Software
personalized
Estimation and validation methods for subgroup identification and personalized medicine
Designed for the analysis of data where the effect of a treatment or intervention may vary across patients. Works with data from randomized controlled trials or observational studies. Provides fitting and validation of subgroup identification and personalized medicine models under the general subgroup identification framework of Chen et al. (2017).
independenceWeights
Confounding control for continuous-valued exposures
Implements the methodology of Huling, Greifer, and Chen (2021), constructing weights that minimize the weighted statistical dependence between a continuous treatment/exposure and a vector of confounders. Because confounding bias is a function of this dependence, these weights mitigate confounding bias directly without requiring a parametric propensity model.
oem
Orthogonalizing EM algorithm for penalized regression
Efficient computation for penalized regression models using the Orthogonalizing EM algorithm, designed for tall datasets. Supports lasso, MCP, SCAD, elastic net, group lasso, group MCP/SCAD, and more. Also available as a Julia implementation.
personalized2part
Two-part individualized treatment rules for semi-continuous data
Implements the methodology of Huling, Smith, and Chen (2020) for subgroup identification with semi-continuous outcomes. Uses a two-part (hurdle) framework to jointly model the binary and continuous components of the outcome, yielding a single treatment rule. High-dimensional settings are handled via a cooperative lasso penalty.
hierSDR
Hierarchical sufficient dimension reduction
Semiparametric sufficient dimension reduction for settings where population heterogeneity is defined by binary stratifying factors (e.g., chronic conditions in hospital risk modeling). Dimension reduction conforms to the hierarchical relationships between subpopulations, enabling tailored and interpretable models.
vennLasso
Variable selection for heterogeneous populations
Variable selection for high-dimensional models where population heterogeneity is defined by binary stratifying factors. Yields sparsity patterns that adhere to the hierarchical structure among subpopulations, enabling structured, interpretable variable selection across groups.
groupFusedMulti
Doubly structured variable selection for grouped multivariate outcomes
Penalized estimation for high-dimensional regression with multivariate outcomes that have a natural group structure. Implements the methodology of Huling et al. (2023).
personalizedLong
Fused comparative intervention scoring for long-term interventions
Estimation of individualized intervention rules for long-term treatments whose effects change smoothly over time and vary across a population. Implements the fused comparative intervention scoring methodology for heterogeneous longitudinal intervention effects.
aftiv
Instrumental variable estimation under the semiparametric AFT model
Instrumental variable estimation for time-to-event outcomes under the semiparametric accelerated failure time model.