https://github.com/chkiefer/lavacreg

lavacreg (Latent Variable Count Regression Models)) is an R package to estimate multigroup structural equation models with count regressions (Poisson or negative binomial).
https://github.com/chkiefer/lavacreg

count-regression latent-covariates structural-equation-modeling

Last synced: 3 months ago
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lavacreg (Latent Variable Count Regression Models)) is an R package to estimate multigroup structural equation models with count regressions (Poisson or negative binomial).

• Host: GitHub
• URL: https://github.com/chkiefer/lavacreg
• Owner: chkiefer
• Created: 2020-07-09T07:26:06.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2024-06-13T07:29:19.000Z (5 months ago)
• Last Synced: 2024-07-16T16:01:42.920Z (4 months ago)
• Topics: count-regression, latent-covariates, structural-equation-modeling
• Language: R
• Homepage: https://chkiefer.github.io/lavacreg/
• Size: 9.21 MB
• Stars: 3
• Watchers: 2
• Forks: 2
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
# lavacreg: Latent Variable Count Regression Models

[![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip)

lavacreg is an R package for fitting count regression models (i.e., Poisson, negative binomial) with manifest as well as latent covariates and within multiple groups. It can be installed via GitHub.

## Installation

`lavacreg` is currently on CRAN. The development version of `lavacreg` can be installed directly from this GitHub repository using the additional package `devtools`. Under Windows, please make sure Rtools (http://cran.r-project.org/bin/windows/Rtools) are installed and no older version of `lavacreg` is currently loaded:

```

install.packages("devtools")

library(devtools)

install_github("chkiefer/lavacreg")

```

## Run lavacreg

The main function of the package is `countreg()`. There is an article available here on GitHub (https://chkiefer.github.io/lavacreg/articles/intro.html) to introduce you to its functionality.