Code in R is available here.
The goal of SIHR is to provide inference procedures in the high-dimensional setting for (1) linear functionals in generalized linear regression, (2) conditional average treatment effects in generalized linear regression (CATE), (3) quadratic functionals in generalized linear regression (QF) (4) inner product in generalized linear regression (InnProd) and (5) distance in generalized linear regression (Dist). Currently, we support different generalized linear regression, by specifying the argument model in “linear”, “logisitc”, “logistic_alter”.
Rakshit, P. , Wang, Z., Cai, T. T. and Guo, Z. (2024)
R Journal, to appear.
Guo, Z. , Rakshit, P., Herman, D., and Chen, J. (2019).
Inference for Case Probability in High-dimensional Logistic Regression.
Journal of Machine Learning Research, 22(254):1−54.
Guo, Z. Renaux, C., Bühlmann, P. and Cai, T. T. (2019).
Group Inference in High Dimensions with Applications to Hierarchical Testing.
Electronic Journal of Statistics, to appear.
Cai, T, Cai, T. T. and Guo, Z. (2019).
Optimal Statistical Inference for Individualized Treatment Effects in High-dimensional Models.
Journal of the Royal Statistical Society: Series B, to appear.
Cai, T. T. and Guo, Z. (2020).
Semi-supervised Inference for Explained Variance in High-dimensional Linear Regression and Its Applications.
Journal of the Royal Statistical Society: Series B, 82(2), 391-419.
Cai, T. T. , Guo, Z. Ma, R. (2021+).
Statistical Inference for High-Dimensional Generalized Linear Models with Binary Outcomes.
Journal of the American Statistical Association, to appear.