LAM: Some Latent Variable Models

Includes some procedures for latent variable modeling with a particular focus on multilevel data. The 'LAM' package contains mean and covariance structure modelling for multivariate normally distributed data (mlnormal(); Longford, 1987; <doi:10.1093/biomet/74.4.817>), a general Metropolis-Hastings algorithm (amh(); Roberts & Rosenthal, 2001, <doi:10.1214/ss/1015346320>) and penalized maximum likelihood estimation (pmle(); Cole, Chu & Greenland, 2014; <doi:10.1093/aje/kwt245>).

Version: 0.3-48
Depends: R (≥ 3.1)
Imports: CDM, coda, graphics, MASS, numDeriv, Rcpp, sirt (≥ 2.0), stats, TAM (≥ 2.8), utils
LinkingTo: Rcpp, RcppArmadillo
Suggests: lavaan, lme4, STARTS (≥ 0.2)
Published: 2018-06-07
Author: Alexander Robitzsch [aut,cre]
Maintainer: Alexander Robitzsch <robitzsch at ipn.uni-kiel.de>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/alexanderrobitzsch/LAM
NeedsCompilation: yes
Citation: LAM citation info
Materials: README NEWS
CRAN checks: LAM results

Downloads:

Reference manual: LAM.pdf
Package source: LAM_0.3-48.tar.gz
Windows binaries: r-devel: LAM_0.3-48.zip, r-release: LAM_0.3-48.zip, r-oldrel: LAM_0.3-48.zip
OS X binaries: r-release: LAM_0.3-48.tgz, r-oldrel: LAM_0.3-48.tgz
Old sources: LAM archive

Reverse dependencies:

Reverse imports: STARTS

Linking:

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