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) .


Reference manual

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install.packages("lpmec")

1.1.4 by Connor Jerzak, 8 months ago


https://github.com/cjerzak/lpmec-software


Report a bug at https://github.com/cjerzak/lpmec-software/issues


Browse source code at https://github.com/cran/lpmec


Authors: Connor Jerzak [aut, cre] , Stephen Jessee [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports reticulate, stats, sensemakr, pscl, AER, sandwich, mvtnorm, Amelia, emIRT, gtools

Suggests testthat, knitr, rmarkdown

System requirements: Python (>= 3.10) with jax, numpy, numpyro (optional; for NumPyro backend via reticulate)


See at CRAN