Package: superMICE 1.1.1

superMICE: SuperLearner Method for MICE

Adds a Super Learner ensemble model method (using the 'SuperLearner' package) to the 'mice' package. Laqueur, H. S., Shev, A. B., Kagawa, R. M. C. (2021) <doi:10.1093/aje/kwab271>.

Authors:Aaron B. Shev

superMICE_1.1.1.tar.gz
superMICE_1.1.1.zip(r-4.5)superMICE_1.1.1.zip(r-4.4)superMICE_1.1.1.zip(r-4.3)
superMICE_1.1.1.tgz(r-4.4-any)superMICE_1.1.1.tgz(r-4.3-any)
superMICE_1.1.1.tar.gz(r-4.5-noble)superMICE_1.1.1.tar.gz(r-4.4-noble)
superMICE_1.1.1.tgz(r-4.4-emscripten)superMICE_1.1.1.tgz(r-4.3-emscripten)
superMICE.pdf |superMICE.html✨
superMICE/json (API)

# Install 'superMICE' in R:

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

Bug tracker:https://github.com/abshev/supermice/issues

On CRAN:

3.00 score 2 stars 150 downloads 1 exports 71 dependencies

Last updated 3 years agofrom:56451a8fd3. Checks:OK: 1 NOTE: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 05 2024
R-4.5-win	NOTE	Nov 05 2024
R-4.5-linux	NOTE	Nov 05 2024
R-4.4-win	NOTE	Nov 05 2024
R-4.4-mac	NOTE	Nov 05 2024
R-4.3-win	NOTE	Nov 05 2024
R-4.3-mac	NOTE	Nov 05 2024

Exports:mice.impute.SuperLearner

Dependencies:backports bit bit64 bitops boot broom caTools cli clipr codetools cpp11 crayon cvAUC data.table dplyr fansi forcats foreach gam generics glmnet glue gplots gtools haven hms iterators jomo KernSmooth lattice lifecycle lme4 magrittr MASS Matrix mice minqa mitml nlme nloptr nnet nnls numDeriv ordinal pan pillar pkgconfig prettyunits progress purrr R6 Rcpp RcppEigen readr rlang ROCR rpart shape stringi stringr SuperLearner survival tibble tidyr tidyselect tzdb ucminf utf8 vctrs vroom withr

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Function to generate imputations using SuperLearner for data with a binary outcome.	binarySuperLearner
Function to generate imputations using SuperLearner for data with a continuous outcome	continuousSuperLearner
Kernel functions used for local imputation	gaussianKernel
Jackknife method for selection bandwidth	jackknifeBandwidthSelection
Computes jackknife variance	jackknifeVariance
Function to generate imputations using non-parametric and semi-parametric local imputation methods.	localImputation
SuperLearner method for 'mice' package.	mice.impute.SuperLearner