rxode2 solve — .popedW","text":"rxode2 weights poped error function","code":""},{"path":"/reference/dot-popedW.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Get the weight from the rxode2 solve — .popedW","text":"Matthew L. Fidler","code":""},{"path":"/reference/dot-setupPopEDdatabase.html","id":null,"dir":"Reference","previous_headings":"","what":"Setup the poped database — .setupPopEDdatabase","title":"Setup the poped database — .setupPopEDdatabase","text":"Setup poped database","code":""},{"path":"/reference/dot-setupPopEDdatabase.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Setup the poped database — .setupPopEDdatabase","text":"","code":".setupPopEDdatabase(ui, data, control)"},{"path":"/reference/dot-setupPopEDdatabase.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Setup the poped database — .setupPopEDdatabase","text":"ui rxode2 ui function data babelmixr2 design data control PopED control","code":""},{"path":"/reference/dot-setupPopEDdatabase.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Setup the poped database — .setupPopEDdatabase","text":"PopED database","code":""},{"path":"/reference/dot-setupPopEDdatabase.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Setup the poped database — .setupPopEDdatabase","text":"Matthew L. Fidler","code":""},{"path":"/reference/getStandardColNames.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine standardized rxode2 column names from data — getStandardColNames","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"Determine standardized rxode2 column names data","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data)"},{"path":"/reference/getStandardColNames.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"data data.frame source column names","code":""},{"path":"/reference/getStandardColNames.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"named character vector names standardized names values either name column data NA column present data.","code":""},{"path":"/reference/getStandardColNames.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine standardized rxode2 column names from data — getStandardColNames","text":"","code":"getStandardColNames(data.frame(ID=1, DV=2, Time=3, CmT=4)) #> id time amt rate dur evid cmt ss ii addl dv #> \"ID\" \"Time\" NA NA NA NA \"CmT\" NA NA NA \"DV\" #> mdv dvid cens limit #> NA NA NA NA"},{"path":"/reference/modelUnitConversion.html","id":null,"dir":"Reference","previous_headings":"","what":"Unit conversion for pharmacokinetic models — modelUnitConversion","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"Unit conversion pharmacokinetic models","code":""},{"path":"/reference/modelUnitConversion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion( dvu = NA_character_, amtu = NA_character_, timeu = NA_character_, volumeu = NA_character_ )"},{"path":"/reference/modelUnitConversion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"dvu, amtu, timeu units DV, AMT, TIME columns data volumeu units volume parameters model","code":""},{"path":"/reference/modelUnitConversion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"list names units associated parameter (\"amtu\", \"clearanceu\", \"volumeu\", \"timeu\", \"dvu\") numeric value multiply modeled estimate (example, cp) model consistent data units.","code":""},{"path":[]},{"path":"/reference/modelUnitConversion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Unit conversion for pharmacokinetic models — modelUnitConversion","text":"","code":"modelUnitConversion(dvu = \"ng/mL\", amtu = \"mg\", timeu = \"hr\", volumeu = \"L\") #> Loading required namespace: testthat #> $amtu #> [1] \"mg\" #> #> $clearanceu #> [1] \"L/h\" #> #> $volumeu #> [1] \"L\" #> #> $timeu #> [1] \"hr\" #> #> $dvu #> [1] \"ng/mL\" #> #> $cmtu #> [1] \"mg/L\" #> #> $dvConversion #> [1] 1000 #>"},{"path":"/reference/monolixControl.html","id":null,"dir":"Reference","previous_headings":"","what":"Monolix Controller for nlmixr2 — monolixControl","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Monolix Controller nlmixr2","code":""},{"path":"/reference/monolixControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Monolix Controller for nlmixr2 — monolixControl","text":"","code":"monolixControl( nbSSDoses = 7, useLinearization = FALSE, stiff = FALSE, addProp = c(\"combined2\", \"combined1\"), exploratoryAutoStop = FALSE, smoothingAutoStop = FALSE, burnInIterations = 5, smoothingIterations = 200, exploratoryIterations = 250, simulatedAnnealingIterations = 250, exploratoryInterval = 200, exploratoryAlpha = 0, omegaTau = 0.95, errorModelTau = 0.95, variability = c(\"none\", \"firstStage\", \"decreasing\"), runCommand = getOption(\"babelmixr2.monolix\", \"\"), rxControl = NULL, sumProd = FALSE, optExpression = TRUE, calcTables = TRUE, compress = TRUE, ci = 0.95, sigdigTable = NULL, absolutePath = FALSE, modelName = NULL, muRefCovAlg = TRUE, run = TRUE, ... )"},{"path":"/reference/monolixControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Monolix Controller for nlmixr2 — monolixControl","text":"nbSSDoses Number steady state doses (default 7) useLinearization Use linearization log likelihood fim. stiff boolean using stiff ODE solver addProp specifies type additive plus proportional errors, one standard deviations add (combined1) type variances add (combined2). combined1 error type can described following equation: $$y = f + (+ b\\times f^c) \\times \\varepsilon$$ combined2 error model can described following equation: $$y = f + \\sqrt{^2 + b^2\\times f^{2\\times c}} \\times \\varepsilon$$ : - y represents observed value - f represents predicted value - additive standard deviation - b proportional/power standard deviation - c power exponent (proportional case c=1) exploratoryAutoStop logical turn exploratory phase auto-stop SAEM (default 250) smoothingAutoStop Boolean indicating smoothing automatically stop (default FALSE) burnInIterations Number burn iterations smoothingIterations Number smoothing iterations exploratoryIterations Number iterations exploratory phase (default 250) simulatedAnnealingIterations Number simulating annealing iterations exploratoryInterval Minimum number iterations exploratory phase (default 200) exploratoryAlpha Convergence memory exploratory phase (used exploratoryAutoStop TRUE) omegaTau Proportional rate variance simulated annealing errorModelTau Proportional rate error model simulated annealing variability