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.Rapp.history
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names(cube)
now
colnames(cube)
unique(cube[,2])
now[2]
unique(cube[,2])==now[2]
now[2]
now[2]+1
unique(cube[,2])+1
unique(cube[,2])%in%now[2]
unique(cube[,2])[1]
unique(cube[,2])[1]-now[2]
which(cube[,1]==now[1]&cube[,2]==now[2]&cube[,3]==now[3]&cube[,4]==now[4]&cube[,5]=now[5])round(now[1],1)
round(now[1],1)
which(#
round(cube[,1],1)==round(now[1],1)&#
round(cube[,2],1)==round(now[2],1)&#
round(cube[,3],1)==round(now[3],1)&#
round(cube[,4],1)==round(now[4],1)&#
round(cube[,5],1)=round(now[5],1))
which(round(cube[,1],1)==round(now[1],1))
which(#
round(cube[,1],1)==round(now[1],1)&#
round(cube[,2],1)==round(now[2],1)&#
round(cube[,3],1)==round(now[3],1)&#
round(cube[,4],1)==round(now[4],1)&#
round(cube[,5],1)==round(now[5],1))
which(#
round(cube[,1],1)==round(now[1],1)&#
round(cube[,2],1)==round(now[2],1)&#
round(cube[,3],1)==round(now[3],1)&#
round(cube[,4],1)==round(now[4],1))
which(#
round(cube[,1],0)==round(now[1],0)&#
round(cube[,2],0)==round(now[2],0)&#
round(cube[,3],0)==round(now[3],0)&#
round(cube[,4],0)==round(now[4],0)&#
round(cube[,5],0)==round(now[5],0))
require(TMB)#
dyn.load(dynlib("simple"))
## Test data#
set.seed(123)#
y <- rep(1900:2010,each=2)#
year <- factor(y)#
quarter <- factor(rep(1:4,length.out=length(year)))#
period <- factor((y > mean(y))+1)#
## Random year+quarter effect, fixed period effect:#
B <- model.matrix(~year+quarter-1)#
A <- model.matrix(~period-1)#
B <- as(B,"dgTMatrix")#
A <- as(A,"dgTMatrix")#
u <- rnorm(ncol(B)) ## logsdu=0#
beta <- rnorm(ncol(A))*100#
eps <- rnorm(nrow(B),sd=1) ## logsd0=0#
x <- as.numeric( A %*% beta + B %*% u + eps )#
#
## Fit model#
obj <- MakeADFun(data=list(x=x, B=B, A=A),#
parameters=list(u=u*0, beta=beta*0, logsdu=1, logsd0=1),#
random="u",#
DLL="simple",#
silent=TRUE#
)#
opt <- nlminb(obj$par, obj$fn, obj$gr)
devtools::install_github("james-thorson/FishStatsUtils", ref="development")
devtools::install_github("james-thorson/VAST", ref="development")
library(testthat)
library(VAST)
devtools::install_github("james-thorson/VAST", ref="development")
library(VAST)
devtools::install_github("james-thorson/VAST", ref="development")
devtools::install_github("james-thorson/VAST")
install.packages('TMB', type = 'source')
library(TMB)
devtools::install_github("james-thorson/VAST")
library(VAST)
install_github("james-thorson/VAST", INSTALL_opts="--no-staged-install")
install.packages("devtools")#
library("devtools")
install_github("james-thorson/VAST", INSTALL_opts="--no-staged-install")#
# Load package#
library(VAST)
# Install and load devtools package#
install.packages("devtools")#
library("devtools")
library("devtools")
install.packages("digest")
devtools::install_github("hadley/devtools")
library(digest)
devtools::install_github("hadley/devtools")
library(processx)
install.package(processx)
install.packages("processx")
uninstall.R()
library(devtools)
library(NRG)
# Install package#
install_github("james-thorson/VAST", INSTALL_opts="--no-staged-install")#
# Load package#
library(VAST)
library(VAST)
install.package("VAST")
library(INLA)
library(TMB)
