# Hui Zou

#### 13 packages on CRAN

Provides functions for fitting the entire solution path of the Elastic-Net and also provides functions for doing sparse PCA.

Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression.

We implement a cocktail algorithm, a good mixture of coordinate decent, the majorization-minimization principle and the strong rule, for computing the solution paths of the elastic net penalized Cox's proportional hazards model.

A generalized coordinate descent (GCD) algorithm for computing the solution path of the hybrid Huberized support vector machine (HHSVM) and its generalization, including the LASSO and elastic net (adaptive) penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.

A unified algorithm, blockwise-majorization-descent (BMD), for efficiently computing the solution paths of the group-lasso penalized least squares, logistic regression, Huberized SVM and squared SVM. The package is an implementation of Yang, Y. and Zou, H. (2015) DOI: <doi:10.1007/s11222-014-9498-5>.

This package implements a iteratively reweighed least square (IRLS) strategy that incorporates a blockwise majorization decent (BMD) method, for efficiently computing the solution paths of the (grouped) lasso and the (grouped) elastic net for the Tweedie model.

An efficient algorithm inspired by majorization-minimization principle for solving the entire solution path of a flexible nonparametric expectile regression estimator constructed in a reproducing kernel Hilbert space.

A novel implementation that solves the linear distance weighted discrimination and the kernel distance weighted discrimination. Reference: Wang and Zou (2018) <doi:10.1111/rssb.12244>.

It implements a new procedure of variable selection in the context of redundancy between explanatory variables, which holds true with high dimensional data (Grimonprez et al. (2018) <https://hal.inria.fr/hal-01857242>).

Efficient procedures for computing a new Multi-Class Sparse Discriminant Analysis method that estimates all discriminant directions simultaneously.

A coordinate descent algorithm for computing the solution path of the sparse and coupled sparse asymmetric least squares, including the elastic net and (adaptive) Lasso penalized SALES and COSALES regressions.

Formulates a sparse distance weighted discrimination (SDWD) for high-dimensional classification and implements a very fast algorithm for computing its solution path with the L1, the elastic-net, and the adaptive elastic-net penalties. More details about the methodology SDWD is seen on Wang and Zou (2016) Sparse distance weighted discrimination, Journal of Computational and Graphical Statistics, 25(3), 826-838. (<doi:10.1080/10618600.2015.1049700>).

A boosted Tweedie compound Poisson model using the gradient boosting. It is capable of fitting a flexible nonlinear Tweedie compound Poisson model (or a gamma model) and capturing interactions among predictors.