describes methodology parameters without variability. : - Fixed throughout (none) - Variability first stage (firstStage) - Decreasing reaches fixed value (decreasing) runCommand shell command function run monolix; can specify default options(\"babelmixr2.monolix\"=\"runMonolix\"). empty 'lixoftConnectors' available, use lixoftConnectors run monolix. See details function usage. rxControl `rxode2` ODE solving options fitting, created `rxControl()` sumProd boolean indicating model change multiplication high precision multiplication sums high precision sums using PreciseSums package. default FALSE. optExpression Optimize rxode2 expression speed calculation. default turned . calcTables boolean determine foceiFit calculate tables. default TRUE compress object compressed items ci Confidence level tables. default 0.95 95% confidence. sigdigTable Significant digits final output table. specified, matches significant digits `sigdig` optimization algorithm. `sigdig` NULL, use 3. absolutePath Boolean indicating absolute path used monolix runs modelName Model name used generate NONMEM output. NULL try infer model name (x clear). Otherwise use character outputs. muRefCovAlg controls algebraic expressions can mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#` algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model. Therefore, default `TRUE`. run monolix run results imported nlmixr2? (Default TRUE) ... Ignored parameters","code":""},{"path":"/reference/monolixControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Monolix Controller for nlmixr2 — monolixControl","text":"monolix control object","code":""},{"path":"/reference/monolixControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Monolix Controller for nlmixr2 — monolixControl","text":"runCommand given string, called system() command like: runCommand mlxtran. example, runCommand=\"'/path//monolix/mlxbsub2021' -p \" command line used look like following: '/path//monolix/mlxbsub2021' monolix.mlxtran runCommand given function, called FUN(mlxtran, directory, ui) run Monolix. allows run Monolix way may need, long can write R. babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting Monolix. Note can get translated monolix components parsed/compiled rxode2 ui object ui$monolixModel ui$mlxtran","code":""},{"path":"/reference/monolixControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Monolix Controller for nlmixr2 — monolixControl","text":"Matthew Fidler","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Estimate starting parameters using PKNCA","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"","code":"# S3 method for class 'pknca' nlmixr2Est(env, ...)"},{"path":"/reference/nlmixr2Est.pknca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"env Environment nlmixr2 estimation routines. needs : - rxode2 ui object `$ui` - data fit estimation routine `$data` - control estimation routine's control options `$ui` ... arguments provided `nlmixr2Est()` provided flexibility currently used inside nlmixr","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"model updated starting parameters. model new element named \"nca\" available includes PKNCA results used calculation.","code":""},{"path":"/reference/nlmixr2Est.pknca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Estimate starting parameters using PKNCA — nlmixr2Est.pknca","text":"Parameters estimated follows: ka 4 half-lives Tmax higher 3: log(2)/(tmax/4) vc Inverse dose-normalized Cmax cl Estimated median clearance vp,vp22- 4-fold vc, respectively default, controlled vpMult vp2Mult arguments pkncaControl q,q2 0.5- 0.25-fold cl, respectively default, controlled qMult q2Mult arguments pkncaControl bounds parameter estimates set 10% first percentile 10 times 99th percentile. (ka, lower bound set lower 10% first percentile 0.03 upper bound modified 10 times 99th percentile.) Parameter estimation methods may changed future version.","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"","code":"nmGetDistributionMonolixLines(line) # S3 method for class 'rxUi' nmGetDistributionMonolixLines(line) # S3 method for class 'norm' nmGetDistributionMonolixLines(line)"},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Lines estimation monolix","code":""},{"path":"/reference/nmGetDistributionMonolixLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionMonolixLines","text":"Matthew Fidler","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":null,"dir":"Reference","previous_headings":"","what":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"S3 method getting distribution lines base rxode2 saem problem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"","code":"nmGetDistributionNonmemLines(line) # S3 method for class 'rxUi' nmGetDistributionNonmemLines(line) # S3 method for class 'norm' nmGetDistributionNonmemLines(line)"},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"line Parsed rxode2 model environment","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Lines estimation nonmem","code":""},{"path":"/reference/nmGetDistributionNonmemLines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This is a S3 method for getting the distribution lines for a base rxode2 saem problem — nmGetDistributionNonmemLines","text":"Matthew Fidler","code":""},{"path":"/reference/nonmemControl.html","id":null,"dir":"Reference","previous_headings":"","what":"NONMEM estimation control — nonmemControl","title":"NONMEM estimation control — nonmemControl","text":"NONMEM estimation control","code":""},{"path":"/reference/nonmemControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl( est = c(\"focei\", \"imp\", \"its\", \"posthoc\"), advanOde = c(\"advan13\", \"advan8\", \"advan6\"), cov = c(\"r,s\", \"r\", \"s\", \"\"), maxeval = 1e+05, tol = 6, atol = 12, sstol = 6, ssatol = 12, sigl = 12, sigdig = 3, print = 1, extension = getOption(\"babelmixr2.nmModelExtension\", \".nmctl\"), outputExtension = getOption(\"babelmixr2.nmOutputExtension\", \".lst\"), runCommand = getOption(\"babelmixr2.nonmem\", \"\"), iniSigDig = 5, protectZeros = FALSE, muRef = TRUE, addProp = c(\"combined2\", \"combined1\"), rxControl = NULL, sumProd = FALSE, optExpression = TRUE, calcTables = TRUE, compress = TRUE, ci = 0.95, sigdigTable = NULL, readRounding = FALSE, readBadOpt = FALSE, niter = 100L, isample = 1000L, iaccept = 0.4, iscaleMin = 0.1, iscaleMax = 10, df = 4, seed = 14456, mapiter = 1, mapinter = 