lpmec: Measurement Error Analysis and Correction Under Identification
Restrictions
Implements methods for analyzing latent variable models with measurement
error correction, including Item Response Theory (IRT) models. Provides tools for
various correction methods such as Bayesian Markov Chain Monte Carlo (MCMC),
over-imputation, bootstrapping for robust standard errors, Ordinary Least Squares
(OLS), and Instrumental Variables (IV) based approaches. Supports flexible
specification of observable indicators and groupings for latent variable analyses
in social sciences and other fields. Methods are described in a working paper
(2025) <doi:10.48550/arXiv.2507.22218>.
| Version: |
1.1.4 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
reticulate, stats, sensemakr, pscl, AER, sandwich, mvtnorm, Amelia, emIRT, gtools |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: |
2026-02-09 |
| DOI: |
10.32614/CRAN.package.lpmec (may not be active yet) |
| Author: |
Connor Jerzak [aut, cre],
Stephen Jessee [aut] |
| Maintainer: |
Connor Jerzak <connor.jerzak at gmail.com> |
| BugReports: |
https://github.com/cjerzak/lpmec-software/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/cjerzak/lpmec-software |
| NeedsCompilation: |
no |
| SystemRequirements: |
Python (>= 3.10) with jax, numpy, numpyro
(optional; for NumPyro backend via reticulate) |
| Citation: |
lpmec citation info |
| Materials: |
NEWS |
| CRAN checks: |
lpmec results |
Documentation:
Downloads:
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