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Commit8027574

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version 2.2.2
1 parentb34b44e commit8027574

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9 files changed

+41
-54
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9 files changed

+41
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‎.github/workflows/R-CMD-check.yml‎

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@@ -34,35 +34,35 @@ jobs:
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steps:
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-uses:actions/checkout@v3
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# ----------------------- Set up R -----------------------
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-name:Set up R
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uses:r-lib/actions/setup-r@v2
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with:
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r-version:${{ matrix.r }}
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http-user-agent:${{ matrix.http-user-agent }}
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# ----------------- TinyTeX setup for macOS & Linux -----------------
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-name:Install TinyTeX (macOS & Linux)
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if:runner.os != 'Windows'
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run:|
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Rscript -e "install.packages('tinytex')"
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Rscript -e "tinytex::install_tinytex(force = TRUE)"
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Rscript -e "tinytex::tlmgr_install(c('xcolor','framed','inconsolata'))"
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# ----------------- TinyTeX setup for Windows -----------------
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-name:Install TinyTeX (Windows)
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if:runner.os == 'Windows'
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# ----------------------- TinyTeX setup -----------------------
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-name:Install TinyTeX
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run:|
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Rscript -e "install.packages('tinytex')"
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Rscript -e "tinytex::install_tinytex(force = TRUE)"
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Rscript -e "tinytex::tlmgr_install(c('latex-bin','pdftex','babel-english','xcolor','framed','inconsolata'))"
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echo "C:/Users/runneradmin/AppData/Roaming/TinyTeX/bin/windows" >> $env:GITHUB_PATH
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# ----------------- Check pdflatex version (Windows) -----------------
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-name:Check pdflatex version
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-name:Set TinyTeX PATH (Linux/macOS)
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if:runner.os != 'Windows'
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run:|
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export PATH="$HOME/bin/TinyTeX/bin:$PATH"
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echo "$HOME/bin/TinyTeX/bin" >> $GITHUB_PATH
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-name:Set TinyTeX PATH (Windows)
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if:runner.os == 'Windows'
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run:pdflatex --version
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# ----------------- Install tidy -----------------
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shell:pwsh
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run:|
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$texPath = "C:/Users/runneradmin/AppData/Roaming/TinyTeX/bin/windows"
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$env:PATH = "$texPath;$env:PATH"
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Get-Command pdflatex
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# ------------------------ Install tidy ------------------------
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-name:Install tidy (macOS only)
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if:runner.os == 'macOS'
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run:brew install tidy-html5
@@ -71,7 +71,7 @@ jobs:
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if:runner.os == 'Linux'
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run:sudo apt-get update && sudo apt-get install -y tidy
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# ----------------- Install R dependencies -----------------
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# ------------------- Install R dependencies-------------------
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-name:Install R dependencies
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uses:r-lib/actions/setup-r-dependencies@v2
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with:
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any::tinytex
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# ----------------- Run R CMD check -----------------
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-name:Run R CMD check (Linux & macOS)
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if:runner.os != 'Windows'
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uses:r-lib/actions/check-r-package@v2
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with:
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args:'c("--as-cran","--no-manual")'
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build_args:'c("--no-manual","--compact-vignettes=gs+qpdf")'
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upload-snapshots:true
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-name:Run R CMD check (Windows - skip manual)
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if:runner.os == 'Windows'
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-name:Run R CMD check
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uses:r-lib/actions/check-r-package@v2
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with:
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args:'c("--as-cran","--no-manual")'

