Package: CALIBERrfimpute 1.0-7

Anoop Shah

CALIBERrfimpute: Multiple Imputation Using MICE and Random Forest

Functions to impute using random forest under full conditional specifications (multivariate imputation by chained equations). The methods are described in Shah and others (2014) <doi:10.1093/aje/kwt312>.

Authors:Anoop Shah [aut, cre], Jonathan Bartlett [ctb], Harry Hemingway [ths], Owen Nicholas [ths], Aroon Hingorani [ths]

CALIBERrfimpute_1.0-7.tar.gz
CALIBERrfimpute_1.0-7.tar.gz(r-4.5-noble)CALIBERrfimpute_1.0-7.tar.gz(r-4.4-noble)
CALIBERrfimpute_1.0-7.tgz(r-4.4-emscripten)CALIBERrfimpute_1.0-7.tgz(r-4.3-emscripten)
CALIBERrfimpute.pdf |CALIBERrfimpute.html✨
CALIBERrfimpute/json (API)
NEWS

# Install 'CALIBERrfimpute' in R:

install.packages('CALIBERrfimpute', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.60 score 2 stars 470 downloads 2 mentions 5 exports 65 dependencies

Last updated 2 years agofrom:e44534f37f. Checks:2 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Feb 28 2025
R-4.5-linux	OK	Feb 28 2025

Exports:makemar mice.impute.rfcat mice.impute.rfcont setRFoptions simdata

Dependencies:backports bit bit64 boot broom cli clipr codetools cpp11 crayon dplyr fansi forcats foreach generics glmnet glue haven hms iterators jomo lattice lifecycle lme4 magrittr MASS Matrix mice minqa mitml mvtnorm nlme nloptr nnet numDeriv ordinal pan pillar pkgconfig prettyunits progress purrr R6 randomForest rbibutils Rcpp RcppEigen Rdpack readr reformulas rlang rpart shape stringi stringr survival tibble tidyr tidyselect tzdb ucminf utf8 vctrs vroom withr

Comparison of parametric and Random Forest MICE in imputation of missing data in survival analysis

Rendered fromsimstudy_survival.Rnwusingutils::Sweaveon Feb 28 2025.

Last update: 2022-12-04
Started: 2014-02-07

Citation

Readme and manuals

Help Manual

Help page	Topics
Imputation in MICE using Random Forest	CALIBERrfimpute-package CALIBERrfimpute
Creates artificial missing at random missingness	makemar
Impute categorical variables using Random Forest within MICE	mice.impute.rfcat
Impute continuous variables using Random Forest within MICE	mice.impute.rfcont
Set Random Forest options for imputation using MICE	setRFoptions
Simulate multivariate data for testing	simdata