Conformalized Quantile Regression

Implementation in Python of the conformalized quantile regression (CQR) method for constructing marginal, distribusion-free prediction intervals

KnockoffZoom

A flexible tool for the multi-resolution localization of causal variants across the genome

Deep Knockoffs

Python package for sampling approximate model-X knockoffs using deep generative models

Metropolized Knockoff Sampling

Python and R scripts for sampling exact knockoffs for graphical models via the method of Metropolized knockoff sampling

SNPknock

R package with efficient algorithms to generate knockoffs for hidden Markov models, with special support for genetic data

SLOPE

R and MATLAB packages implementing SLOPE, or Sorted L-One Penalized Estimation. The sorted L1 norm is useful for statistical estimation and testing, particularly for variable selection in the linear model.

Knockoff Filter

R and MATLAB packages implementing the knockoff filter, a novel model selection strategy for variable selection which provably controls Type-I errors such as the FDR (false discovery rate)

SURE for Matrix Estimation

A MATLAB package containing unbiased risk estimates for spectral estimators and applying these formulas to the denoising of real clinical cardiac MRI series data.

TFOCS

Templates for First-Order Conic Solvers (TFOCS, pronounced tee-fox) is a MATLAB package that provides a set of templates, or building blocks, that can be used to construct efficient, customized solvers for a variety of models.

NESTA

NESTA is a fast and robust first-order method that solves minimum L1 problems and a large number of extensions including total-variation minimization.

SVT

Singular Value Thresholding (SVT) is an algorithm to minimize the nuclear norm of a matrix, subject to certain types of constraints.

CurveLab

CurveLab is a toolbox implementing the Fast Discrete Curvelet Transform in two and three dimensions, both in Matlab and C++.

L1-MAGIC

A collection of MATLAB routines for solving the convex optimization programs central to compressive sampling