‎DESCRIPTION‎

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@@ -2,17 +2,17 @@ Package: CDatanet
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Type: Package
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Title: Econometrics of Network Data
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Version: 2.2.2
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Date: 2025-10-13
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Date: 2025-12-01
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Authors@R: person("Aristide", "Houndetoungan", role = c("cre","aut"), email = "ahoundetoungan@ecn.ulaval.ca")
7-
Description: Simulating and estimating peer effect models and network formation models. The class of peer effect models includes linear-in-means models (Lee, 2004; <doi:10.1111/j.1468-0262.2004.00558.x>), Tobit models (Xu and Lee, 2015; <doi:10.1016/j.jeconom.2015.05.004>), and discrete numerical data models (Houndetoungan,2024; <doi:10.2139/ssrn.3721250>). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; <doi:10.3982/ECTA12679>) and exponential random graph models (Mele, 2017; <doi:10.3982/ECTA10400>).
7+
Description: Simulating and estimating peer effect models and network formation models. The class of peer effect models includes linear-in-means models (Lee, 2004; <doi:10.1111/j.1468-0262.2004.00558.x>), Tobit models (Xu and Lee, 2015; <doi:10.1016/j.jeconom.2015.05.004>), and discrete numerical data models (Houndetoungan,2025; <doi:10.48550/arXiv.2405.17290>). The network formation models include pair-wise regressions with degree heterogeneity (Graham, 2017; <doi:10.3982/ECTA12679>) and exponential random graph models (Mele, 2017; <doi:10.3982/ECTA10400>).
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License: GPL-3
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Language: en-US
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Encoding: UTF-8
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BugReports: https://github.com/ahoundetoungan/CDatanet/issues
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URL: https://github.com/ahoundetoungan/CDatanet
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LazyData: true
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Depends: R (>= 3.5.0)
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Imports: Rcpp (>= 1.0.0), Formula, formula.tools,ddpcr,Matrix, matrixcalc, foreach, doRNG, doParallel, parallel
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Imports: Rcpp (>= 1.0.0), Formula, formula.tools, Matrix, matrixcalc, foreach, doRNG, doParallel, parallel
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LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppDist, RcppNumerical, RcppEigen
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RoxygenNote: 7.3.2
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Roxygen: list(markdown = TRUE)

‎NAMESPACE‎

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@@ -38,7 +38,6 @@ export(vec.to.mat)
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importFrom(Formula,as.Formula)
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importFrom(Matrix,rankMatrix)
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importFrom(Rcpp,sourceCpp)
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importFrom(ddpcr,quiet)
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importFrom(doParallel,registerDoParallel)
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importFrom(doRNG,"%dorng%")
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importFrom(foreach,"%dopar%")

‎R/CDatanet.R‎

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@@ -7,7 +7,7 @@
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#' @references
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#' Eddelbuettel, D., & Francois, R. (2011). \pkg{Rcpp}: Seamless \R and \code{C++} integration. \emph{Journal of Statistical Software}, 40(8), 1-18, \doi{10.18637/jss.v040.i08}.
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#' @references
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#' Houndetoungan, E. A. (2024). Count Data Models with Heterogeneous Peer Effects. Available atSSRN 3721250, \doi{10.2139/ssrn.3721250}.
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#' Houndetoungan, E. A. (2025). Count Data Models with Heterogeneous Peer Effects. Available atarXiv:2405.17290, \doi{10.48550/arXiv.2405.17290}.
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#' @references
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#' Lee, L. F. (2004). Asymptotic distributions of quasi-maximum likelihood estimators for spatial autoregressive models. \emph{Econometrica}, 72(6), 1899-1925, \doi{10.1111/j.1468-0262.2004.00558.x}.
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#' @references

‎R/auxfunc.R‎

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@@ -302,7 +302,6 @@ fcoefficients <- function(coef, std) {
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#' @importFrom stats delete.response
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#' @importFrom stats as.formula
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#' @importFrom Matrix rankMatrix
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#' @importFrom ddpcr quiet
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formula.to.data<-function(formula,
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contextual,
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Glist,

