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refitME

Monte Carlo Expectation Maximization - A measurement error modelling wrapper function for lm, glm and gam model objects.

An R-package for methods developed in:

Stoklosa, J., Hwang, W-H., and Warton, D.I. refitME: Measurement Error Modelling using Monte Carlo Expectation Maximization in R.

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Version

Install

install.packages('refitME')

Monthly Downloads

163

Version

1.3.1

License

GPL-2

Maintainer

Jakub Stoklosa

Last Published

April 13th, 2025

Functions in refitME (1.3.1)

MCEMfit_CR

Function for fitting VGAM capture-recapture (CR) model using the MCEM algorithm
Corymbiaeximiadata

The Corymbia eximia presence-only data set
MCEMfit_gen

Function for fitting any likelihood-based model using the MCEM algorithm
refitME

A wrapper function for correcting measurement error in predictor variables via the MCEM algorithm
MCEMfit_gam

Function for wrapping the MCEM algorithm on gam objects
sqrt.na

Function that replaces NA with zero for a matrix
MCEMfit_glm

Function for wrapping the MCEM algorithm on lm or glm objects
Milanmortdata

The Milan mortality data set
Framinghamdata

The Framingham heart study data set
wt.var

Function that calculates a weighted variance
Priniadata

The yellow-bellied Prinia Prinia flaviventris capture-recapture data
logLik_MCEMfit_lm

Extract log-Likelihoods for MCEMfit_lm model objects
logLik.refitME

Extract log-Likelihoods for refitME model objects
anova.refitME

An ANOVA function for fitted refitME objects
anova_MCEMfit_glm

An ANOVA function for fitted MCEMfit_glm objects