0, noabort = TRUE, modelName = NULL, muRefCovAlg = TRUE, run = TRUE, ... )"},{"path":"/reference/nonmemControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"NONMEM estimation control — nonmemControl","text":"est NONMEM estimation method advanOde ODE solving method NONMEM cov NONMEM covariance method maxeval NONMEM's maxeval (non posthoc methods) tol NONMEM tolerance ODE solving advan atol NONMEM absolute tolerance ODE solving sstol NONMEM tolerance steady state ODE solving ssatol NONMEM absolute tolerance steady state ODE solving sigl NONMEM sigl estimation option sigdig significant digits NONMEM print print number NONMEM extension NONMEM file extensions outputExtension Extension use NONMEM output listing runCommand Command run NONMEM (typically path \"nmfe75\") function. See details information. iniSigDig many significant digits printed $THETA $OMEGA estimate zero. Also controls zero protection numbers protectZeros Add methods protect divide zero muRef Automatically mu-reference control stream addProp, sumProd, optExpression, calcTables, compress, ci, sigdigTable Passed nlmixr2est::foceiControl rxControl Options pass rxode2::rxControl simulations readRounding Try read NONMEM output NONMEM terminated due rounding errors readBadOpt Try read NONMEM output NONMEM terminated due apparent failed optimization niter number iterations NONMEM estimation methods isample Isample argument NONMEM estimation method iaccept Iaccept NONMEM estimation methods iscaleMin parameter IMP NONMEM method (ISCALE_MIN) iscaleMax parameter IMP NONMEM method (ISCALE_MAX) df degrees freedom IMP method seed seed NONMEM methods mapiter number map iterations IMP method mapinter MAPINTER parameter IMP method noabort Add NOABORT option $EST modelName Model name used generate NONMEM output. NULL try infer model name (x clear). Otherwise use character outputs. muRefCovAlg controls algebraic expressions can mu-referenced treated mu-referenced covariates : 1. Creating internal data-variable `nlmixrMuDerCov#` algebraic mu-referenced expression 2. Change algebraic expression `nlmixrMuDerCov# * mu_cov_theta` 3. Use internal mu-referenced covariate saem 4. optimization completed, replace `model()` old `model()` expression 5. Remove `nlmixrMuDerCov#` nlmix2 output general, covariates accurate since changes system linear compartment model. Therefore, default `TRUE`. run NONMEM run (files imported nlmixr2); default TRUE, FALSE simply create NONMEM control stream data file. ... optional genRxControl argument controlling automatic rxControl generation.","code":""},{"path":"/reference/nonmemControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"NONMEM estimation control — nonmemControl","text":"babelmixr2 control option generating NONMEM control stream reading back babelmixr2/nlmixr2","code":""},{"path":"/reference/nonmemControl.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"NONMEM estimation control — nonmemControl","text":"runCommand given string, called system() command like: runCommand controlFile outputFile. example, runCommand=\"'/path//nmfe75'\" command line used look like following: '/path//nmfe75' one.cmt.nmctl one.cmt.lst runCommand given function, called FUN(ctl, directory, ui) run NONMEM. allows run NONMEM way may need, long can write R. babelmixr2 wait function return proceeding. runCommand NA, nlmixr() stop writing model files without starting NONMEM.","code":""},{"path":"/reference/nonmemControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"NONMEM estimation control — nonmemControl","text":"Matthew L. Fidler","code":""},{"path":"/reference/nonmemControl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"NONMEM estimation control — nonmemControl","text":"","code":"nonmemControl() #> $est #> [1] \"focei\" #> #> $cov #> [1] \"r,s\" #> #> $advanOde #> [1] \"advan13\" #> #> $maxeval #> [1] 1e+05 #> #> $print #> [1] 1 #> #> $noabort #> [1] TRUE #> #> $iniSigDig #> [1] 5 #> #> $tol #> [1] 6 #> #> $atol #> [1] 12 #> #> $sstol #> [1] 6 #> #> $ssatol #> [1] 12 #> #> $sigl #> [1] 12 #> #> $muRef #> [1] TRUE #> #> $sigdig #> [1] 3 #> #> $protectZeros #> [1] FALSE #> #> $runCommand #> [1] \"\" #> #> $outputExtension #> [1] \".lst\" #> #> $addProp #> [1] \"combined2\" #> #> $rxControl #> $scale #> NULL #> #> $method #> liblsoda #> 2 #> #> $atol #> [1] 1e-12 #> #> $rtol #> [1] 1e-06 #> #> $maxsteps #> [1] 70000 #> #> $hmin #> [1] 0 #> #> $hmax #> [1] NA #> #> $hini #> [1] 0 #> #> $maxordn #> [1] 12 #> #> $maxords #> [1] 5 #> #> $covsInterpolation #> nocb #> 2 #> #> $addCov #> [1] TRUE #> #> $returnType #> rxSolve #> 0 #> #> $sigma #> NULL #> #> $sigmaDf #> NULL #> #> $nCoresRV #> [1] 1 #> #> $sigmaIsChol #> [1] FALSE #> #> $sigmaSeparation #> [1] \"auto\" #> #> $sigmaXform #> identity #> 4 #> #> $nDisplayProgress #> [1] 10000 #> #> $amountUnits #> [1] NA #> #> $timeUnits #> [1] \"hours\" #> #> $addDosing #> [1] FALSE #> #> $stateTrim #> [1] Inf #> #> $updateObject #> [1] FALSE #> #> $omega #> NULL #> #> $omegaDf #> NULL #> #> $omegaIsChol #> [1] FALSE #> #> $omegaSeparation #> [1] \"auto\" #> #> $omegaXform #> variance #> 6 #> #> $nSub #> [1] 1 #> #> $thetaMat #> NULL #> #> $thetaDf #> NULL #> #> $thetaIsChol #> [1] FALSE #> #> $nStud #> [1] 1 #> #> $dfSub #> [1] 0 #> #> $dfObs #> [1] 0 #> #> $seed #> NULL #> #> $nsim #> NULL #> #> $minSS #> [1] 10 #> #> $maxSS #> [1] 1000 #> #> $strictSS #> [1] 1 #> #> $infSSstep #> [1] 12 #> #> $istateReset #> [1] TRUE #> #> $subsetNonmem #> [1] TRUE #> #> $hmaxSd #> [1] 0 #> #> $maxAtolRtolFactor #> [1] 0.1 #> #> $from #> NULL #> #> $to #> NULL #> #> $by #> NULL #> #> $length.out #> NULL #> #> $iCov #> NULL #> #> $keep #> NULL #> #> $keepF #> character(0) #> #> $drop #> NULL #> #> $warnDrop #> [1] TRUE #> #> $omegaLower #> [1] -Inf #> #> $omegaUpper #> [1] Inf #> #> $sigmaLower #> [1] -Inf #> #> $sigmaUpper #> [1] Inf #> #> $thetaLower #> [1] -Inf #> #> $thetaUpper #> [1] Inf #> #> $indLinPhiM #> [1] 0 #> #> $indLinPhiTol #> [1] 1e-07 #> #> $indLinMatExpType #> expokit #> 2 #> #> $indLinMatExpOrder #> [1] 6 #> #> $idFactor #> [1] TRUE #> #> $mxhnil #> [1] 0 #> #> $hmxi #> [1] 0 #> #> $warnIdSort #> [1] TRUE #> #> $ssAtol #> [1] 1e-12 #> #> $ssRtol #> [1] 1e-06 #> #> $safeZero #> [1] 0 #> #> $sumType #> pairwise #> 1 #> #> $prodType #> long double #> 1 #> #> $sensType #> advan #> 4 #> #> $linDiff #> tlag f rate dur tlag2 f2 rate2 dur2 #> 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 