# Rob Tibshirani

#### 25 packages on CRAN

R functions for adaptively constructing index models for continuous, binary and survival outcomes. Implementation requires loading R-pacakge "survival"

This is a function for validating microarray clusters via reproducibility, based on the paper referenced below.

Estimation of a sparse inverse covariance matrix using a lasso (L1) penalty. Facilities are provided for estimates along a path of values for the regularization parameter.

A method for fitting the entire regularization path of the principal components lasso for linear and logistic regression models. The algorithm uses cyclic coordinate descent in a path-wise fashion. See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) 'Principal component-guided sparse regression' <arXiv:1810.04651>.

Fits a pliable lasso model. For details see Tibshirani and Friedman (2018) <arXiv:1712.00484>.

Significance Analysis of Microarrays for differential expression analysis, RNAseq data and related problems.

New tools for post-selection inference, for use with forward stepwise regression, least angle regression, the lasso, and the many means problem. The lasso function implements Gaussian, logistic and Cox survival models.

Supervised principal components for regression and survival analsysis. Especially useful for high-dimnesional data, including microarray data.

Univarate shrinkage prediction for survival analysis using in the Cox model.. Especially useful for high-dimensional data, including microarray data.

Cell-type specific differential expression of a microarray experiment of heterogeneous tissue samples, using SAM.

Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression and the Cox model. Two recent additions are the multiple-response Gaussian, and the grouped multinomial regression. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers listed in the URL below.

Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect. For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.

Implementation of the convex hierarchical testing (CHT) procedure introduced in Bien, Simon, and Tibshirani (2015) Convex Hierarchical Testing of Interactions. Annals of Applied Statistics. Vol. 9, No. 1, 27-42.

Hybrid hierarchical clustering via mutual clusters. A mutual cluster is a set of points closer to each other than to all other points. Mutual clusters are used to enrich top-down hierarchical clustering.

We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.

Performs Penalized Multivariate Analysis: a penalized matrix decomposition, sparse principal components analysis, and sparse canonical correlation analysis, described in the following papers: (1) Witten, Tibshirani and Hastie (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3):515-534. (2) Witten and Tibshirani (2009) Extensions of sparse canonical correlation analysis, with applications to genomic data. Statistical Applications in Genetics and Molecular Biology 8(1): Article 28.

A direct and flexible method for estimating an ICA model. This approach estimates the densities for each component directly via a tilted gaussian. The tilt functions are estimated via a GAM poisson model. Details can be found in "Elements of Statistical Learning (2nd Edition)" Section 14.7.4

Performs minimax linkage hierarchical clustering. Every cluster has an associated prototype element that represents that cluster as described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084.

Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear, logistic and Cox models.

Provides a covariance estimator for multivariate normal data that is sparse and positive definite. Implements the majorize-minimize algorithm described in Bien, J., and Tibshirani, R. (2011), "Sparse Estimation of a Covariance Matrix," Biometrika. 98(4). 807--820.

Fit a trio model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. This package is based on Noah Simon, et al. (2011) <doi:10.1080/10618600.2012.681250>.