‎R/homophily.fe.R‎

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@@ -134,7 +134,7 @@ homophily.fe <- function(network,
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}
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N<- sum(Nvec)
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137-
quiet(gc())
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invisible(gc())
138138
if (sum(!((network==0)| (network==1)))!=0) {
139139
stop("Network should contain only 0 and 1.")
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}
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index<- cbind(tmp2,tmp1)
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rm(list= c("tmp1","tmp2"))
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quiet(gc())
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invisible(gc())
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indexgr<-matrix(c(cumsum(c(0,nvec[-M])), cumsum(nvec)-1),ncol=2)#start group, end group
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# INDEXgr <- matrix(c(cumsum(c(0, Nvec[-M])), cumsum(Nvec) - 1), ncol = 2)
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# Formula to data
@@ -159,13 +159,13 @@ homophily.fe <- function(network,
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type="network",theta0=NA)
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if(!missing(data)) {
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rm("data")
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quiet(gc())
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invisible(gc())
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}
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formula<-f.t.data$formula
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dX<-f.t.data$X
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if(nrow(dX)!=N&& nrow(dX)!=0) stop("The number of observations in X does not match the network.")
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rm("f.t.data")
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quiet(gc())
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invisible(gc())
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hasX<-TRUE
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}
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coln<- colnames(dX)
@@ -233,7 +233,7 @@ homophily.LogitFE <- function(network, fe.way, M, nvec, n, N, Nvec, index, index
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234234
#starting value
235235
initllh<-NULL
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quiet(gc())
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invisible(gc())
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if(is.null(init)){
238238
if(print) cat("starting point searching\n")
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beta<-NULL
@@ -299,12 +299,12 @@ homophily.LogitFE <- function(network, fe.way, M, nvec, n, N, Nvec, index, index
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}
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}
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}
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quiet(gc())
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invisible(gc())
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theta<-1*init
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estim<-NULL
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quiet(gc())
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invisible(gc())
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309309
if(print) {
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cat("maximizer searching\n")
@@ -421,7 +421,7 @@ homophily.LogitFESym <- function(network, M, nvec, n, N, Nvec, index, indexgr, f
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#starting value
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initllh<-NULL
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quiet(gc())
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invisible(gc())
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if(is.null(init)){
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if(print) cat("starting point searching\n")
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beta<-NULL
@@ -465,13 +465,12 @@ homophily.LogitFESym <- function(network, M, nvec, n, N, Nvec, index, indexgr, f
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stopifnot(length(init)== (K+n))
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}
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}
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quiet(gc())
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invisible(gc())
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theta<-1*init
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estim<-NULL
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quiet(gc())
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invisible(gc())
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if(print) {
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cat("maximizer searching\n")

‎R/homophily.re.R‎

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@@ -30,7 +30,6 @@
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#' For one-way random effect models (`re.way = 1`), \eqn{\nu_j = \mu_j}. For symmetric models, the network is not directed and the
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#' random effects need to be one way.
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#' @seealso \code{\link{homophily.fe}}.
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#' @importFrom ddpcr quiet
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#' @importFrom stats lm
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#' @importFrom stats var
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#' @importFrom stats cov
@@ -150,7 +149,7 @@ homophily.re <- function(network,
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}
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N<- sum(Nvec)
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quiet(gc())
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invisible(gc())
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if (sum(!((network==0)| (network==1)))!=0) {
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stop("Network should contain only 0 and 1.")
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}
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tmp2<- c(0,tmp1[-n]+1)
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index<- cbind(tmp2,tmp1)
166165
rm(list= c("tmp1","tmp2"))
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quiet(gc())
166+
invisible(gc())
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169168
indexgr<-matrix(c(cumsum(c(0,nvec[-M])), cumsum(nvec)-1),ncol=2)
170169
INDEXgr<-matrix(c(cumsum(c(0,Nvec[-M])), cumsum(Nvec)-1),ncol=2)
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173172
type="network",theta0=NA)
174173
if(!missing(data)) {
175174
rm("data")
176-
quiet(gc())
175+
invisible(gc())
177176
}
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179178
formula<-f.t.data$formula
180179
dX<-f.t.data$X
181180
if(nrow(dX)!=N) stop("The number of observations in X does not match the network.")
182181
rm("f.t.data")
183-
quiet(gc())
182+
invisible(gc())
184183
coln<- colnames(dX)
185184
nfix<- ifelse("(Intercept)"%in%coln,1,0)
186185
K<- ncol(dX)
@@ -204,12 +203,12 @@ homophily.re <- function(network,
204203
dXdX<- rbind(cbind(diag(Nvec), t(sumdX)), cbind(sumdX,dXdX))
205204
invdXdX<- solve(as.matrix(dXdX))
206205
rm("sumdX")
207-
quiet(gc())
206+
invisible(gc())
208207
}else{
209208
invdXdX<- solve(as.matrix(dXdX))
210209
}
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rm("dXdX")
212-
quiet(gc())
211+
invisible(gc())
213212

214213
#starting value
215214
beta<-init$beta
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219218
snu2<-init$snu2
220219
rho<-init$rho
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quiet(gc())
221+
invisible(gc())
223222
if (is.null(beta)) {
224223
# print(dim(invdXdX))
225224
# print(length(sumnetwork))

‎archives/CDatanet_2.2.2.tar.gz‎

11.9 KB
Binary file not shown.

‎man/CDatanet-package.Rd‎

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