1.5e-05 #> #> $linDiffCentral #> tlag f rate dur tlag2 f2 rate2 dur2 #> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> #> $resample #> NULL #> #> $resampleID #> [1] TRUE #> #> $maxwhile #> [1] 100000 #> #> $cores #> [1] 0 #> #> $atolSens #> [1] 1e-08 #> #> $rtolSens #> [1] 1e-06 #> #> $ssAtolSens #> [1] 1e-08 #> #> $ssRtolSens #> [1] 1e-06 #> #> $simVariability #> [1] NA #> #> $nLlikAlloc #> NULL #> #> $useStdPow #> [1] 0 #> #> $naTimeHandle #> ignore #> 1 #> #> $addlKeepsCov #> [1] FALSE #> #> $addlDropSs #> [1] TRUE #> #> $ssAtDoseTime #> [1] TRUE #> #> $ss2cancelAllPending #> [1] FALSE #> #> $naInterpolation #> locf #> 1 #> #> $keepInterpolation #> na #> 2 #> #> $safeLog #> [1] 1 #> #> $safePow #> [1] 1 #> #> $.zeros #> NULL #> #> attr(,\"class\") #> [1] \"rxControl\" #> #> $sumProd #> [1] FALSE #> #> $optExpression #> [1] TRUE #> #> $calcTables #> [1] TRUE #> #> $compress #> [1] TRUE #> #> $ci #> [1] 0.95 #> #> $sigdigTable #> NULL #> #> $readRounding #> [1] FALSE #> #> $readBadOpt #> [1] FALSE #> #> $genRxControl #> [1] TRUE #> #> $niter #> [1] 100 #> #> $isample #> [1] 1000 #> #> $iaccept #> [1] 0.4 #> #> $iscaleMin #> [1] 0.1 #> #> $iscaleMax #> [1] 10 #> #> $df #> [1] 4 #> #> $seed #> [1] 14456 #> #> $mapiter #> [1] 1 #> #> $modelName #> NULL #> #> $muRefCovAlg #> [1] TRUE #> #> $run #> [1] TRUE #> #> attr(,\"class\") #> [1] \"nonmemControl\""},{"path":"/reference/pkncaControl.html","id":null,"dir":"Reference","previous_headings":"","what":"PKNCA estimation control — pkncaControl","title":"PKNCA estimation control — pkncaControl","text":"PKNCA estimation control","code":""},{"path":"/reference/pkncaControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PKNCA estimation control — pkncaControl","text":"","code":"pkncaControl( concu = NA_character_, doseu = NA_character_, timeu = NA_character_, volumeu = NA_character_, vpMult = 2, qMult = 1/2, vp2Mult = 4, q2Mult = 1/4, dvParam = \"cp\", groups = character(), sparse = FALSE, ncaData = NULL, ncaResults = NULL, rxControl = rxode2::rxControl() )"},{"path":"/reference/pkncaControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PKNCA estimation control — pkncaControl","text":"concu, doseu, timeu concentration, dose, time units source data (passed PKNCA::pknca_units_table()). volumeu compartment volume model (NULL, simplified units source data used) vpMult, qMult, vp2Mult, q2Mult Multipliers vc cl provide initial estimates vp, q, vp2, q2 dvParam parameter name model modified concentration unit conversions. must assigned line , separate residual error model line. groups Grouping columns NCA summaries group (required sparse = TRUE) sparse concentration-time data sparse PK (commonly used small nonclinical species terminal difficult sampling) dense PK (commonly used clinical studies larger nonclinical species)? ncaData Data use calculating NCA parameters. Typical use subset original data informative NCA. ncaResults Already computed NCA results (PKNCAresults object) bypass automatic calculations. least following parameters must calculated NCA: tmax, cmax.dn, cl.last rxControl Control options sent rxode2::rxControl()","code":""},{"path":"/reference/pkncaControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"PKNCA estimation control — pkncaControl","text":"list parameters","code":""},{"path":"/reference/popedControl.html","id":null,"dir":"Reference","previous_headings":"","what":"Control for a PopED design task — popedControl","title":"Control for a PopED design task — popedControl","text":"Control PopED design task","code":""},{"path":"/reference/popedControl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Control for a PopED design task — popedControl","text":"","code":"popedControl( stickyRecalcN = 4, maxOdeRecalc = 5, odeRecalcFactor = 10^(0.5), maxn = NULL, rxControl = NULL, sigdig = 4, important = NULL, unimportant = NULL, iFIMCalculationType = c(\"reduced\", \"full\", \"weighted\", \"loc\", \"reducedPFIM\", \"fullABC\", \"largeMat\", \"reducedFIMABC\"), iApproximationMethod = c(\"fo\", \"foce\", \"focei\", \"foi\"), iFOCENumInd = 1000, prior_fim = matrix(0, 0, 1), d_switch = c(\"d\", \"ed\"), ofv_calc_type = c(\"lnD\", \"d\", \"a\", \"Ds\", \"inverse\"), strEDPenaltyFile = \"\", ofv_fun = NULL, iEDCalculationType = c(\"mc\", \"laplace\", \"bfgs-laplace\"), ED_samp_size = 45, bLHS = c(\"hypercube\", \"random\"), bUseRandomSearch = TRUE, bUseStochasticGradient = TRUE, bUseLineSearch = TRUE, bUseExchangeAlgorithm = FALSE, bUseBFGSMinimizer = FALSE, bUseGrouped_xt = FALSE, EACriteria = c(\"modified\", \"fedorov\"), strRunFile = \"\", poped_version = NULL, modtit = \"PopED babelmixr2 model\", output_file = \"PopED_output_summary\", output_function_file = \"PopED_output_\", strIterationFileName = \"PopED_current.R\", user_data = NULL, ourzero = 1e-05, dSeed = NULL, line_opta = NULL, line_optx = NULL, bShowGraphs = FALSE, use_logfile = FALSE, m1_switch = c(\"central\", \"complex\", \"analytic\", \"ad\"), m2_switch = c(\"central\", \"complex\", \"analytic\", \"ad\"), hle_switch = c(\"central\", \"complex\", \"ad\"), gradff_switch = c(\"central\", \"complex\", \"analytic\", \"ad\"), gradfg_switch = c(\"central\", \"complex\", \"analytic\", \"ad\"), grad_all_switch = c(\"central\", \"complex\"), rsit_output = 5, sgit_output = 1, hm1 = 1e-05, hlf = 1e-05, hlg = 1e-05, hm2 = 1e-05, hgd = 1e-05, hle = 1e-05, AbsTol = 1e-06, RelTol = 1e-06, iDiffSolverMethod = NULL, bUseMemorySolver = FALSE, rsit = 300, sgit = 150, intrsit = 250, intsgit = 50, maxrsnullit = 50, convergence_eps = 1e-08, rslxt = 10, rsla = 10, cfaxt = 0.001, cfaa = 0.001, bGreedyGroupOpt = FALSE, EAStepSize = 0.01, EANumPoints = FALSE, EAConvergenceCriteria = 1e-20, bEANoReplicates = FALSE, BFGSProjectedGradientTol = 1e-04, BFGSTolerancef = 0.001, BFGSToleranceg = 0.9, BFGSTolerancex = 0.1, ED_diff_it = 30, ED_diff_percent = 10, line_search_it = 50, Doptim_iter = 1, iCompileOption = c(\"none\", \"full\", \"mcc\", \"mpi\"), compileOnly = FALSE, iUseParallelMethod = c(\"mpi\", \"matlab\"), MCC_Dep = NULL, strExecuteName = \"calc_fim.exe\", iNumProcesses = 2, iNumChunkDesignEvals = -2, Mat_Out_Pre = \"parallel_output\", strExtraRunOptions = \"\", dPollResultTime = 0.1, strFunctionInputName = \"function_input\", bParallelRS = FALSE, bParallelSG = FALSE, bParallelMFEA = FALSE, bParallelLS = FALSE, groupsize = NULL, time = \"time\", timeLow = \"low\", timeHi = \"high\", id = \"id\", m = NULL, x = NULL, ni = NULL, maxni = NULL, minni = NULL, maxtotni = NULL, mintotni = NULL, maxgroupsize = NULL, mingroupsize = NULL, maxtotgroupsize = NULL, mintotgroupsize = NULL, xt_space = NULL, a = NULL, maxa = NULL, mina = NULL, a_space = NULL, x_space = NULL, use_grouped_xt = FALSE, grouped_xt = NULL, use_grouped_a = FALSE, grouped_a = NULL, use_grouped_x = FALSE, grouped_x = NULL, our_zero = NULL, auto_pointer = \"\", user_distribution_pointer = \"\", minxt = NULL, maxxt = NULL, discrete_xt = NULL, discrete_a = NULL, fixRes = FALSE, script = NULL, overwrite = TRUE, literalFix = TRUE, opt_xt = FALSE, opt_a = FALSE, opt_x = FALSE, opt_samps = FALSE, optTime = TRUE, ... )"},{"path":"/reference/popedControl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Control for a PopED design task — popedControl","text":"stickyRecalcN number bad ODE solves reducing atol/rtol rest problem. maxOdeRecalc Maximum number times reduce ODE tolerances try resolve system bad ODE solve. odeRecalcFactor ODE recalculation factor ODE solving goes bad, factor rtol/atol reduced maxn Maximum number design points optimization; default declared maximum number design points babelmixr2 dataset (NULL) rxControl `rxode2` ODE solving options fitting, created `rxControl()` sigdig Optimization significant digits. controls: tolerance inner outer optimization 10^-sigdig tolerance ODE solvers 0.5*10^(-sigdig-2); sensitivity equations steady-state solutions default 0.5*10^(-sigdig-1.5) (sensitivity changes applicable liblsoda) tolerance boundary check 5 * 10 ^ (-sigdig + 1) important character vector important parameters NULL default. used Ds-optimality unimportant character vector unimportant parameters NULL default. used Ds-optimality iFIMCalculationType can either integer named value Fisher Information Matrix type: 0/\"full\" = Full FIM 1/\"reduced\" = Reduced FIM 2/\"weighted\" = weighted models 3/\"loc\" = Loc models 4/\"reducedPFIM\" = reduced FIM derivative SD sigma PFIM 5/\"fullABC\" = FULL FIM parameterized ,B,C matrices & derivative variance 6/\"largeMat\" = Calculate one model switch time, good large matrices 7/\"reducedFIMABC\" = =Reduced FIM parameterized ,B,C matrices & derivative variance iApproximationMethod Approximation method model, 0=FO, 1=FOCE, 2=FOCEI, 3=FOI iFOCENumInd integer; number individuals focei solve prior_fim matrix; prior FIM d_switch integer character option: 0/\"ed\" = ED design 1/\"d\" = D design ofv_calc_type objective calculation type: 1/\"d\" = D-optimality\". Determinant FIM: det(FIM) 2/\"\" = \"-optimality\". Inverse sum expected parameter variances: 1/trace_matrix(inv(FIM)) 4/\"lnD\" = \"lnD-optimality\". Natural logarithm determinant FIM: log(det(FIM)) 6/\"Ds\" = \"Ds-optimality\". Ratio Determinant FIM Determinant uninteresting rows columns FIM: det(FIM)/det(FIM_u) 7/\"inverse\" = Inverse sum expected parameter RSE: 1/sum(get_rse(FIM,poped.db,use_percent=FALSE)) strEDPenaltyFile Penalty function name path filename, empty string means penalty. User defined criterion can defined way. ofv_fun User defined function used compute objective function. function must poped database object first argument \"...\" argument list. Can referenced function file name function defined file name file. e.g. \"cost.txt\" function named \"cost\" . iEDCalculationType ED Integral Calculation type: 0/\"mc\" = Monte-Carlo-Integration 1/\"laplace\" = Laplace Approximation 2/\"bfgs-laplace\" = BFGS Laplace Approximation ED_samp_size Sample size E-family sampling bLHS sample distributions E-family calculations. 0=Random Sampling, 1=LatinHyperCube – bUseRandomSearch ******START Optimization algorithm SPECIFICATION OPTIONS********** Use random search (1=TRUE, 0=FALSE) bUseStochasticGradient Use Stochastic Gradient search (1=TRUE, 0=FALSE) bUseLineSearch Use Line search (1=TRUE, 0=FALSE) bUseExchangeAlgorithm Use Exchange algorithm (1=TRUE, 0=FALSE) bUseBFGSMinimizer Use BFGS Minimizer (1=TRUE, 0=FALSE) bUseGrouped_xt Use grouped time points (1=TRUE, 0=FALSE). EACriteria Exchange Algorithm Criteria: 1/\"modified\" = Modified 2/\"fedorov\" = Fedorov strRunFile Filename path, function name, run file used instead regular PopED call. poped_version ******START Labeling file names SPECIFICATION OPTIONS********** current PopED version modtit model title output_file Filename path output file search output_function_file Filename suffix result function file strIterationFileName Filename path storage current optimal design user_data ******START Miscellaneous SPECIFICATION OPTIONS********** User defined data structure , example used send data model ourzero Value interpret zero design dSeed seed number used optimization sampling – integer -1 creates random seed .integer(Sys.time()) NULL. line_opta Vector line search continuous design variables (1=TRUE,0=FALSE) line_optx Vector line search discrete design variables (1=TRUE,0=FALSE) bShowGraphs Use graph output search use_logfile log file used (0=FALSE, 1=TRUE) m1_switch Method used calculate M1: 1/\"central\" = Central difference 0/\"complex\" = Complex difference 20/\"analytic\" = Analytic derivative 30/\"ad\" = Automatic differentiation m2_switch Method used calculate M2: 1/\"central\" = Central difference 0/\"complex\" = Complex difference 20/\"analytic\" = Analytic derivative 30/\"ad\" = Automatic differentiation hle_switch Method used calculate linearization residual error: 1/\"central\" = Central difference 0/\"complex\" = Complex difference 30/\"ad\" = Automatic differentiation gradff_switch Method used calculate gradient model: 1/\"central\" = Central difference 0/\"complex\" = Complex difference 20/\"analytic\" = Analytic derivative 30/\"ad\" = Automatic differentiation gradfg_switch Method used calculate gradient parameter vector g: 1/\"central\" = Central difference 0/\"complex\" = Complex difference 20/\"analytic\" = Analytic derivative 30/\"ad\" = Automatic differentiation grad_all_switch Method used calculate gradients: 1/\"central\" = Central difference 0/\"complex\" = Complex difference rsit_output Number iterations random search screen output sgit_output Number iterations stochastic gradient search screen output hm1 Step length derivative linearized model w.r.t. typical values hlf Step length derivative model w.r.t. g hlg Step length derivative g w.r.t. b hm2 Step length derivative variance w.r.t. typical values hgd Step length derivative OFV w.r.t. time hle Step