Merge pull request #5 from fbertran/IntoDoMC

Merge 2.2 updates into doMC branch

Author: fbertran

.Rbuildignore

@@ -1,5 +1,12 @@
+genlogo.R
+.gitignore
+^codecov\.yml$
+^revdep$
+^_pkgdown\.yml$
+^figure$
+^pkgdown$
 ^Meta$
-^doc$
+#^doc$
 #All packages
 ^.*\.Rproj$
 ^\.Rproj\.user$
@@ -13,15 +20,31 @@
 #If README too big, not on CRAN but only on git
 ^man/figures*$
 ^README\.Rmd$
-^README\.md$
+#^README\.md$
 #Specific
 ^fullrespdf*$
 ^inst/animation*$
 ^articles*$
+^review*$
+^\.github$
+
+#For the cached chunks of the vignettes
+^vignettes.*cache$
+#For the image outputs of the vignettes
+^vignettes.*files$
+#Specific
+^animation.gif$
+^evolution_example*$
+^add_data
+^doc$
+^Meta$
+^cran-comments[.].*$
+
 #Vignette cache
 ^cran-comments[.].*$
 ^vignettes/.*[.](pdf|PDF)$
 ^vignettes/.*[.](r|R)$
 ^vignettes/[.]install_extras$
 ^vignettes/.*_cache$
 ^vignettes/.*_files$
+

.github/.gitignore

@@ -0,0 +1 @@
+*.html

.github/workflows/R-CMD-check.yaml

@@ -0,0 +1,38 @@
+# For help debugging build failures open an issue on the RStudio community with the 'github-actions' tag.
+# https://community.rstudio.com/new-topic?category=Package%20development&tags=github-actions
+on:
+  push:
+    branches:
+      - main
+      - master
+  pull_request:
+    branches:
+      - main
+      - master
+
+name: R-CMD-check
+
+jobs:
+  R-CMD-check:
+    runs-on: macOS-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+    steps:
+      - uses: actions/checkout@v2
+      - uses: r-lib/actions/setup-r@v1
+      - name: Install ghostscript on macOS
+        if: runner.os == 'macOS'
+        run: brew install ghostscript
+      - name: Install Magick on macOS
+        if: runner.os == 'macOS'
+        run: brew install imagemagick
+      - name: Install dependencies
+        run: |
+          install.packages(c("remotes", "rcmdcheck"))
+          remotes::install_deps(dependencies = TRUE)
+        shell: Rscript {0}
+      - name: Check
+        run: |
+          options(crayon.enabled = TRUE)
+          rcmdcheck::rcmdcheck(args = "--no-manual", error_on = "error")
+        shell: Rscript {0}

.gitignore

@@ -1,17 +1,30 @@
-Meta
-doc
-inst/doc
 # History files
 .Rhistory
 .Rapp.history
 
 # Session Data files
 .RData
+
 # RStudio files
 .Rproj.user/
+.Rproj.user/*
+
+.DS_Store
+
 # OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3
 .httr-oauth
+
 # Shiny token, see https://shiny.rstudio.com/articles/shinyapps.html
 rsconnect/
 .Rproj.user
-.DS_Store
+
+# Files for review
+review/
+
+#doc
+Meta
+
+# User-specific files
+.Ruserdata
+doc
+inst/doc

DESCRIPTION

@@ -1,10 +1,11 @@
 Package: SelectBoost
 Type: Package
 Title: A General Algorithm to Enhance the Performance of Variable Selection Methods in Correlated Datasets
-Version: 1.3.0
-Date: 2019-05-03
+Version: 2.2.0
+Date: 2021-03-20
 Depends: R (>= 2.10)
-Imports: lars, glmnet, igraph, parallel, msgps, Rfast, doMC, foreach, methods, Cascade, graphics, grDevices
+biocViews:
+Imports: lars, glmnet, igraph, parallel, msgps, Rfast, doMC, foreach, methods, Cascade, graphics, grDevices, varbvs, spls, abind
 Suggests: 
     knitr,
     rmarkdown,
@@ -17,11 +18,12 @@ Authors@R: c(
 	person(given = "Nicolas", family= "Jung", role = c("ctb"), email = "[email protected]"))
 Author: Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>), Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>), Ismail Aouadi [ctb], Nicolas Jung [ctb]
 Maintainer: Frederic Bertrand <[email protected]>
-Description: The 'selectboost' algorithm (Aouadi et al. 2018, <arXiv:1810.01670>) is a general algorithm which improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.
+Description: An implementation of the selectboost algorithm (Bertrand et al. 2020, 'Bioinformatics', <doi:10.1093/bioinformatics/btaa855>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.
 License: GPL-3
 Encoding: UTF-8
 Classification/MSC: 62H11, 62J12, 62J99
+LazyData: true
 VignetteBuilder: knitr
-RoxygenNote: 6.1.1
-URL: https://github.com/fbertran/SelectBoost, http://www-irma.u-strasbg.fr/~fbertran/
-BugReports: https://github.com/fbertran/SelectBoost/issues
+RoxygenNote: 7.1.1
+URL: https://fbertran.github.io/SelectBoost/, https://github.com/fbertran/SelectBoost/
+BugReports: https://github.com/fbertran/SelectBoost/issues/

