An extension of XGBoost to probabilistic modelling
gamlss 开源项目推荐(33 个)
gamlss 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
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按 Star 数排序展示前 20 个 · GitHub 共 33 个仓库An extension of LightGBM to probabilistic modelling
An extension of CatBoost to probabilistic modelling
A package for online distributional learning.
Interpretable additive models for tabular data: classical GAMs (with REML smoothness selection, inference, shape constraints & diagnostics) plus neural additive models (NAM, NBM, NodeGAM, GPNAM, SIAN, SPAM, and more) for regression, classification, and distributional regression.
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
Toolbox to estimate, automatically regularize, and select between Generalized Additive Mixed Models and their extensions in Python
RefCurv: A Software for the Construction of Pediatric Reference Curves
A Python library for Generalized Additive Models (GAMs) with flexible smoothing, principled inference, and beautiful visualizations.
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
Shiny App: Calculation of Age-dependent Reference Intervals (AdRI)
Framework for the visualization of distributional regression models
Shiny App for regression analysis using GAMLSS
Shiny App: Calculation of Age-dependent Reference Intervals with GAMLSS (AdRI_GAMLSS)
This repo provides supplemental material for the article titled: "Assessing Potential Heteroscedasticity in Psychological Data: A GAMLSS approach"
Fast GAMLSS surrogate for Hall dynamic weight change model (used in microsimulation)
MSc dissertation repository implementing gradient boosting for power logit and inflated power logit regression models
Generalized Additive Models for Location, Scale and Shape in PyTorch.
R code and BayesX scripts for effect selection and complexity reduction on basis of the Normal Beta Prime Spike and Slab Prior
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