install.packages('TMB', type = 'source')
install.packages("TMB")
library(TMB)#
## Optionally:#
## precompile()#
runExample(all=TRUE)
library(shiny)#
library(dygraphs)#
library(httr)#
library(RCurl)#
library(ggplot2)#
library(dplyr)#
library(dygraphs)#
library(xts) # To make the convertion data-frame / xts format
input<-list()#
input$county<-"Washington : King"#
input$state<-"Idaho"#
input$dateoffsetIN=12#
input$lwr = 0.96#
input$upr = 1.6#
input$ylimmIN=c(.96,1.6)#
input$reflines1=4#
input$reflines2=7#
input$reflines3=14#
input$showrid =FALSE
convert_to_daily_ts <- function(x,y,n){#
A <- as.numeric(format(x[1],"%Y"))#
a <- as.numeric(format(x[1],"%j"))#
B <- as.numeric(format(x[n],"%Y"))#
b <- as.numeric(format(x[n],"%j"))#
AA <- as.numeric(x[1])#
BB <- as.numeric(x[n])#
#ts(y, start = c(A,a), end = c(B,b), frequency = 365)#
y_ts<- ts(y, start = AA, end = BB, frequency = 1)#
return(y_ts)#
}#
#
getReff<-function(datIN=sub,days=7,unit="deaths"){#
#dat <- ts(data.frame(date=datIN$date,datIN[unit]))#
dat <- (data.frame(date=datIN$date,datIN[unit]))#
n <- dim(dat)[1]#
days:n#
lnslope <- slope <- rep(NA,n)#
for(d in days:n){#
tt <- ((d-days)+1):d#
tmp <- dat[tt,]#
if(any(tmp[,2]==0))#
tmp[,2][tmp[,2]==0]<-NA#
if(!any(is.na(tmp[,2]))){#
lmm <- lm(log(tmp[,2])~tt)#
lnslope[d] <- (as.numeric(coef(lmm)[2]))#
slope[d] <- exp(lnslope[d])#
}else{#
lnslope[d] <- slope[d] <- NA#
}#
}#
return(slope)#
}#
#
# pre-code:#
url <- "https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv"#
csv <- read.csv(text=getURL(url), skip=0, header=T)#
csv$label <- paste(csv$state,":",csv$county)#
lab_counties <- as_tibble(csv)%>%#
group_by(state,county,label)%>%#
summarise(count=length(county))#
#
start_lab <- lab_counties[which(lab_counties$county=="King"&lab_counties$state=="Washington"),]
start_lab
start_lab <- data.frame(lab_counties[which(lab_counties$county=="King"&lab_counties$state=="Washington"),])
start_lab
countyIN <-lab_counties[lab_counties$label==input$county,]$county#
stateIN <- input$state#
sub <- csv[csv$label==input$county,]#
sub$date <- as.Date(sub$date)#
sub_state <- csv[csv$state==input$state,]#
sub_state$date <- as.Date(sub_state$date)
head(sub)
head(state)
head(sub_state)
state<-as_tibble(sub_state)%>%#
group_by(date,state)%>%#
summarize(cases=sum(cases),deaths=sum(deaths))
head(state)
sub <- csv[csv$label==input$county,]#
sub$date <- as.Date(sub$date)#
sub_state <- csv[csv$state==input$state,]#
sub_state$date <- as.Date(sub_state$date)#
state <- as_tibble(sub_state)%>%#
group_by(date,state)%>%#
summarize(cases=sum(cases),deaths=sum(deaths))#
sub$Reff_deaths <- getReff(unit="deaths",datIN=sub,days=input$dateoffsetIN)#
sub$Reff_cases <- getReff(unit="cases",datIN=sub,days=input$dateoffsetIN)#
state$Reff_deaths <- getReff(unit="deaths",datIN=state,days=input$dateoffsetIN)#
state$Reff_cases <- getReff(unit="cases",datIN=state,days=input$dateoffsetIN)#
# eval(parse(text=paste0("sub$'doubling every ",input$reflines1," days'<- exp((log(100)-log(50))/(",input$reflines1,"))")))#
# eval(parse(text=paste0("sub$'... ",input$reflines2," days'<- exp((log(100)-log(50))/(",input$reflines2,"))")))#
# eval(parse(text=paste0("sub$'... ",input$reflines3," days'<- exp((log(100)-log(50))/(",input$reflines3,"))")))#
lab<-c(paste0("doubling every ",input$reflines1," days"),#
