refitME: Measurement Error Modelling using MCEM

Fits measurement error models using Monte Carlo Expectation Maximization (MCEM). For specific details on the methodology, see: Greg C. G. Wei & Martin A. Tanner (1990) A Monte Carlo Implementation of the EM Algorithm and the Poor Man's Data Augmentation Algorithms, Journal of the American Statistical Association, 85:411, 699-704 <doi:10.1080/01621459.1990.10474930> For more examples on measurement error modelling using MCEM, see the 'RMarkdown' vignette: "'refitME' R-package tutorial".

Version: 1.2.2
Depends: R (≥ 4.1.0)
Imports: MASS, SemiPar, mgcv, VGAM, VGAMdata, caret, expm, mvtnorm, sandwich, stats, dplyr, scales
Published: 2021-08-03
Author: Jakub Stoklosa ORCID iD [aut, cre], Wenhan Hwang [aut, ctb], David Warton [aut, ctb]
Maintainer: Jakub Stoklosa <j.stoklosa at unsw.edu.au>
License: GPL-2
NeedsCompilation: no
Materials: README
CRAN checks: refitME results

Documentation:

Reference manual: refitME.pdf

Downloads:

Package source: refitME_1.2.2.tar.gz
Windows binaries: r-devel: refitME_1.2.2.zip, r-release: refitME_1.2.2.zip, r-oldrel: refitME_1.2.0.zip
macOS binaries: r-release (arm64): refitME_1.2.2.tgz, r-release (x86_64): refitME_1.2.2.tgz, r-oldrel: refitME_1.2.0.tgz
Old sources: refitME archive

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