MultiDimensional Feature Selection
Run end-to-end MDFS
Plot MDFS details
Find indices of relevant variables from MDFS
Find indices of relevant variables
Get the recommended range for multiple discretisations
Call omp_set_num_threads
Add contrast variables to data
as.data.frame S3 method implementation for MDFS
Interesting tuples
Interesting tuples (discrete)
Max information gains
Max information gains (discrete)
Compute p-values from information gains and return MDFS
Discretize variable on demand
Generate contrast variables from data
Functions for MultiDimensional Feature Selection (MDFS): calculating multidimensional information gains, scoring variables, finding important variables, plotting selection results. This package includes an optional CUDA implementation that speeds up information gain calculation using NVIDIA GPGPUs. R. Piliszek et al. (2019) <doi:10.32614/RJ-2019-019>.