Package: mlmi 1.1.2

mlmi: Maximum Likelihood Multiple Imputation

Implements so called Maximum Likelihood Multiple Imputation as described by von Hippel and Bartlett (2021) <doi:10.1214/20-STS793>. A number of different imputations are available, by utilising the 'norm', 'cat' and 'mix' packages. Inferences can be performed either using combination rules similar to Rubin's or using a likelihood score based approach based on theory by Wang and Robins (1998) <doi:10.1093/biomet/85.4.935>.

Authors:Jonathan Bartlett

mlmi_1.1.2.tar.gz
mlmi_1.1.2.zip(r-4.5)mlmi_1.1.2.zip(r-4.4)mlmi_1.1.2.zip(r-4.3)
mlmi_1.1.2.tgz(r-4.4-any)mlmi_1.1.2.tgz(r-4.3-any)
mlmi_1.1.2.tar.gz(r-4.5-noble)mlmi_1.1.2.tar.gz(r-4.4-noble)
mlmi_1.1.2.tgz(r-4.4-emscripten)mlmi_1.1.2.tgz(r-4.3-emscripten)
mlmi.pdf |mlmi.html
mlmi/json (API)
NEWS

# Install 'mlmi' in R:
install.packages('mlmi', repos = c('https://jwb133.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/jwb133/mlmi/issues

Datasets:
  • ctsTrialWide - Simulated example data with continuous outcome measured repeatedly over time

On CRAN:

7 exports 0.53 score 8 dependencies 8 scripts 312 downloads

Last updated 1 years agofrom:e04bde8130. Checks:OK: 3 NOTE: 4. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 26 2024
R-4.5-winNOTEAug 26 2024
R-4.5-linuxNOTEAug 26 2024
R-4.4-winNOTEAug 26 2024
R-4.4-macNOTEAug 26 2024
R-4.3-winOKAug 26 2024
R-4.3-macOKAug 26 2024

Exports:catImpmixImpnormImpnormUniImprefBasedCtsscoreBasedwithinBetween

Dependencies:catgsllatticeMASSMatrixmixnlmenorm