paste0("... ",input$reflines2," days"),#
paste0("... ",input$reflines3," days"))#
exp((log(100)-log(50))/(input$reflines1))#
sub$lab1<-paste("R_e: deaths; doubling every",round((log(100)-log(50))/log(sub$Reff_deaths)),"days")#
sub$lab2<-paste("R_e: deaths; doubling every",round((log(100)-log(50))/log(sub$Reff_cases) ),"days")#
ref<- c(exp((log(100)-log(50))/(input$reflines1)),#
exp((log(100)-log(50))/(input$reflines2)),#
exp((log(100)-log(50))/(input$reflines3)))#
# Then you can create the xts format, and thus use dygraph#
plotdat1 <- xts(x = sub[,c("Reff_deaths","Reff_cases")], order.by = sub$date)#
plotdat2 <- xts(x = state[,c("Reff_deaths","Reff_cases")], order.by = state$date)
head(plotdat1)
head(plotdat2)
head(state)
head(sub)
input$state = "Washington"
sub <- csv[csv$label==input$county,]#
sub$date <- as.Date(sub$date)#
sub_state <- csv[csv$state==input$state,]#
sub_state$date <- as.Date(sub_state$date)#
state <- as_tibble(sub_state)%>%#
group_by(date,state)%>%#
summarize(cases=sum(cases),deaths=sum(deaths))#
sub$Reff_deaths <- getReff(unit="deaths",datIN=sub,days=input$dateoffsetIN)#
sub$Reff_cases <- getReff(unit="cases",datIN=sub,days=input$dateoffsetIN)#
state$Reff_deaths <- getReff(unit="deaths",datIN=state,days=input$dateoffsetIN)#
state$Reff_cases <- getReff(unit="cases",datIN=state,days=input$dateoffsetIN)#
# eval(parse(text=paste0("sub$'doubling every ",input$reflines1," days'<- exp((log(100)-log(50))/(",input$reflines1,"))")))#
# eval(parse(text=paste0("sub$'... ",input$reflines2," days'<- exp((log(100)-log(50))/(",input$reflines2,"))")))#
# eval(parse(text=paste0("sub$'... ",input$reflines3," days'<- exp((log(100)-log(50))/(",input$reflines3,"))")))#
lab<-c(paste0("doubling every ",input$reflines1," days"),#
paste0("... ",input$reflines2," days"),#
paste0("... ",input$reflines3," days"))#
exp((log(100)-log(50))/(input$reflines1))#
sub$lab1<-paste("R_e: deaths; doubling every",round((log(100)-log(50))/log(sub$Reff_deaths)),"days")#
sub$lab2<-paste("R_e: deaths; doubling every",round((log(100)-log(50))/log(sub$Reff_cases) ),"days")#
ref<- c(exp((log(100)-log(50))/(input$reflines1)),#
exp((log(100)-log(50))/(input$reflines2)),#
exp((log(100)-log(50))/(input$reflines3)))#
# Then you can create the xts format, and thus use dygraph#
plotdat1 <- xts(x = sub[,c("Reff_deaths","Reff_cases")], order.by = sub$date)#
plotdat2 <- xts(x = state[,c("Reff_deaths","Reff_cases")], order.by = state$date)
head(state)
head(sub)
head(csv)
which(csv$county=="King")
csv[cc,]
cc<-which(csv$county=="King")
csv[cc,]
sub
url <- "https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv"#
csv <- read.csv(text=getURL(url), skip=0, header=T)
cc<-which(csv$county=="King")
csv[cc,]
sub <- csv[csv$label==input$county,]#
sub$date <- as.Date(sub$date)#
sub_state <- csv[csv$state==input$state,]#
sub_state$date <- as.Date(sub_state$date)#
state <- as_tibble(sub_state)%>%#
group_by(date,state)%>%#
summarize(cases=sum(cases),deaths=sum(deaths))#
sub$Reff_deaths <- getReff(unit="deaths",datIN=sub,days=input$dateoffsetIN)#
