Setup and recommendations | Spark and sparklyr | Hadoop | Installation | Example Usage
Package: bigMICE 1.0.0
bigMICE: Multiple Imputation of Big Data
A computational toolbox designed for handling missing values in large datasets with the Multiple Imputation by Chained Equations (MICE) by using 'Apache Spark'. The methodology is described in Morvan et al. (2026) <doi:10.48550/arXiv.2601.21613>.
Authors:
bigMICE_1.0.0.tar.gz
bigMICE_1.0.0.zip(r-4.7-any)bigMICE_1.0.0.zip(r-4.6-any)bigMICE_1.0.0.zip(r-4.5-any)
bigMICE_1.0.0.tgz(r-4.6-any)bigMICE_1.0.0.tgz(r-4.5-any)
bigMICE_1.0.0.tar.gz(r-4.7-any)bigMICE_1.0.0.tar.gz(r-4.6-any)
bigMICE_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
bigMICE/json (API)
| # Install 'bigMICE' in R: |
| install.packages('bigMICE', repos = c('https://osysoev.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:55bcbb5dbb. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 140 | ||
| source / vignettes | OK | 172 | ||
| linux-release | OK | 133 | ||
| macos-release | OK | 123 | ||
| macos-oldrel | OK | 138 | ||
| windows-devel | OK | 110 | ||
| windows-release | OK | 99 | ||
| windows-oldrel | OK | 113 | ||
| wasm-release | OK | 112 |
Exports:impute_with_linear_regressionimpute_with_logistic_regressionimpute_with_MeMoMeimpute_with_mult_logistic_regressionimpute_with_random_forest_classifierimpute_with_random_forest_regressorimpute_with_random_samplesinit_with_random_samplesmice.sparkmice.spark.plussampler.spark
Dependencies:askpassblobclicodetoolsconfigcpp11curldata.tableDBIdbplyrdplyrgenericsglobalsgluehttrjsonlitelatticelifecyclemagrittrMatrixmimeopensslpillarpkgconfigpurrrR6rlangrstudioapisparklyrstringistringrsystibbletidyrtidyselectutf8uuidvctrswithrxml2yaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Linear Regression Imputation function | impute_with_linear_regression |
| Logistic Regression Imputation function | impute_with_logistic_regression |
| Mean/Mode/Median Imputation function | impute_with_MeMoMe |
| Multinomial Logistic Regression Imputation function | impute_with_mult_logistic_regression |
| Random Forest Classification Imputation function | impute_with_random_forest_classifier |
| Random Forest Regression Imputation function | impute_with_random_forest_regressor |
| Random Sample Imputation function | impute_with_random_samples |
| Random Sample Imputation function | init_with_random_samples |
| MICE for Spark DataFrames using Sparklyr and Spark MLlib | mice.spark |
| MICE+ for Spark DataFrames using Sparklyr and Spark MLlib | mice.spark.plus |
| Print Method for Multiple Imputation Results | print.mi_results |
| MICE sampler function | sampler.spark |
| Summary Method for Multiple Imputation Results | summary.mi_results |