RPtests - Goodness of Fit Tests for High-Dimensional Linear Regression Models

Performs goodness of fits tests for both high and low-dimensional linear models. It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity. In addition one can test the significance of potentially large groups of variables, and also produce p-values for the significance of individual variables in high-dimensional linear regression.

Last updated 4 years ago

cpp

2.08 score 2 stars 2 dependents 4 scripts 254 downloads

LassoBacktracking - Modelling Interactions in High-Dimensional Data with Backtracking

Implementation of the algorithm introduced in Shah, R. D. (2016) <https://www.jmlr.org/papers/volume17/13-515/13-515.pdf>. Data with thousands of predictors can be handled. The algorithm performs sequential Lasso fits on design matrices containing increasing sets of candidate interactions. Previous fits are used to greatly speed up subsequent fits, so the algorithm is very efficient.

Last updated 2 years ago

cpp

1.00 score 1 stars 3 scripts 217 downloads

FSInteract - Fast Searches for Interactions

Performs fast detection of interactions in large-scale data using the method of random intersection trees introduced in Shah, R. D. and Meinshausen, N. (2014) <http://www.jmlr.org/papers/v15/shah14a.html>. The algorithm finds potentially high-order interactions in high-dimensional binary two-class classification data, without requiring lower order interactions to be informative. The search is particularly fast when the matrices of predictors are sparse. It can also be used to perform market basket analysis when supplied with a single binary data matrix. Here it will find collections of columns which for many rows contain all 1's.

Last updated 8 years ago

cppopenmp

1.00 score 1 scripts 494 downloads