Fix for incompatibility with PLNmodels latest release

.Rprofile

@@ -1 +1 @@
-options(repos=structure(c(CRAN="http://cran.us.r-project.org")))
+options(repos=structure(c(CRAN="https://cloud.r-project.org/")))

.gitignore

@@ -4,3 +4,4 @@
 .Ruserdata
 inst/doc
 docs
+dev_history.R

DESCRIPTION

@@ -2,37 +2,45 @@ Type: Package
 Package: EMtree
 Title: Infers Direct Species Association Networks using Tree Averaging
 Version: 1.1.0
-Author: Raphaelle Momal <[email protected]>
-Maintainer: Raphaelle Momal <[email protected]>
-Description: Uses averages over spanning trees within an Expectation Maximization algorithm to infer conditional dependence networks. Involves plotting functionalities.
+Authors@R: c(
+  person("Raphaëlle", "Momal", role = c("aut", "cre","cph"), email = "[email protected]",
+      comment = c(ORCID = "0000-0002-1550-4530"))) 
+Description: The spanning tree averaging within an Expectation-Maximization algorithm 
+    (Momal, Robin and Ambroise, 2020 <doi:10.1111/2041-210X.13380>) leverages the 
+        Matrix Tree Theorem to infer conditional dependence networks, such as direct species association 
+            networks.
 License: GPL-3
 Imports: 
     dplyr,
     ggplot2,
     ggraph,
     huge,
-    influenceR,
     magrittr,
     Matrix,
     mvtnorm,
     parallel,
     PLNmodels,
-    purrr,
     tibble,
     tidygraph,
-    tidyr,
     vegan,
+    gridExtra,
+    pillar,
     ade4,
-    gridExtra
+    viridis,
+    gmp
 Suggests: 
     testthat (>= 2.1.0),
     knitr,
     rmarkdown,
     networkD3,
+    pROC,
+    tidyr,
     RColorBrewer
 Encoding: UTF-8
-LazyData: false
-RoxygenNote: 7.1.1
+LazyData: true
+RoxygenNote: 7.3.2
+Depends: R (>= 3.5)
 VignetteBuilder: knitr
 URL: https://rmomal.github.io/EMtree/
 BugReports: https://github.com/Rmomal/EMtree/issues
+Language: en-US

R/F_inference.R

@@ -410,8 +410,8 @@ ResampleEMtree <- function(counts,covar_matrix=NULL, unlinked=NULL,
   #- parallel computation of S fits of new_EMtree
   if(is.null(user_covariance_estimation)){
     suppressWarnings(
-      PLNfit <- PLNmodels::PLN(counts ~ -1  + offset(log(O)) + .,
-                               data=data.frame(X),control=list("trace"=0))
+      PLNfit <- PLNmodels::PLN(formula=counts ~ -1  + offset(log(O)) + .,
+                               data=data.frame(X),control=PLN_param(trace=0))
     )}
   obj<-parallel::mclapply(1:S,function(b){
     if(init){

README.Rmd

@@ -28,9 +28,9 @@ By default, it uses the Poisson log-Normal Model ([PLNmodels](https://github.com
 ### CRAN dependencies
 
 ```{r,eval=FALSE}
-required_CRAN <- c("Matrix", "purrr","parallel",  "mvtnorm", "vegan","huge",
-                   "ggplot2", "magrittr", "dplyr","tidyr", "tibble",
-                   "PLNmodels","ggraph", "tidygraph", "ade4")
+required_CRAN <- c("Matrix", "parallel",  "mvtnorm", "vegan",
+                   "ggplot2", "magrittr", "dplyr", "tibble",
+                   "PLNmodels","ggraph", "tidygraph","huge")
 not_installed_CRAN <- setdiff(required_CRAN, rownames(installed.packages()))
 if (length(not_installed_CRAN) > 0) install.packages(not_installed_CRAN)
 ```

README.md

@@ -31,9 +31,9 @@ copulas, or Gaussian data transformations.
 ### CRAN dependencies
 
 ``` r
-required_CRAN <- c("Matrix", "purrr","parallel",  "mvtnorm", "vegan","huge",
-                   "ggplot2", "magrittr", "dplyr","tidyr", "tibble",
-                   "PLNmodels","ggraph", "tidygraph", "ade4")
+required_CRAN <- c("Matrix", "parallel",  "mvtnorm", "vegan",
+                   "ggplot2", "magrittr", "dplyr", "tibble",
+                   "PLNmodels","ggraph", "tidygraph","huge")
 not_installed_CRAN <- setdiff(required_CRAN, rownames(installed.packages()))
 if (length(not_installed_CRAN) > 0) install.packages(not_installed_CRAN)
 ```

cran-comments.md

@@ -1,10 +1,13 @@
 ## Test environments
-* local R installation, R 4.0.2
-* ubuntu 16.04 (on travis-ci), R 4.0.2
-* win-builder (devel)
+- R-hub fedora-clang-devel (r-devel)
+- R-hub windows-x86_64-devel (r-devel)
+- R-hub macos-highsierra-release (r-release)
 