NAMESPACE

@@ -9,6 +9,8 @@ S3method(summary,selectboost)
 S3method(trajC0,selectboost)
 export(AICc_glmnetB)
 export(BIC_glmnetB)
+export(alasso_enet_msgps_all)
+export(alasso_msgps_all)
 export(auto.analyze)
 export(autoboost)
 export(boost.Xpass)
@@ -21,43 +23,57 @@ export(boost.normalize)
 export(boost.random)
 export(boost.select)
 export(compsim)
+export(enet_msgps_all)
 export(enetf_msgps_AICc)
 export(enetf_msgps_BIC)
 export(enetf_msgps_Cp)
 export(enetf_msgps_GCV)
 export(fastboost)
+export(force.non.inc)
 export(group_func_1)
 export(group_func_2)
 export(lasso_cascade)
 export(lasso_cv_glmnet_1se)
 export(lasso_cv_glmnet_1se_weighted)
+export(lasso_cv_glmnet_all_5f)
 export(lasso_cv_glmnet_bin_1se)
+export(lasso_cv_glmnet_bin_all)
 export(lasso_cv_glmnet_bin_min)
 export(lasso_cv_glmnet_min)
 export(lasso_cv_glmnet_min_weighted)
 export(lasso_cv_lars_1se)
 export(lasso_cv_lars_min)
 export(lasso_glmnet_bin_AICc)
 export(lasso_glmnet_bin_BIC)
+export(lasso_glmnet_bin_all)
 export(lasso_msgps_AICc)
 export(lasso_msgps_BIC)
 export(lasso_msgps_Cp)
 export(lasso_msgps_GCV)
+export(lasso_msgps_all)
 export(rerr)
 export(ridge_logistic)
 export(selectboost)
+export(sgpls_spls_all)
 export(simulation_DATA)
 export(simulation_X)
 export(simulation_cor)
+export(spls_spls_all)
+export(splsda_spls_all)
 export(trajC0)
+export(varbvs_binomial_all)
+export(varbvs_linear_all)
 exportClasses(network.confidence)
 exportMethods(plot)
 import(glmnet)
 import(lars)
 import(msgps)
+import(spls)
+import(varbvs)
 importClassesFrom(Cascade,micro_array)
 importFrom(Rfast,rvmf)
 importFrom(Rfast,vmf.mle)
+importFrom(abind,abind)
 importFrom(doMC,registerDoMC)
 importFrom(foreach,"%dopar%")
 importFrom(foreach,foreach)

NEWS

@@ -1,4 +1,24 @@
-# SelectBoost 1.3.0
+# SelectBoost 2.2.0
+
+* Add more badges, logo and update pkgdown site
+* Add github actions
+* Added reference to a Leukemia article that used the Selectboost algorithm to decypher multi-omics networks.
+
+# SelectBoost 2.1.0
+
+* Added reference for the article about the Selectboost algorithm that was published in Bioinformatics
+
+# SelectBoost 2.0.0
+
+* Major imrovement of the package with new c0 sequences: "mixed", "zoom_l" and "zoom_q". The use of the "zoom_l" sequence is recommended. New function to perform a post-processing step that ensures that the proportion of times a variable is selected is non-increasing with respect to 1-c0.
+
+# SelectBoost 1.4.1
+
+* Update to datalist, lazyload data to fix CRAN check, correct some typos in readme and in compsim.simuls help page.
+
+# SelectBoost 1.4.0
+
+* Fixes to get in CRAN.
 
 * Completed third vignette.
 

