bark R package for Bayesian nonparametric kernel regression
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Updated
Oct 6, 2024 - R
bark R package for Bayesian nonparametric kernel regression
Regularized Bayesian varying coefficient regression for group testing data
📦 R/haldensify: Highly Adaptive Lasso Conditional Density Estimation
Anisotropic smoothing for change-point regression data
Functions for conducting regression estimation on nonsmooth data
Here is a collection of machine learning methods implemented from scratch.
Source files for R package Sieve
🤠 📿 The Highly Adaptive Lasso
Development version of the TrendLSW R package
A statistical framework for feature selection and association mapping with 3D shapes
The julia package for nonparametric density estimate and regression
This is an R package to compute the multivariate quasiconvex/quasiconcave nonparametric LSE with or without additional monotonicity constraints described in "Least Squares Estimation of a Monotone Quasiconvex Regression Function" by Somabha Mukherjee, Rohit K. Patra, Andrew L. Johnson, and Hiroshi Morita.
The state-of-the-art method for denoising 1D signals
Simple local constant and local linear regressions in Julia
Nonparametric regression examples with R and Python
Nonparametric Sobol Estimator with Bootstrap Bandwidth
Reproducibility repo for the simulation and real data results in the SINATRA manuscript set to appear in AoAS.
Companion Jupyter notebook of the paper "Functional Estimation of Anisotropic Covariance and Autocovariance Operators on the Sphere"
Easy-to-use collection of statistical methods and techniques, all written in R 🗂
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