From 12622a0d17861ab8b733e9f53feaaf851f86e4b3 Mon Sep 17 00:00:00 2001
From: PharmCat <v.s.arnautov@yandex.ru>
Date: Sun, 21 Mar 2021 14:06:02 +0300
Subject: [PATCH] v0.7.1f

---
 Project.toml               | 2 +-
 change.log                 | 2 ++
 docs/src/bioequivalence.md | 5 +++++
 src/Metida.jl              | 4 ++--
 test/test.jl               | 7 ++++---
 5 files changed, 14 insertions(+), 6 deletions(-)

diff --git a/Project.toml b/Project.toml
index a15b9bf7..8bb62b06 100644
--- a/Project.toml
+++ b/Project.toml
@@ -19,7 +19,7 @@ CategoricalArrays = "0.8, 0.9"
 Distributions = "0.20, 0.21, 0.22, 0.23, 0.24"
 ForwardDiff = "0.10"
 LineSearches = "7"
-Optim = "0.19, 0.20, 0.21, 0.22, 1.0, 1.1, 1.2"
+Optim = "1.0, 1.1, 1.2, 1.3"
 StatsBase = "0.29, 0.30, 0.31, 0.32, 0.33"
 StatsModels = "0.6"
 Tables = "1"
diff --git a/change.log b/change.log
index afe92341..ff4ef94a 100644
--- a/change.log
+++ b/change.log
@@ -5,6 +5,8 @@ v0.7.1
   * F value (test)
   * C matrix - pinv to inv
   * minor Show bugfix
+  * changes in tests - stable models used
+  * export StatsBase methods
   * documentation
 
 v0.7.0
diff --git a/docs/src/bioequivalence.md b/docs/src/bioequivalence.md
index 7ee62513..a4bca1cb 100644
--- a/docs/src/bioequivalence.md
+++ b/docs/src/bioequivalence.md
@@ -28,3 +28,8 @@ fit!(lmm)
 ci = confint(lmm)[end]
 exp.(ci) .* 100.0
 ```
+
+## Reference
+
+  * [Annex](https://www.ema.europa.eu/en/documents/other/31-annex-i-statistical-analysis-methods-compatible-ema-bioequivalence-guideline_en.pdf) I for EMA’s Guideline on the Investigation of Bioequivalence
+  * [FDA Guidance for Industry: Statistical Approaches to Establishing Bioequivalence](https://www.fda.gov/media/70958/download), APPENDIX F
diff --git a/src/Metida.jl b/src/Metida.jl
index 6c465eb9..0d4e5119 100644
--- a/src/Metida.jl
+++ b/src/Metida.jl
@@ -29,6 +29,8 @@ fit!, LMM, VarEffect, theta, logreml, m2logreml, thetalength, dof_satter, dof_co
 gmatrix, rmatrix, vmatrix!,
 AbstractCovarianceType, AbstractCovmatMethod, MetidaModel
 
+export coef, coefnames, confint, nobs, dof_residual, dof, loglikelihood, aic, bic, aicc, isfitted, vcov, stderror, modelmatrix, response
+
 include("abstracttype.jl")
 include("sweep.jl")
 include("varstruct.jl")
@@ -71,8 +73,6 @@ function _precompile_()
 
     precompile(Tuple{typeof(Metida.initvar),  Array{Float64, 1},  Array{Float64, 2}})
 
-    #precompile(Tuple{typeof(Metida.intersectsubj),  Array{VarEffect, 1},  VarEffect})
-
 end
 _precompile_()
 #include(".jl")
diff --git a/test/test.jl b/test/test.jl
index 69966419..a432310a 100644
--- a/test/test.jl
+++ b/test/test.jl
@@ -186,7 +186,7 @@ end
     @test Metida.m2logreml(lmm) ≈ 10.065239006121315 atol=1E-6
 end
 ################################################################################
-#                                  ftdf
+#                                  ftdf / 1fptime.csv
 ################################################################################
 @testset "  Model: Categorical * Continuous predictor, CSH/SI        " begin
     # nowarn
@@ -209,10 +209,11 @@ end
     @test Metida.m2logreml(lmm) ≈ 1300.1807598168923 atol=1E-6
 end
 ################################################################################
-#                                  ftdf2
+#                                  ftdf2 / 1freparma.csv
 ################################################################################
 @testset "  Model: Categorical * Continuous predictor, 0/ARMA        " begin
     # nowarn
+    # SPSS 715.452856
     io = IOBuffer();
     lmm = Metida.LMM(@formula(response ~ 1 + factor*time), ftdf2;
     repeated = Metida.VarEffect(Metida.@covstr(time|subject&factor), Metida.ARMA),
@@ -239,7 +240,7 @@ end
     @test Metida.m2logreml(lmm) ≈ 731.7794071577566 atol=1E-6
 end
 ################################################################################
-#                                  ftdf3
+#                                  ftdf3 / 2f2rand.csv
 ################################################################################
 @testset "  Model: CS, CS/SI                                         " begin
     lmm = Metida.LMM(@formula(response ~ 1 + factor), ftdf3; contrasts=Dict(:factor => DummyCoding(; base=1.0)),