sub$Reff_cases <- getReff(unit="cases",datIN=sub,days=input$dateoffsetIN)#
state$Reff_deaths <- getReff(unit="deaths",datIN=state,days=input$dateoffsetIN)#
state$Reff_cases <- getReff(unit="cases",datIN=state,days=input$dateoffsetIN)
url <- "https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv"#
csv <- read.csv(text=getURL(url), skip=0, header=T)#
csv$label <- paste(csv$state,":",csv$county)#
lab_counties <- as_tibble(csv)%>%#
group_by(state,county,label)%>%#
summarise(count=length(county))#
start_lab <- lab_counties[which(lab_counties$county=="King"&lab_counties$state=="Washington"),]
sub <- csv[csv$label==input$county,]#
sub$date <- as.Date(sub$date)#
sub_state <- csv[csv$state==input$state,]#
sub_state$date <- as.Date(sub_state$date)#
state <- as_tibble(sub_state)%>%#
group_by(date,state)%>%#
summarize(cases=sum(cases),deaths=sum(deaths))#
sub$Reff_deaths <- getReff(unit="deaths",datIN=sub,days=input$dateoffsetIN)#
sub$Reff_cases <- getReff(unit="cases",datIN=sub,days=input$dateoffsetIN)#
state$Reff_deaths <- getReff(unit="deaths",datIN=state,days=input$dateoffsetIN)#
state$Reff_cases <- getReff(unit="cases",datIN=state,days=input$dateoffsetIN)
plot(sub$Reff_deaths)
days<-1
datIN=sub
unit=
"deaths"
dat <- (data.frame(date=datIN$date,datIN[unit]))#
n <- dim(dat)[1]#
lnslope <- slope <- rep(NA,n)
days
n
for(d in days:n){#
tt <- ((d-days)+1):d#
tmp <- dat[tt,]#
if(any(tmp[,2]==0))#
tmp[,2][tmp[,2]==0]<-NA#
if(!any(is.na(tmp[,2]))){#
lmm <- lm(log(tmp[,2])~tt)#
lnslope[d] <- (as.numeric(coef(lmm)[2]))#
slope[d] <- exp(lnslope[d])#
}else{#
lnslope[d] <- slope[d] <- NA#
}#
}
lnslope
slope
d<-1
tt <- ((d-days)+1):d#
tmp <- dat[tt,]
tt
tmp
((d-days)+1):d
order(unique(csv$state))
sort(unique(csv$state))
load("/Users/kholsman/Documents/D_AFSC_Files/AFSC_data/01_SRVY_AFSC_data_all/Newest/Out_SMRY_QRYS/Biom_weighted/ebs/plk/ebs_plk_c_ED_BYstation_bin.Rdata")
ls()
rm(liast=ls())
"/Users/kholsman/Documents/D_AFSC_Files/AFSC_data/01_SRVY_AFSC_data_all/Newest/Out_SMRY_QRYS/Biom_weighted/ebs/plk/ebs_plk_c_ED_NEBS_BYstation_bin.Rdata"
ls()
load("/Users/kholsman/Documents/D_AFSC_Files/AFSC_data/01_SRVY_AFSC_data_all/Newest/Out_SMRY_QRYS/Biom_weighted/annual_mn/BS_c_ED_annual_mn.Rdata")
ls()
load("/Users/kholsman/Documents/D_AFSC_Files/AFSC_data/01_SRVY_AFSC_data_all/Newest/Out_SMRY_QRYS/Biom_weighted/c_USE/BS_c_ED.Rdata")
ls()
names(BS_c_USE)
BS_c_USE[BS_c_USE$R_ggd==Inf,]
BS_c_USE[BS_c_USE$R_ggd==Inf,1:10]
head(BS_c_USE[BS_c_USE$R_ggd==Inf,1:10])
heaD(BS_c_USE)
head(BS_c_USE)
which(R_ggd==Inf)
which(BS_c_USE$R_ggd==Inf)
BS_c_USE[which(BS_c_USE$R_ggd==Inf),]
ddd<-BS_c_USE[which(BS_c_USE$R_ggd==Inf),]
i<-1
load("/Users/kholsman/Documents/D_AFSC_Files/AFSC_data/01_SRVY_AFSC_data_all/Newest/Out_SMRY_QRYS/Biom_weighted/c_USE/BS_c_ED.Rdata")dd
dd$Obs
ddd$Obs
ddd$totK
ddd$totJ
ddd$tot_C_J_gpred
BS_c_USE[which(BS_c_USE$W_use==0),]
load("/Users/kholsman/GitHub_new/EBM_Holsman_NatComm/data/out/EBM_ceattlenew.Rdata")
ls()
C_thresh_13_1
load("/Users/kholsman/GitHub_new/EBM_Holsman_NatComm/data/out/multispp_simulations.Rdata")
dat_2_5_12
load("/Users/kholsman/GitHub_new/EBM_Holsman_NatComm/data/out/risk.Rdata")
ls()
risk12
load("/Users/kholsman/GitHub_new/EBM_Holsman_NatComm/data/in/raw/covariates.Rdata")
