By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.

A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.

A workshop on using generalized additive models and the mgcv package.

Penalized least squares estimation using the Orthogonalizing EM (OEM) algorithm

Fast Change Point Detection in R

A Julia module that implements the (normalized) iterative hard thresholding algorithm(IHT) of Blumensath and Davies. IHT performs feature selection akin to LASSO- or MCP-penalized regression using a greedy selection approach.

Nonparametric regression and prediction using the highly adaptive lasso algorithm

LASSOPACK: Stata module for lasso, square-root lasso, elastic net, ridge, adaptive lasso estimation and cross-validation

Source files for R package Sieve

GAUDI: a penalized regression based PRS method designed specifically for admixed individuals

Variable selection for heterogeneous populations using the vennLasso penalty

Penalized Regression with Second-Generation P-Values

Regression models for "epigenetic clock" estimation of canine chronological age

Network-Based Regularization for Generalized Linear Models

Biomarker selection in penalized regression models

Supplementary material for the medium article Beyond linear regression: Leveraging linear regression for feature selection of continuous/categorical variables.

Multi-source sparse Tweedie modelling

sparseGFM implements sparse generalized factor models for dimension reduction and variable selection in high-dimensional continuous, count, and binary data. Stable release available on CRAN (https://cran.r-project.org/package=sparseGFM); development version hosted on GitHub.

We explored various approaches to deal with high-dimensional data in this study, and we compared them using simulation and soil datasets. We discovered that grouping had a significant impact on model correctness and error reduction. For the core projection step, we first looked at the properties of all the algorithms and how they function to com…

CRAN R package - oscar: Optimal Subset CArdinality Regression models