length derivative model w.r.t. sigma AbsTol absolute tolerance diff equation solver RelTol relative tolerance diff equation solver iDiffSolverMethod diff equation solver method, NULL default. bUseMemorySolver differential equation results stored memory (1) (0) rsit Number Random search iterations sgit Number stochastic gradient iterations intrsit Number Random search iterations discrete optimization. intsgit Number Stochastic Gradient search iterations discrete optimization maxrsnullit Iterations adaptive narrowing random search convergence_eps Stochastic Gradient convergence value, (difference OFV D-optimal, difference gradient ED-optimal) rslxt Random search locality factor sample times rsla Random search locality factor covariates cfaxt Stochastic Gradient search first step factor sample times cfaa Stochastic Gradient search first step factor covariates bGreedyGroupOpt Use greedy algorithm group assignment optimization EAStepSize Exchange Algorithm StepSize EANumPoints Exchange Algorithm NumPoints EAConvergenceCriteria Exchange Algorithm Convergence Limit/Criteria bEANoReplicates Avoid replicate samples using Exchange Algorithm BFGSProjectedGradientTol BFGS Minimizer Convergence Criteria Normalized Projected Gradient Tolerance BFGSTolerancef BFGS Minimizer Line Search Tolerance f BFGSToleranceg BFGS Minimizer Line Search Tolerance g BFGSTolerancex BFGS Minimizer Line Search Tolerance x ED_diff_it Number iterations ED-optimal design calculate convergence criteria ED_diff_percent ED-optimal design convergence criteria percent line_search_it Number grid points line search Doptim_iter Number iterations full Random search full Stochastic Gradient line search used iCompileOption Compile options PopED \"none\"/-1 = compilation \"full/0 3 = Full compilation \"mcc\"/1 4 = using MCC (shared lib) \"mpi\"/2 5 = MPI, using numbers, option 0,1,2 runs PopED option 3,4,5 stops compilation. using characters, option compileOnly determines model compiled (PopED run). compileOnly logical; compile model, run PopED (conjunction iCompileOption) iUseParallelMethod Parallel method use 0/\"matlab\"= Matlab PCT 1/\"mpi\" = MPI MCC_Dep Additional dependencies used MCC compilation (mat-files), several space separated strExecuteName Compilation output executable name iNumProcesses Number processes use running parallel (e.g. 3 = 2 workers, 1 job manager) iNumChunkDesignEvals Number design evaluations evaluated process getting new work job manager Mat_Out_Pre prefix output mat file communicate executable strExtraRunOptions Extra options send e$g. MPI executable batch script, see execute_parallel$m information options dPollResultTime Polling time check parallel execution finished strFunctionInputName file containing popedInput structure used evaluate designs bParallelRS random search going executed parallel bParallelSG stochastic gradient search going executed parallel bParallelMFEA modified exchange algorithm going executed parallel bParallelLS line search going executed parallel groupsize Vector defining size different groups (num individuals group). one number number every group. time string represents time dataset (ie xt) timeLow string represents lower design time (ie minxt) timeHi string represents upper design time (ie maxmt) id id variable m Number groups study. individual group design. x matrix defining initial discrete values model row group/individual. ni Vector defining number samples group. maxni ******START DESIGN SPACE OPTIONS********** Max number samples per group/individual minni Min number samples per group/individual maxtotni Number defining maximum number samples allowed experiment. mintotni Number defining minimum number samples allowed experiment. maxgroupsize Vector defining max size different groups (max number individuals group) mingroupsize Vector defining min size different groups (min num individuals group) – maxtotgroupsize total maximal groupsize groups mintotgroupsize total minimal groupsize groups xt_space Cell array cell defining discrete variables allowed xt value. Can also vector values c(1:10) (values allowed xt), list lists list(1:10, 2:23, 4:6) (one value xt row major order just one row xt, rows duplicated). Matrix defining initial continuous covariate values. n_rows=number groups, n_cols=number covariates. number rows one number groups > 1 groups assigned values. maxa Vector defining max value covariate. single value supplied values given max value mina Vector defining min value covariate. single value supplied values given max value a_space Cell array cell defining discrete variables allowed value. Can also list values list(1:10) (values allowed ), list lists list(1:10, 2:23, 4:6) (one value ). x_space Cell array cell defining discrete variables x value. use_grouped_xt Group sampling times groups group values (TRUE FALSE). grouped_xt Matrix defining grouping sample points. Matching integers mean points matched. Allows finer control use_grouped_xt use_grouped_a Group continuous design variables groups group values (TRUE FALSE). grouped_a Matrix defining grouping continuous design variables. Matching integers mean values matched. Allows finer control use_grouped_a. use_grouped_x Group discrete design variables groups group values (TRUE FALSE). grouped_x Matrix defining grouping discrete design variables. Matching integers mean values matched. Allows finer control use_grouped_x. our_zero Value interpret zero design. auto_pointer Filename path, function name, Autocorrelation function, empty string means autocorrelation user_distribution_pointer Filename path, function name, user defined distributions E-family designs minxt Matrix single value defining minimum value xt sample. single value supplied xt values given minimum value maxxt Matrix single value defining maximum value xt sample. single value supplied xt values given maximum value. discrete_xt Cell array cell defining discrete variables allowed xt value. Can also list values list(1:10) (values allowed xt), list lists list(1:10, 2:23, 4:6) (one value xt). See examples create_design_space. discrete_a Cell array cell defining discrete variables allowed value. Can also list values list(1:10) (values allowed ), list lists list(1:10, 2:23, 4:6) (one value ). See examples create_design_space. fixRes boolean; Fix residuals specified model script write PopED/rxode2 script can modified fine control. default NULL. script TRUE, script returned lines