ls()
rm(list=ls())
load("/Users/kholsman/GitHub_new/EBM_Holsman_NatComm/data/in/raw/covariates.Rdata")
head(covariates)
write.csv(covariates,file="/Users/kholsman/GitHub_new/EBM_Holsman_NatComm/data/in/raw/covariates.csv",sep=",")
devtools::install_github("ropensci/rfigshare")
require(rfigshare)
fs_author_search("Boettiger")
"/Users/kholsman/GitHub_new/EBM_Holsman_NatComm"
fs_author_search("Boettiger")
600/400
(600-400)/400
15000/5000
log(.2,base=10)
10^( log(.2,base=10))
10^(-.080)
(10^(-.080))/9
((10^(-.080))/9)*100
10^0
1-(10^-0.08)
(1-(10^-0.08))/9
((1-(10^-0.08))/9)*100
10^-.2
(10^-.2)/9
(10^-.2)*100/9
10^-.08
10^-.2
10^-.1
10^0
79/.95
69800*.95
66310-54000
890-660
.8*(890-660)
4100/11500
library("mgcv")require("nlme")dum <- rep(1,18)b1 <- gam(travel ~ s(Rail, bs="re", by=dum), data=Rail, method="REML")b2 <- gam(travel ~ s(Rail, bs="re"), data=Rail, method="REML")head(predict(b1, newdata = cbind(Rail, dum = dum))) # ranefs onhead(predict(b1, newdata = cbind(Rail, dum = 0))) # ranefs offhead(predict(b2, newdata = Rail, exclude = "s(Rail)")) # ranefs off, no dummy
library("mgcv")
require("nlme")
dum <- rep(1,18)b1 <- gam(travel ~ s(Rail, bs="re", by=dum), data=Rail, method="REML")b2 <- gam(travel ~ s(Rail, bs="re"), data=Rail, method="REML")
dum <- rep(1,18)
b1 <- gam(travel ~ s(Rail, bs="re", by=dum), data=Rail, method="REML")
b2 <- gam(travel ~ s(Rail, bs="re"), data=Rail, method="REML")
head(predict(b1, newdata = cbind(Rail, dum = dum))) # ranefs on
head(predict(b1, newdata = cbind(Rail, dum = 0))) # ranefs off
head(predict(b2, newdata = Rail, exclude = "s(Rail)"))
Rail$dum
head(Rail)
dum
.9*124
111+15
967.54-480
817-557
713-557
156+487
42*9.5
14.5*42
7.5*42
480+557
967.54+713.74
967.54+315
1282-1037
620+967.54
1587-1037
load("/Users/kholsman/GitHub_new/ACLIM2/Data/in/Newest/Rdata/grid_list.Rdata")
ls()
head(* grid_list)
head(grid_list)
names(grid_list)
grid_list$stations
head(grid_list$stations)
names(grid_list)
head(grid_list$strata_list)
head(grid_list$survey)
unique(grid_list$survey)
unique(unlist(grid_list$survey))
names(grid_list)
head(grid_list$stations)
unique(grid_list$stations$Stratum)
head(grid_list$tmpgrid)
head(grid_list$sub_ROMS2)
head(grid_list$grid)
names(grid_list$grid)
head(grid_list$grid$survey)
names(grid_list$grid)
unique(grid_list$grid$survey)
unique(unlist(grid_list$grid$survey))
unique(as.vector(grid_list$grid$survey))
head(grid_list$stations)
ls()
load("/Users/kholsman/GitHub_new/ACLIM2/Data/in/Newest/Rdata/B10K-K20_CORECFS/Level3/ACLIMsurveyrep_B10K-K20_CORECFS.Rdata")
ls()
devtools::install_github("r-spatial/sf", configure.args="--with-proj-lib=/usr/local/lib/")
devtools::install_github("r-spatial/stars", configure.args="--with-proj-lib=/usr/local/lib/")
library(stars)
setwd("/Volumes/LaCie/GitHub_cloud/ACLIM2") # loads packages, data, setup, etc.#
tmstp <- "2022_10_17" update_biascorrection <- FALSE suppressMessages(source("R/make.R")) tmstp <- "2022_10_17" Rdata_path <- paste0("../../romsnpz/",tmstp,"_Rdata") main <- getwd() #"~/GitHub_new/ACLIM2" tmstamp1 <- format(Sys.time(), "%Y%m%d") # tmstamp1 <- "20220428"#