NEWS.md

@@ -9,8 +9,8 @@
 #' @name AICc_BIC_glmnetB
 #'
 #' @return A list relevant to model selection.
-#' @author Eric C. Chi, slightly modified by Frederic Bertrand, \email{frederic.bertrand@@math.unistra.fr}
-#' @references \emph{Robust Parametric Classification and Variable Selection by a Minimum Distance Criterion}, Chi and Scott, Journal of Computational and Graphical Statistics, \bold{23}(1), 2014, p111--128, \url{https://doi.org/10.1080/10618600.2012.737296}.
+#' @author Frederic Bertrand, \email{frederic.bertrand@@math.unistra.fr}
+#' @references \emph{Robust Parametric Classification and Variable Selection by a Minimum Distance Criterion}, Chi and Scott, Journal of Computational and Graphical Statistics, \bold{23}(1), 2014, p111--128, \doi{10.1080/10618600.2012.737296}.
 #' @seealso \code{\link{var_select}}
 #' @examples
 #' set.seed(314)
@@ -125,7 +125,7 @@ BIC_glmnetB = function(Z, Y, glmnet.model, alpha, modelSet, reducer='median') {
 				corzz = function(zz) {
 				  if(is.infinite(exp(zz))){
 				    return(zz)
-				    cat(L[i],"\n")
+				    #cat(L[i],"\n")
 				  } else {
 				    return(log(1 + exp(zz)))
 				  }
@@ -149,7 +149,7 @@ BIC_glmnetB = function(Z, Y, glmnet.model, alpha, modelSet, reducer='median') {
 				corzz = function(zz) {
 				  if(is.infinite(exp(zz))){
 				    return(zz)
-				    cat(L[i],"\n")
+				    #cat(L[i],"\n")
 				  } else {
 				    return(log(1 + exp(zz)))
 				  }
@@ -184,7 +184,7 @@ BIC_glmnetB = function(Z, Y, glmnet.model, alpha, modelSet, reducer='median') {
 				corzz = function(zz) {
 				  if(is.infinite(exp(zz))){
 				    return(zz)
-				    cat(L[i],"\n")
+				    #cat(L[i],"\n")
 				  } else {
 				    return(log(1 + exp(zz)))
 				  }
@@ -208,7 +208,7 @@ BIC_glmnetB = function(Z, Y, glmnet.model, alpha, modelSet, reducer='median') {
 				corzz = function(zz) {
 				  if(is.infinite(exp(zz))){
 				    return(zz)
-				    cat(L[i],"\n")
+				    #cat(L[i],"\n")
 				  } else {
 				    return(log(1 + exp(zz)))
 				  }
@@ -279,7 +279,7 @@ AICc_glmnetB = function(Z, Y, glmnet.model, alpha, modelSet, reducer='median') {
 				corzz = function(zz) {
 				  if(is.infinite(exp(zz))){
 				    return(zz)
-				    cat(L[i],"\n")
+				    #cat(L[i],"\n")
 				  } else {
 				    return(log(1 + exp(zz)))
 				  }
@@ -303,7 +303,7 @@ AICc_glmnetB = function(Z, Y, glmnet.model, alpha, modelSet, reducer='median') {
 				corzz = function(zz) {
 				  if(is.infinite(exp(zz))){
 				    return(zz)
-				    cat(L[i],"\n")
+				    #cat(L[i],"\n")
 				  } else {
 				    return(log(1 + exp(zz)))
 				  }
@@ -338,7 +338,7 @@ AICc_glmnetB = function(Z, Y, glmnet.model, alpha, modelSet, reducer='median') {
 				corzz = function(zz) {
 				  if(is.infinite(exp(zz))){
 				    return(zz)
-				    cat(L[i],"\n")
+				    #cat(L[i],"\n")
 				  } else {
 				    return(log(1 + exp(zz)))
 				  }
@@ -362,7 +362,7 @@ AICc_glmnetB = function(Z, Y, glmnet.model, alpha, modelSet, reducer='median') {
 				corzz = function(zz) {
 				  if(is.infinite(exp(zz))){
 				    return(zz)
-				    cat(L[i],"\n")
+				    #cat(L[i],"\n")
 				  } else {
 				    return(log(1 + exp(zz)))
 				  }

R/BIC_AICc_glmnet.R

@@ -12,9 +12,14 @@
 #'
 #' @docType package
 #' @name SelectBoost
-#' @references \emph{selectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets}, Ismaïl Aouadi, Nicolas Jung, Raphael Carapito, Laurent Vallat, Seiamak Bahram, Myriam Maumy-Bertrand, Frédéric Bertrand, \url{https://arxiv.org/abs/1810.01670}
+#' @references F. Bertrand, I. Aouadi, N. Jung, R. Carapito, L. Vallat, S. Bahram, M. Maumy-Bertrand (2020). SelectBoost: a general algorithm to enhance the performance of variable selection methods in correlated datasets, \emph{Bioinformatics}. \doi{10.1093/bioinformatics/btaa855}
+#'
+#' SelectBoost was used to decypher networks in
+#' C. Schleiss, [...], M. Maumy-Bertrand, S. Bahram, F. Bertrand, and L. Vallat. (2021). Temporal multiomic modelling reveals a B-cell receptor proliferative program in chronic lymphocytic leukemia. \emph{Leukemia}.
+#' 
 # @import lars glmnet igraph parallel msgps Rfast doMC foreach
-#' @import lars glmnet msgps
+#' @import lars glmnet msgps spls varbvs
+#' @importFrom abind abind
 #' @importFrom methods new
 #' @importFrom utils head tail
 #' @importFrom stats coef cor heatmap predict var
@@ -28,5 +33,22 @@
 #' @importFrom igraph graph.adjacency infomap.community communities
 #' @importFrom grDevices rgb gray
 #' @importFrom graphics image layout
-
+#' @examples
+#' set.seed(314)
+#' xran=matrix(rnorm(75),15,5)
+#' ybin=sample(0:1,15,replace=TRUE)
+#' yran=rnorm(15)
+#'
+#' #For quick test purpose, not meaningful, should be run with greater value of B
+#' #(disabling parallel computing as well)
+#' res.fastboost <- fastboost(xran,yran,B=3,use.parallel=FALSE)
+#'
+#' \donttest{
+#' fastboost(xran,yran)
+#' #Customize resampling levels
+#' fastboost(xran,yran,steps.seq=c(.99,.95,.9),c0lim=FALSE)
+#'
+#' #Binary logistic regression
+#' fastboost(xran,ybin,func=lasso_cv_glmnet_bin_min)
+#'}
 NULL