Package: CDatanet 2.2.2

Aristide Houndetoungan

CDatanet: Econometrics of Network Data

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>).

Authors:Aristide Houndetoungan [cre, aut]

CDatanet_2.2.2.tar.gz
CDatanet_2.2.2.tar.gz(r-4.7-arm64)CDatanet_2.2.2.tar.gz(r-4.7-x86_64)CDatanet_2.2.2.tar.gz(r-4.6-arm64)CDatanet_2.2.2.tar.gz(r-4.6-x86_64)
CDatanet_2.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
CDatanet/json (API)
NEWS

# Install 'CDatanet' in R:
install.packages('CDatanet', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ahoundetoungan/cdatanet/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library

On CRAN:

Conda:

openblascppopenmp

1.20 score 16 scripts 209 downloads 17 exports 19 dependencies

Last updated from:bdf0e95d16. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK246
linux-devel-x86_64OK299
source / vignettesOK314
linux-release-arm64OK252
linux-release-x86_64OK247
wasm-releaseOK190

Exports:cdnethomophili.datahomophily.fehomophily.remat.to.vecmeffectsnorm.networkpeer.avgremove.idssarsartsimcdEysimcdnetsimnetworksimsarsimsartvec.to.mat

Dependencies:codetoolsdigestdoParalleldoRNGforeachFormulaformula.toolsiteratorslatticeMatrixmatrixcalcoperator.toolsRcppRcppArmadilloRcppDistRcppEigenRcppNumericalRcppProgressrngtools

Readme and manuals

Help Manual

Help pageTopics
The CDatanet PackageCDatanet-package CDatanet
Estimating Count Data Models with Social Interactions under Rational Expectations Using the NPL Methodcdnet
Converting Data between Directed Network Models and Symmetric Network Models.homophili.data
Estimating Network Formation Models with Degree Heterogeneity: the Fixed Effect Approachhomophily.fe
Estimating Network Formation Models with Degree Heterogeneity: the Bayesian Random Effect Approachhomophily.re
Marginal Effects for Count Data Models and Tobit Models with Social Interactionsmeffects meffects.cdnet meffects.sart meffects.summary.cdnet meffects.summary.sart
Creating Objects for Network Modelsmat.to.vec norm.network vec.to.mat
Computing Peer Averagespeer.avg
Printing the Average Expected Outcomes for Count Data Models with Social Interactionsprint.simcdEy print.summary.simcdEy summary.simcdEy
Removing Identifiers with NA from Adjacency Matrices Optimallyremove.ids
Estimating Linear-in-mean Models with Social Interactionssar
Estimating Tobit Models with Social Interactionssart
Counterfactual Analyses with Count Data Models and Social InteractionssimcdEy
Simulating Count Data Models with Social Interactions Under Rational Expectationssimcdnet
Simulating Network Datasimnetwork
Simulating Data from Linear-in-Mean Models with Social Interactionssimsar
Simulating Data from Tobit Models with Social Interactionssimsart
Summary for the Estimation of Count Data Models with Social Interactions under Rational Expectationsprint.cdnet print.summary.cdnet summary.cdnet
Summary for the Estimation of Linear-in-mean Models with Social Interactionsprint.sar print.summary.sar summary.sar
Summary for the Estimation of Tobit Models with Social Interactionsprint.sart print.summary.sart summary.sart