CGMissingDataR is an R package based on the CGMissingData Python library for evaluating model performance under feature missingness by:
- injecting missing values into feature columns at specified masking rates,
- imputing missing values using a Multiple Imputation by Chained Equations (MICE)-style iterative imputer, and
- training Random Forest and k-Nearest Neighbors regressors to report Mean ABsolute Percentage Error (MAPE) and R across missingness levels.
Before the installation, ensure that you have the following R packages installed:
install.packages(c("FNN", "ranger", "mice"))Install the development version of CGMissingDataR from GitHub:
devtools::install_github("saraswatsh/CGMissingDataR")Vignette
A brief vignette illustrating the usage of CGMissingDataR can be found here.
Changelog
The changelog is available here.