Package: lavacreg 0.2-3-9013

lavacreg: Latent Variable Count Regression Models

Estimation of a multi-group count regression models (i.e., Poisson, negative binomial) with latent covariates. This packages provides two extensions compared to ordinary count regression models based on a generalized linear model: First, measurement models for the predictors can be specified allowing to account for measurement error. Second, the count regression can be simultaneously estimated in multiple groups with stochastic group weights. The marginal maximum likelihood estimation is described in Kiefer & Mayer (2020) <doi:10.1080/00273171.2020.1751027>.

Authors:Christoph Kiefer [cre, aut]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
lavacreg/json (API)

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

Bug tracker:https://github.com/chkiefer/lavacreg/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • example01 - A first example dataset to illustrate the use of lavacreg

On CRAN:

Conda:

count-regressionlatent-covariatesstructural-equation-modelingopenblascppopenmp

3.48 score 3 stars 6 scripts 406 downloads 3 exports 12 dependencies

Last updated from:8cc04bddf8. Checks:14 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR141
linux-devel-x86_64ERROR169
source / vignettesERROR281
linux-release-arm64ERROR137
linux-release-x86_64ERROR206
macos-release-arm64ERROR122
macos-release-x86_64ERROR262
macos-oldrel-arm64ERROR144
macos-oldrel-x86_64ERROR390
windows-devel-arm64ERROR292
windows-devel-x86_64ERROR183
windows-release-arm64ERROR332
windows-release-x86_64ERROR151
windows-oldrel-x86_64ERROR88
wasm-releaseOK113

Exports:countregis_countsummary

Dependencies:data.tablefastGHQuadlavaanMASSmnormtnumDerivpbivnormpracmaquadprogRcppRcppArmadilloSparseGrid