Package: eivtools 0.1-9

J.R. Lockwood

eivtools: Measurement Error Modeling Tools

This includes functions for analysis with error-prone covariates, including deconvolution, latent regression and errors-in-variables regression. It implements methods by Rabe-Hesketh et al. (2003) <doi:10.1191/1471082x03st056oa>, Lockwood and McCaffrey (2014) <doi:10.3102/1076998613509405>, and Lockwood and McCaffrey (2017) <doi:10.1007/s11336-017-9556-y>, among others.

Authors:J.R. Lockwood

eivtools_0.1-9.tar.gz
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eivtools.pdf |eivtools.html✨
eivtools/json (API)

# Install 'eivtools' in R:
install.packages('eivtools', repos = c('https://jrlockwood.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jrlockwood/eivtools/issues

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

4 exports 0.63 score 15 dependencies 17 scripts 177 downloads

Last updated 2 years agofrom:b8d8d62302. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 06 2024
R-4.5-winNOTESep 06 2024
R-4.5-linuxNOTESep 06 2024
R-4.4-winNOTESep 06 2024
R-4.4-macNOTESep 06 2024
R-4.3-winOKSep 06 2024
R-4.3-macOKSep 06 2024

Exports:deconv_npmleeivregget_bugs_wishart_scalematlr_ancova

Dependencies:abindbootclicodagluelatticelifecyclemagrittrR2jagsR2WinBUGSrjagsrlangstringistringrvctrs