gam - Generalized Additive Models
Functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).
Last updated 2 months ago
9.67 score 4 stars 61 packages 2.2k scripts 19k downloadslars - Least Angle Regression, Lasso and Forward Stagewise
Efficient procedures for fitting an entire lasso sequence with the cost of a single least squares fit. Least angle regression and infinitesimal forward stagewise regression are related to the lasso, as described in the paper below.
Last updated 3 years ago
8.06 score 6 stars 79 packages 724 scripts 11k downloadsISLR - Data for an Introduction to Statistical Learning with Applications in R
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R'.
Last updated 3 years ago
7.65 score 4 stars 2 packages 9.5k scripts 32k downloadssoftImpute - Matrix Completion via Iterative Soft-Thresholded SVD
Iterative methods for matrix completion that use nuclear-norm regularization. There are two main approaches.The one approach uses iterative soft-thresholded svds to impute the missing values. The second approach uses alternating least squares. Both have an 'EM' flavor, in that at each iteration the matrix is completed with the current estimate. For large matrices there is a special sparse-matrix class named "Incomplete" that efficiently handles all computations. The package includes procedures for centering and scaling rows, columns or both, and for computing low-rank SVDs on large sparse centered matrices (i.e. principal components).
Last updated 4 years ago
7.48 score 10 stars 23 packages 235 scripts 1.8k downloadsmda - Mixture and Flexible Discriminant Analysis
Mixture and flexible discriminant analysis, multivariate adaptive regression splines (MARS), BRUTO, and vector-response smoothing splines. Hastie, Tibshirani and Friedman (2009) "Elements of Statistical Learning (second edition, chap 12)" Springer, New York.
Last updated 15 days ago
7.16 score 2 stars 16 packages 356 scripts 8.5k downloadsISLR2 - Introduction to Statistical Learning, Second Edition
We provide the collection of data-sets used in the book 'An Introduction to Statistical Learning with Applications in R, Second Edition'. These include many data-sets that we used in the first edition (some with minor changes), and some new datasets.
Last updated 2 years ago
5.42 score 2 stars 1.8k scripts 14k downloadsgamsel - Fit Regularization Path for Generalized Additive Models
Using overlap grouped-lasso penalties, 'gamsel' selects whether a term in a 'gam' is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families. See <doi:10.48550/arXiv.1506.03850> for more details.
Last updated 2 months ago
3.95 score 2 stars 30 scripts 413 downloadssvmpath - The SVM Path Algorithm
Computes the entire regularization path for the two-class svm classifier with essentially the same cost as a single SVM fit.
Last updated 4 years ago
2.84 score 2 packages 38 scripts 357 downloadssparsenet - Fit Sparse Linear Regression Models via Nonconvex Optimization
Efficient procedure for fitting regularization paths between L1 and L0, using the MC+ penalty of Zhang, C.H. (2010)<doi:10.1214/09-AOS729>. Implements the methodology described in Mazumder, Friedman and Hastie (2011) <DOI: 10.1198/jasa.2011.tm09738>. Sparsenet computes the regularization surface over both the family parameter and the tuning parameter by coordinate descent.
Last updated 6 days ago
2.38 score 2 stars 1 packages 20 scripts 199 downloadsProDenICA - Product Density Estimation for ICA using Tilted Gaussian Density Estimates
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)" in Section 14.7.4.
Last updated 3 years ago
2.23 score 2 stars 21 scripts 170 downloads