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:
Reverse dependencies:
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