update_hind <- TRUE # set to true to update hind and hindS; needed annually update_proj <- TRUE # set to true to update fut; not needed update_hist <- TRUE # set to true to update fut; not needed#
# the reference years for bias correcting in R/setup.R ref_years # the year to z-score scale / delta in R/setup.R deltayrs data_path#
#load(file.path(Rdata_path,"../weekly_vars_C.Rdata")) load(file.path(Rdata_path,"weekly_vars.Rdata")) #load(file.path(Rdata_path,"../srvy_vars_C.Rdata")) load(file.path(Rdata_path,"srvy_vars.Rdata"))#
load(file.path(Rdata_path,"l3srvy_varlist.Rdata")) load(file.path(Rdata_path,"l3wk_varlist.Rdata")) load(file.path(Rdata_path,"l3srvy_varlist_H16.Rdata")) load(file.path(Rdata_path,"l3wk_varlist_H16.Rdata"))#
load(file.path(Rdata_path,"l2_vars.Rdata"))#
vl1 <- l3srvy_varlist #srvy_vars[!srvy_vars%in%rm_var_list] vl2 <- l3wk_varlist# weekly_vars[!weekly_vars%in%rm_wk_list]#
# add in largeZoop (gets generated in make_indices_region_new.R) vl <- c(unique(c(vl1,vl2)),"largeZoop_integrated")#
# Identify which variables would be normally # distributed (i.e., can have negative values) normvl <- c( vl[grep("pH",vl)], vl[grep("temp",vl)], vl[grep("Ben",vl)], vl[grep("Hsbl",vl)], vl[grep("shflux",vl)], vl[grep("ssflux",vl)], vl[grep("vNorth",vl)], vl[grep("uEast",vl)]) normlist <- data.frame(var = vl, lognorm = "log") normlist$lognorm[normlist$var%in%normvl] <- "none" normlist$lognorm[normlist$var%in% c( vl[grep("aice",vl)], vl[grep("fracbelow0",vl)], vl[grep("fracbelow1",vl)], vl[grep("fracbelow2",vl)])] <- "logit"
normlist
# distributed (i.e., can have negative values) normvl <- c( vl[grep("pH",vl)], vl[grep("temp",vl)], vl[grep("Ben",vl)], vl[grep("Hsbl",vl)], vl[grep("shflux",vl)], vl[grep("ssflux",vl)], vl[grep("vNorth",vl)], vl[grep("uEast",vl)]) normlist <- data.frame(var = vl, lognorm = "log")
normlist
normlist <- data.frame(var = vl, lognorm = "log",stringsAsFactors = F) normlist$lognorm[normlist$var%in%normvl] <- "none" normlist$lognorm[normlist$var%in% c( vl[grep("aice",vl)], vl[grep("fracbelow0",vl)], vl[grep("fracbelow1",vl)], vl[grep("fracbelow2",vl)])] <- "logit"
normlist
weekly_vars <- c(weekly_vars,"largeZoop_integrated") srvy_vars <- c(srvy_vars,"largeZoop_integrated") save(normlist,file = file.path(Rdata_path,"normlist.Rdata")) write.csv(normlist,file = file.path(Rdata_path,"normlist.csv")) save(weekly_vars,file = "Data/out/weekly_vars.Rdata") save(srvy_vars,file = "Data/out/srvy_vars.Rdata") write.csv(normlist,file = file.path("Data/out/","normlist.csv"))#
# generate indices and bias corrected projections # this takes about 30 mins each # ------------------------------------------- # CMIP6 K20P19 # -------------------------------------------
gcmcmipL2 <- c("B10K-K20P19_CMIP5_MIROC", "B10K-K20P19_CMIP5_GFDL", "B10K-K20P19_CMIP5_CESM")
suppressMessages(makeACLIM2_L4_Indices_strata( CMIP_fdlr = "Data/out/K20P19_CMIP5", CMIP = "CMIP5", scenIN = c("rcp45","rcp85"), hind_sim = "B10K-K20P19_CORECFS", gcmcmipLIST = gcmcmipL2, subfldr = "BC_ACLIMregion", sim_listIN = sim_list, varlistIN = normlist$var, #varlistIN = c("aice","largeZoop_integrated"), prefix = "ACLIMregion")) gc()
library(dplyr)
test%>%across()
install.library(dplyr)
install.libraries(dplyr)