personalized: Estimation and validation methods for subgroup identification and personalized medicine
The 'personalized' package is designed for the analysis of data where the effect of a treatment or intervention may vary for different patients. It can be used for either data from randomized controlled trials or observational studies and is not limited specifically to the analysis of medical data. It provides functions for fitting and validation of subgroup identification and personalized medicine models under the general subgroup identification framework of Chen et al. (2017).
oem: Orthogonalizing EM algorithm for penalized estimation
oem provides computation for various penalized regression models using the Orthogonalizing EM algorithm and is highly efficient for tall data. Penalties available include the lasso, MCP, SCAD, elastic net, group lasso, group MCP/SCAD, and more.
vennLasso: Variable selection for heterogeneous populations
The vennLasso package provides variable selection for high-dimensional modeling scenarios where heterogeneity is defined by several binary factors which stratify the population into multiple subpopulations. For example, vennLasso can be used in a hospital-wide risk modeling application if covariate effects in risk models differ for subpopulations of patients with different chronic conditions. Here the chronic conditions are the binary stratifying factors. The vennLasso provides computation for a variable selection method which yields variable selection patterns which adhere to the hierarchical nature of the relationships between the various subpopulations.