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.

Last updated 3 years ago

cpp

8.25 score 57 stars 13 dependents 55 scripts 2.9k 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>.

Last updated 2 years ago

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

7.38 score 32 stars 1 dependents 125 scripts 312 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.

Last updated 10 months ago

color-palettesdata-visualizationggplot2visualization

6.68 score 25 stars 380 scripts 415 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>.

Last updated 6 months ago

group-lassolassomachine-learningmcpoemoem-algorithmpenalized-regressionscadvariable-selectionopenblascppopenmp

5.55 score 27 stars 26 scripts 358 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>.

Last updated 2 years ago

causalcausal-inferenceweighting

3.48 score 6 stars 2 scripts 188 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>.

Last updated 3 years ago

3.00 score 2 stars 9 scripts 146 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.

Last updated 4 years ago

cpp

3.00 score 2 stars 5 scripts 205 downloads