Package: LAM 0.7-22
Alexander Robitzsch
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>).
Authors:
LAM_0.7-22.tar.gz
LAM_0.7-22.tar.gz(r-4.5-noble)LAM_0.7-22.tar.gz(r-4.4-noble)
LAM_0.7-22.tgz(r-4.4-emscripten)LAM_0.7-22.tgz(r-4.3-emscripten)
LAM.pdf |LAM.html✨
LAM/json (API)
NEWS
| # Install 'LAM' in R: |
| install.packages('LAM', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/alexanderrobitzsch/lam/issues
- data.HT12 - Datasets from Heck and Thomas
Last updated 6 months agofrom:a4381a6051. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 13 2024 |
| R-4.5-linux-x86_64 | OK | Dec 13 2024 |
Exports:amhclpm_to_ctmloglike_mvnormloglike_mvnorm_NA_patternmlnormalpmlesuff_stat_NA_pattern
Dependencies:admiscCDMlatticeMatrixmvtnormpbapplypbvpolycorRcppRcppArmadillosirtTAM
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Some Latent Variable Models | LAM-package LAM |
| Bayesian Model Estimation with Adaptive Metropolis Hastings Sampling ('amh') or Penalized Maximum Likelihood Estimation ('pmle') | amh coef.amh coef.pmle confint.amh confint.pmle logLik.amh logLik.pmle plot.amh plot.pmle pmle summary.amh summary.pmle vcov.amh vcov.pmle |
| Transformation of Path Coefficients of Cross-Lagged Panel Model | clpm_to_ctm |
| Datasets from Heck and Thomas (2015) | data.HT data.HT12 |
| Log-Likelihood Value of a Multivariate Normal Distribution | loglike_mvnorm loglike_mvnorm_NA_pattern |
| (Restricted) Maximum Likelihood Estimation with Prior Distributions and Penalty Functions under Multivariate Normality | coef.mlnormal confint.mlnormal logLik.mlnormal mlnormal print.mlnormal summary.mlnormal vcov.mlnormal |
| Sufficient Statistics for Dataset with Missing Response Pattern | suff_stat_NA_pattern |
