mvnimpute: Simultaneously Impute the Missing and Censored Values

Implementing a multiple imputation algorithm for multivariate data with missing and censored values under a coarsening at random assumption (Heitjan and Rubin, 1991<doi:10.1214/aos/1176348396>). The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987)<doi:10.1080/01621459.1987.10478458>. The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data.

Version: 1.0.0
Depends: R (≥ 3.4.0)
Imports: ggplot2, reshape2, LaplacesDemon, rlang, Rcpp, MASS, truncnorm
LinkingTo: Rcpp, RcppArmadillo, RcppDist
Suggests: knitr, rmarkdown, mice, clusterGeneration
Published: 2022-06-23
Author: Hesen Li
Maintainer: Hesen Li <li.hesen.21 at gmail.com>
BugReports: https://github.com/hli226/mvnimpute/issues
License: GPL-2 | GPL-3
URL: https://github.com/hli226/mvnimpute
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: mvnimpute results

Documentation:

Reference manual: mvnimpute.pdf
Vignettes: Vignette_1_simulated_data

Downloads:

Package source: mvnimpute_1.0.0.tar.gz
Windows binaries: r-devel: mvnimpute_1.0.0.zip, r-release: mvnimpute_1.0.0.zip, r-oldrel: mvnimpute_1.0.0.zip
macOS binaries: r-release (arm64): not available, r-oldrel (arm64): not available, r-release (x86_64): mvnimpute_1.0.0.tgz, r-oldrel (x86_64): mvnimpute_1.0.0.tgz

Linking:

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