written file class babelmixr2popedScript. allows printed script screen. script file name (R extension), script written file. overwrite [logical(1)] TRUE, existing file place allowed readable writable. Default FALSE. literalFix boolean, substitute fixed population values literals re-adjust ui parameter estimates optimization; Default `TRUE`. opt_xt boolean indicate meant optimizing times opt_a boolean indicate meant optimizing covariates opt_x boolean indicate discrete design variables optimized opt_samps boolean indicate sample optimizer used (implemented yet PopED) optTime boolean indicate global time indexer inside babelmixr2 reset times different. default TRUE. FALSE can get slightly better run times possibly slightly different results. optTime FALSE global indexer reset every time PopED rxode2 setup problem poped dataset created. can manually reset popedMultipleEndpointResetTimeIndex() ... parameters PopED control","code":""},{"path":"/reference/popedControl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Control for a PopED design task — popedControl","text":"popedControl object","code":""},{"path":"/reference/popedControl.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Control for a PopED design task — popedControl","text":"Matthew L. Fidler","code":""},{"path":"/reference/popedGetMultipleEndpointModelingTimes.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","title":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","text":"function takes vector times corresponding vector IDs, groups times IDs, initializes internal C++ global TimeIndexer, used efficiently lookup final output rxode2 solve returns sorted unique times. popedMultipleEndpointIndexDataFrame() function can used visualize internal data structure inside R, show indexes case time ties given ID. Rather shows one indexs total number indexes data.frame","code":""},{"path":"/reference/popedGetMultipleEndpointModelingTimes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","text":"","code":"popedGetMultipleEndpointModelingTimes(times, modelSwitch, sorted = FALSE) popedMultipleEndpointIndexDataFrame(print = FALSE)"},{"path":"/reference/popedGetMultipleEndpointModelingTimes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","text":"times numeric vector times. modelSwitch integer vector model switch indicator corresponding times sorted boolean indicating returned times sorted print boolean popedMultipleEndpointIndexDataFrame() TRUE show id/index per time even though may reflect returned data.frame","code":""},{"path":"/reference/popedGetMultipleEndpointModelingTimes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","text":"numeric vector unique times.","code":""},{"path":"/reference/popedGetMultipleEndpointModelingTimes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Multiple Endpoint Modeling Times — popedGetMultipleEndpointModelingTimes","text":"","code":"# \\donttest{ times <- c(1.1, 1.2, 1.3, 2.1, 2.2, 3.1) modelSwitch <- c(1, 1, 1, 2, 2, 3) sortedTimes <- popedGetMultipleEndpointModelingTimes(times, modelSwitch, TRUE) print(sortedTimes) #> [1] 1.1 1.2 1.3 2.1 2.2 3.1 # now show the output of the data frame representing the model # switch to endpoint index popedMultipleEndpointIndexDataFrame() #> time MS:1 N:1 MS:3 N:3 MS:5 N:5 #> 1 1.1 1 1 NA NA NA NA #> 2 1.2 2 1 NA NA NA NA #> 3 1.3 3 1 NA NA NA NA #> 4 2.1 NA NA 4 1 NA NA #> 5 2.2 NA NA 5 1 NA NA #> 6 3.1 NA NA NA NA 6 1 # now show a more complex example with overlaps etc. times <- c(1.1, 1.2, 1.3, 0.5, 2.2, 1.1, 0.75,0.75) modelSwitch <- c(1, 1, 1, 2, 2, 2, 3, 3) sortedTimes <- popedGetMultipleEndpointModelingTimes(times, modelSwitch, TRUE) print(sortedTimes) #> [1] 0.50 0.75 1.10 1.20 1.30 2.20 popedMultipleEndpointIndexDataFrame(TRUE) # Print to show individual matching #> modelSwitch: 2 time: 0.500000: 4 #> modelSwitch: 3 time: 0.750000: 7, 8 #> modelSwitch: 2 time: 1.100000: 6 #> modelSwitch: 1 time: 1.100000: 1 #> modelSwitch: 1 time: 1.200000: 2 #> modelSwitch: 1 time: 1.300000: 3 #> modelSwitch: 2 time: 2.200000: 5 #> time MS:1 N:1 MS:3 N:3 MS:5 N:5 #> 1 0.50 NA NA 4 1 NA NA #> 2 0.75 NA NA NA NA 7 2 #> 3 1.10 1 1 6 1 NA NA #> 4 1.20 2 1 NA NA NA NA #> 5 1.30 3 1 NA NA NA NA #> 6 2.20 NA NA 5 1 NA NA # }"},{"path":"/reference/popedMultipleEndpointParam.html","id":null,"dir":"Reference","previous_headings":"","what":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"function populates numeric vector parameters unique times also populates internal C++ global index","code":""},{"path":"/reference/popedMultipleEndpointParam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"","code":"popedMultipleEndpointParam(p, times, modelSwitch, maxMT, optTime = TRUE)"},{"path":"/reference/popedMultipleEndpointParam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"p numeric vector parameters times numeric vector times modelSwitch integer vector indicating model switches PopED maxMT integer specifying maximum number time points mtimes model","code":""},{"path":"/reference/popedMultipleEndpointParam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"numeric vector containing parameters followed unique times, maximum number times greater input append maximum observed times input. assumes first parameter id dropped fro output.","code":""},{"path":"/reference/popedMultipleEndpointParam.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"function first uses input times model switches global time indexer. creates new numeric vector combines input parameters unique times. number times less maxMT, remaining elements filled maximum time.","code":""},{"path":"/reference/popedMultipleEndpointParam.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"Matthew L. Fidler","code":""},{"path":"/reference/popedMultipleEndpointParam.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Populates Multiple Endpoint Parameters for internal solving — popedMultipleEndpointParam","text":"","code":"# \\donttest{ p <- c(1.0, 2.0, 3.0) times <- c(0.5, 1.5, 2.5) modelSwitch <- c(1, 2, 3) maxMT <- 5 popedMultipleEndpointParam(p, times, modelSwitch, maxMT) #> [1] 2.0 3.0 0.5 1.5 2.5 2.5 2.5 # }"},{"path":"/reference/popedMultipleEndpointResetTimeIndex.html","id":null,"dir":"Reference","previous_headings":"","what":"Reset the Global Time Indexer for Multiple Endpoint Modeling — popedMultipleEndpointResetTimeIndex","title":"Reset the Global Time Indexer for Multiple Endpoint Modeling — popedMultipleEndpointResetTimeIndex","text":"clears memory resets global time indexer used multiple endpoint modeling.","code":""},{"path":"/reference/popedMultipleEndpointResetTimeIndex.