🤠 📿 The Highly Adaptive Lasso

Cubic smoothing splines in Julia.

The julia package for nonparametric density estimate and regression

📦 R/haldensify: Highly Adaptive Lasso Conditional Density Estimation

B-Spline Density Estimation Library - nonparametric density estimation using B-Spline density estimator from univariate sample.

Chinese Restaurant Process Models for Regression and Clustering. Master branch contains latest stable build.

A statistical framework for feature selection and association mapping with 3D shapes

Multi-dimensional Functional Principal Component Analysis

Simple local constant and local linear regressions in Julia

Nonparametric regression and prediction using the highly adaptive lasso algorithm

The state-of-the-art method for denoising 1D signals

Source files for R package Sieve

Some R Code for My Paper

Regularized Bayesian varying coefficient regression for group testing data

Easy-to-use collection of statistical methods and techniques, all written in R 🗂

Lecture on Local Polynomial Regression given for the Statistical Machine Learning exam at University of Trieste

Classic metrics methods used in machine learning

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.

Companion Jupyter notebook of the paper "Functional Estimation of Anisotropic Covariance and Autocovariance Operators on the Sphere"

My research