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Fix for incompatibility with PLNmodels latest release
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‎.Rprofile

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

‎.gitignore

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.Ruserdata
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inst/doc
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docs
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dev_history.R

‎DESCRIPTION

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

‎R/F_inference.R

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

‎README.Rmd

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

‎README.md

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

‎cran-comments.md

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

‎dev_history.R

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

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‎tests/.DS_Store

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‎tests/testthat/test-F_Graphs.R

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library(parallel)
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library(tidygraph)
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library(ggraph)
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library(purrr)
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library(dplyr)
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library(tidyr)
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library(gridExtra)
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##########################
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n<-30

‎tests/testthat/test-F_inference.R

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library(vegan)
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library(Matrix)
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library(mvtnorm)
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library(tidyr)
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library(EMtree)
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##########################
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n<-30
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p<-10

‎tests/testthat/test-test_gener_data.R

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library(vegan)
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library(Matrix)
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library(mvtnorm)
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library(tidyr)
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library(EMtree)
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##########################
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n<-100

‎vignettes/.DS_Store

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‎vignettes/Fatala_Net.Rmd

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Source = 'source', Target = 'target',
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NodeID = 'name', Group = 'group',opacity = 1,fontSize = 15, legend=TRUE)
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```
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‎vignettes/Partial_correlations.Rmd

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