An extension of XGBoost to probabilistic modelling

An extension of LightGBM to probabilistic modelling

An extension of CatBoost to probabilistic modelling

Boosting models for fitting generalized additive models for location, shape and scale (GAMLSS) to potentially high dimensional data. The current relase version can be found on CRAN (https://cran.r-project.org/package=gamboostLSS).

An extension of Py-Boost to probabilistic modelling

A package for online learning for distributional regression and online models for conditional heteroskedasiticity

Toolbox to estimate Generalized Additive Mixed Models and their (Markov-switching) extensions in Python

RefCurv: A Software for the Construction of Pediatric Reference Curves

A general modelling framework for specifying and fitting models to empirical fundamental diagrams of road traffic, and for comparing the model fits using information criteria

Framework for the visualization of distributional regression models

Shiny App: Calculation of Age-dependent Reference Intervals (AdRI)

This repo provides supplemental material for the article titled: "Assessing Potential Heteroscedasticity in Psychological Data: A GAMLSS approach"

Shiny App: Calculation of Age-dependent Reference Intervals with GAMLSS (AdRI_GAMLSS)

This repo contains an R script that algorithmically finds the best distribution that fits several continuous, randomized variables

R code and BayesX scripts for effect selection and complexity reduction on basis of the Normal Beta Prime Spike and Slab Prior

Este projeto tem como objetivo, através de uma regressão binomial do tipo logito, predizer as chances de um nódulo de mama ser maligno ou benigno. O projeto visa aplicar o aprendizado das aulas do programa de Especialização em Data Science e Big Data da UFPR e compor parte da nota na disciplina de Inferência Estatística parte 3.

Forecasting Oil Prices with Time Series & Generalized Additive Models for Location, Scale and Shape

GAMLSS code for modeling PFAS DNA methylation relationship.

LaTeX code for my master's thesis.

This is a documentation of research done on a Special Olympics data set