 ## R CMD check results
+> On fedora-clang-devel (r-devel), windows-x86_64-devel (r-devel)
+  checking CRAN incoming feasibility ... NOTE
+  Maintainer: ‘Raphaelle Momal <[email protected]>’
+
+  New submission
 
-0 errors | 0 warnings | 1 note
-
-* This is a new release.
+0 errors ✓ | 0 warnings ✓ | 1 note x

dev_history.R

@@ -13,6 +13,7 @@ attachment::att_to_description()
 use_build_ignore("dev_history.R")
 
 usethis::use_vignette("Fatala_Net","Fatala fishes")
+usethis::use_vignette("Partial_correlations","Partial correlations")
 pkgdown::build_site()
 
 # workflow
@@ -44,14 +45,14 @@ covr::report()
 results <- rhub::check_for_cran(platforms=c("fedora-clang-devel",
                                             "windows-x86_64-devel",
                                             "macos-highsierra-release"))
-
+results2 <- rhub::check_for_cran()
 # Get the summary of your results
-results$cran_summary()
+results2$cran_summary()
 # Generate your cran-comments.md, then you copy-paste the output from the function above
 usethis::use_cran_comments()
 
 devtools::check_win_devel()
 usethis::use_news_md(open = rlang::is_interactive())
 
-
+devtools::release()
 

tests/testthat/test-F_Graphs.R

@@ -4,9 +4,7 @@ library(PLNmodels)
 library(parallel)
 library(tidygraph)
 library(ggraph)
-library(purrr)
 library(dplyr)
-library(tidyr)
 library(gridExtra)
 ##########################
 n<-30

tests/testthat/test-F_inference.R

@@ -6,8 +6,6 @@ library(dplyr)
 library(vegan)
 library(Matrix)
 library(mvtnorm)
-library(tidyr)
-library(EMtree)
 ##########################
 n<-30
 p<-10

tests/testthat/test-test_gener_data.R

@@ -6,7 +6,6 @@ library(dplyr)
 library(vegan)
 library(Matrix)
 library(mvtnorm)
-library(tidyr)
 library(EMtree)
 ##########################
 n<-100

vignettes/Fatala_Net.Rmd

@@ -208,6 +208,3 @@ forceNetwork(Links = graph_d3$links, Nodes =graph_d3$nodes,
              Source = 'source', Target = 'target', 
              NodeID = 'name', Group = 'group',opacity = 1,fontSize = 15, legend=TRUE)
 ```
-
-
-

vignettes/Partial_correlations.Rmd

@@ -0,0 +1,65 @@
+---
+title: "Partial correlations"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Partial correlations}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  cache=TRUE
+)
+library(ggplot2)
+library(dplyr)
+library(tidyr)
+library(magrittr)
+```
+
+```{r setup}
+library(EMtree)
+```
+
+# Partial correlations simulation
+
+```{r}
+set.seed(1)
+p=300
+n=400
+simulation<-data_from_scratch(type="erdos",p=p,n=n,signed=TRUE, dens=3/p)
+G=1*(simulation$omega!=0) ; diag(G) = 0
+draw_network(G, remove_isolated = TRUE, pal_edges = "gray70",pal_nodes = "steelblue",btw_rank = 1)$G
+```
+simulated partial correlation matrix :
+```{r}
+ParCor <- - cov2cor(simulation$omega)
+```
+
+```{r}
+ParCor %>% ToVec() %>% as_tibble() %>% filter(value!=0) %>% 
+  ggplot(aes(value))+geom_histogram(alpha=0.8)+
+  theme_light()+labs(title="Repartition of non-nul partial correlations",
+                     x="Partial correlation values")
+  
+```
+
+
+# Inference
+
+```{r}
+EMtreefit<-ResampleEMtree(simulation$data,S = 30,cores = 3)
+stab_selection=StATS(EMtreefit$Pmat, nlambda=50, stab.thresh=0.8,plot=TRUE)
+```
+
+```{r}
+net90=ToSym(1*(stab_selection$freqs_opt>0.95))
+table(net90, G)
+library(PRROC)
+pr<-pr.curve(scores.class0 = stab_selection$freqs_opt,
+             scores.class1 = ToVec(G))
+pr
+```
+