Toolbox to estimate Generalized Additive Mixed Models and their (Markov-switching) extensions in Python
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Updated
Oct 30, 2024 - Python
Toolbox to estimate Generalized Additive Mixed Models and their (Markov-switching) extensions in Python
A package for online learning for distributional regression and online models for conditional heteroskedasiticity
An extension of XGBoost to probabilistic modelling
An extension of LightGBM to probabilistic modelling
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"
An extension of CatBoost to probabilistic modelling
Framework for the visualization of distributional regression models
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).
Shiny App: Calculation of Age-dependent Reference Intervals with GAMLSS (AdRI_GAMLSS)
RefCurv: A Software for the Construction of Pediatric Reference Curves
GAMLSS code for modeling PFAS DNA methylation relationship.
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
An extension of Py-Boost to probabilistic modelling
This is a documentation of research done on a Special Olympics data set
Forecasting Oil Prices with Time Series & Generalized Additive Models for Location, Scale and Shape
This repo contains an R script that algorithmically finds the best distribution that fits several continuous, randomized variables
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.
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