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fastglm - Fast and Stable Fitting of Generalized Linear Models using 'RcppEigen'

Fits generalized linear models efficiently using 'RcppEigen'. The iteratively reweighted least squares implementation utilizes the step-halving approach of Marschner (2011) <doi:10.32614/RJ-2011-012> to help safeguard against convergence issues. Provides formula and matrix interfaces for count and cumulative-link ordinal models, mean bias reduction, and fitting from data read in blocks of rows for supported model classes.

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cpp

12.25 score 62 stars 24 dependents 279 scripts 13k downloads

personalized - Estimation and Validation Methods for Subgroup Identification and Personalized Medicine

Provides functions for fitting and validation of models for subgroup identification and personalized medicine / precision medicine under the general subgroup identification framework of Chen et al. (2017) <doi:10.1111/biom.12676>. This package is intended for use for both randomized controlled trials and observational studies and is described in detail in Huling and Yu (2021) <doi:10.18637/jss.v098.i05>.

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causal-inferenceheterogeneity-of-treatment-effectindividualized-treatment-rulespersonalized-medicineprecision-medicinesubgroup-identificationtreatment-effectstreatment-scoring

7.86 score 36 stars 1 dependents 113 scripts 620 downloads

jcolors - Colors Palettes for R and 'ggplot2', Additional Themes for 'ggplot2'

Contains a selection of color palettes and 'ggplot2' themes designed by the package author.

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color-palettesdata-visualizationggplot2visualization

6.80 score 25 stars 506 scripts 440 downloads

oem - Orthogonalizing EM: Penalized Regression for Big Tall Data

Solves penalized least squares problems for big tall data using the orthogonalizing EM algorithm of Xiong et al. (2016) <doi:10.1080/00401706.2015.1054436>. The main fitting function is oem() and the functions cv.oem() and xval.oem() are for cross validation, the latter being an accelerated cross validation function for linear models. The big.oem() function allows for out of memory fitting. A description of the underlying methods and code interface is described in Huling and Chien (2022) <doi:10.18637/jss.v104.i06>.

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group-lassolassomachine-learningmcpoemoem-algorithmpenalized-regressionscadvariable-selectionopenblascppopenmp

6.40 score 27 stars 1 dependents 31 scripts 581 downloads

forestBalance - Balancing Confounder Distributions with Forest Energy Balancing

Estimates average treatment effects using kernel energy balancing with random forest similarity kernels. A multivariate random forest jointly models covariates, outcome, and treatment to build a similarity kernel between observations. This kernel is then used for energy balancing to create weights that control for confounding. The method is described in De and Huling (2025) <doi:10.48550/arXiv.2512.18069>.

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cpp

5.29 score 14 scripts 331 downloads

vennLasso - Variable Selection for Heterogeneous Populations

Provides variable selection and estimation routines for models with main effects stratified on multiple binary factors. The 'vennLasso' package is an implementation of the method introduced in Huling et al. (2018) <doi:10.1111/biom.12769>.

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heterogeneitymachine-learningpenalized-regressionrisk-modellingvariable-selectioncpp

4.94 score 5 stars 35 scripts 102 downloads

swjm - Stagewise Variable Selection for Joint Models of Semi-Competing Risks

Implements stagewise regression for variable selection in joint models of recurrent events and terminal events (semi-competing risks). Supports two model frameworks: the joint frailty model (Cox-type) and the joint scale-change model (AFT-type). Provides cooperative lasso, lasso, and group lasso penalties with cross-validation for tuning parameter selection via cross-fitted estimating equations.

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openblascpp

4.85 score 8 scripts 249 downloads

independenceWeights - Estimates Weights for Confounding Control for Continuous-Valued Exposures

Estimates weights to make a continuous-valued exposure statistically independent of a vector of pre-treatment covariates using the method proposed in Huling, Greifer, and Chen (2021) <arXiv:2107.07086>.

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causalcausal-inferenceweighting

3.65 score 9 stars 4 scripts 258 downloads

personalized2part - Two-Part Estimation of Treatment Rules for Semi-Continuous Data

Implements the methodology of Huling, Smith, and Chen (2020) <doi:10.1080/01621459.2020.1801449>, which allows for subgroup identification for semi-continuous outcomes by estimating individualized treatment rules. It uses a two-part modeling framework to handle semi-continuous data by separately modeling the positive part of the outcome and an indicator of whether each outcome is positive, but still results in a single treatment rule. High dimensional data is handled with a cooperative lasso penalty, which encourages the coefficients in the two models to have the same sign.

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cpp

3.60 score 2 stars 6 scripts 490 downloads

hierSDR - Hierarchical Sufficient Dimension Reduction

Provides semiparametric sufficient dimension reduction for central mean subspaces for heterogeneous data defined by combinations of binary factors (such as chronic conditions). Subspaces are estimated to be hierarchically nested to respect the structure of subpopulations with overlapping characteristics. This package is an implementation of the proposed methodology of Huling and Yu (2021) <doi:10.1111/biom.13546>.

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3.00 score 2 stars 9 scripts 278 downloads