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reset the Global Time Indexer for Multiple Endpoint Modeling — popedMultipleEndpointResetTimeIndex","text":"","code":"popedMultipleEndpointResetTimeIndex()"},{"path":"/reference/popedMultipleEndpointResetTimeIndex.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reset the Global Time Indexer for Multiple Endpoint Modeling — popedMultipleEndpointResetTimeIndex","text":"NULL, called side effects","code":""},{"path":"/reference/popedMultipleEndpointResetTimeIndex.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reset the Global Time Indexer for Multiple Endpoint Modeling — popedMultipleEndpointResetTimeIndex","text":"","code":"# \\donttest{ popedMultipleEndpointResetTimeIndex() #> NULL # }"},{"path":"/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. monolix2rx mlxtran, monolix2rx nlmixr2est getValidNlmixrCtl, nlmixr2Est, nmObjGetControl, nmObjGetFoceiControl, nmObjHandleControlObject nonmem2rx .nonmem2rx, nmcov, nmext, nminfo, nmtab, nmxml, nonmem2rx rxode2 .minfo, rxModelVars, rxUiGet","code":""},{"path":"/reference/rxToMonolix.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to monolix syntax — rxToMonolix","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Convert RxODE syntax monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"","code":"rxToMonolix(x, ui)"},{"path":"/reference/rxToMonolix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToMonolix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Monolix syntax","code":""},{"path":"/reference/rxToMonolix.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to monolix syntax — rxToMonolix","text":"Matthew Fidler","code":""},{"path":"/reference/rxToNonmem.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Convert RxODE syntax NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"","code":"rxToNonmem(x, ui)"},{"path":"/reference/rxToNonmem.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"x Expression ui rxode2 ui","code":""},{"path":"/reference/rxToNonmem.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"NONMEM syntax","code":""},{"path":"/reference/rxToNonmem.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Convert RxODE syntax to NONMEM syntax — rxToNonmem","text":"Matthew Fidler","code":""},{"path":"/reference/simplifyUnit.html","id":null,"dir":"Reference","previous_headings":"","what":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"Simplify units removing repeated units numerator denominator","code":""},{"path":"/reference/simplifyUnit.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(numerator = \"\", denominator = \"\")"},{"path":"/reference/simplifyUnit.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"numerator numerator units (whole unit specification) denominator denominator units (NULL numerator whole unit specification)","code":""},{"path":"/reference/simplifyUnit.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"units specified units numerator denominator cancelled.","code":""},{"path":"/reference/simplifyUnit.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"NA \"\" numerator denominator considered unitless.","code":""},{"path":[]},{"path":"/reference/simplifyUnit.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simplify units by removing repeated units from the numerator and denominator — simplifyUnit","text":"","code":"simplifyUnit(\"kg\", \"kg/mL\") #> [1] \"mL\" # units that don't match exactly are not cancelled simplifyUnit(\"kg\", \"g/mL\") #> [1] \"kg*mL/g\""},{"path":"/news/index.html","id":"babelmixr2-015","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.5","title":"babelmixr2 0.1.5","text":"Fix bug PopED error certain dvid values Fix bug /else clauses model cause model predict values correctly. Fix bug shrinkage() calculation works Fix bug can mix 2 different PopED data bases analysis without crashing R. didn’t occur every database clash, frequently occurred interleaved PopED code two different PopED databases, like issue #131. Added new function babelBpopIdx(poped.db, \"par\") get poped index model generated babelmixr2, useful calculating power (example 11).","code":""},{"path":"/news/index.html","id":"babelmixr2-014","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.4","title":"babelmixr2 0.1.4","text":"CRAN release: 2024-09-23 Added experimental PopED integration Removed dependence rxode2parse Imported monolix2rx monolix2rx package Also allow conversion model imported monolix nlmixr2 fit.","code":""},{"path":"/news/index.html","id":"babelmixr2-013","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.3","title":"babelmixr2 0.1.3","text":"Changed default NONMEM rounding protection FALSE Added run option monolixControl() nonemControl() case want export modeling files run models.","code":""},{"path":"/news/index.html","id":"babelmixr2-012","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.2","title":"babelmixr2 0.1.2","text":"CRAN release: 2023-12-12 Handle algebraic mu expressions PKNCA controller now contains rxControl since used translation options revision load pruned ui model query compartment properties (.e. bioavailability, lag time, etc) writing NONMEM model. fix issues PK block define variables larger calculated variable can used model instead. nonmem2rx different lst file, long nonmem2rx::nminfo(file) works, successful conversion nlmixr2 fit object occur. Fix save parameter history $parHistData accommodate changes focei’s output ($parHist now derived). Changed solving options match new steady state options rxode2 NONMEM implements . Also changed iwres model account rxerr. instead err. updated rxode2 well.","code":""},{"path":"/news/index.html","id":"babelmixr2-011","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.1","title":"babelmixr2 0.1.1","text":"CRAN release: 2023-05-27 Add new method .nlmixr2 convert nonmem2rx methods nlmixr fits Dropped pmxTools favor nonmem2rx conserve methods","code":""},{"path":"/news/index.html","id":"babelmixr2-010","dir":"Changelog","previous_headings":"","what":"babelmixr2 0.1.0","title":"babelmixr2 0.1.0","text":"CRAN release: 2022-10-28 Babelmixr support “monolix”, “nonmem”, “pknca” methods release. Added NEWS.md file track changes package.","code":""}]