Package: lavacreg 0.2-3-9011

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

3.18 score 3 stars 6 scripts 474 downloads 3 exports 12 dependencies

Last updated from:920c9605db. Checks:14 ERROR, 1 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64ERROR107
linux-devel-x86_64ERROR158
source / vignettesERROR212
linux-release-arm64ERROR109
linux-release-x86_64ERROR110
macos-release-arm64ERROR160
macos-release-x86_64ERROR188
macos-oldrel-arm64ERROR185
macos-oldrel-x86_64ERROR196
windows-devel-arm64ERROR324
windows-devel-x86_64ERROR93
windows-release-arm64ERROR313
windows-release-x86_64ERROR97
windows-oldrel-x86_64ERROR90
wasm-releaseOK105

Exports:countregis_countsummary

Dependencies:data.tablefastGHQuadlavaanMASSmnormtnumDerivpbivnormpracmaquadprogRcppRcppArmadilloSparseGrid