FAST Change Point Detection in R
-
Updated
May 30, 2024 - R
FAST Change Point Detection in R
A repository for a showcase project. I analyze juice consumer data, using logistic regression and logistic lasso penalized regression in R to predict what juice brand a customer purchases based on characteristics of the situation.
GAUDI: a penalized regression based PRS method designed specifically for admixed individuals
An R package that implements the Hierarchical Feature Regression: a regularized group-shrinkage regression estimator based on supervised hierarchical graphs
Network-Based Regularization for Generalized Linear Models
Penalized regression for multiple types of many features with missing data using expectation-maximization (EM) algorithm.
LASSOPACK: Stata module for lasso, square-root lasso, elastic net, ridge, adaptive lasso estimation and cross-validation
Source files for R package Sieve
CRAN R package - oscar: Optimal Subset CArdinality Regression models
Variable selection for heterogeneous populations using the vennLasso penalty
Penalized least squares estimation using the Orthogonalizing EM (OEM) algorithm
A python package for penalized generalized linear models that supports fitting and model selection for structured, adaptive and non-convex penalties.
Supplementary material for the medium article Beyond linear regression: Leveraging linear regression for feature selection of continuous/categorical variables.
Smooth Effects on Response Penalty for CLM
Two applications of penalized models in statistical modeling
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…
Repo for the paper entitled "Developing Risk Prediction Models using Penalisation within Data that Adheres to Minimum Sample Size Criteria"
My research
Add a description, image, and links to the penalized-regression topic page so that developers can more easily learn about it.
To associate your repository with the penalized-regression topic, visit your repo's landing page and select "manage topics."