R package for Bayesian meta-analysis models, using Stan
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Updated
Feb 13, 2024 - R
R package for Bayesian meta-analysis models, using Stan
Functions to calculate student growth percentiles and percentile growth projections/trajectories for students using large scale, longitudinal assessment data. Functions use quantile regression to estimate the conditional density associated with each student's achievement history. Percentile growth projections/trajectories are calculated using th…
R Package. Bayesian and nonparametric quantile regression, using Gaussian Processes to model the trend, and Dirichlet Processes, for the error. Author: Carlos Omar Pardo Gomez.
Partially-Interpretable Neural Networks for Extreme Value modelling
This is the R code for several common non-parametric methods (kernel est., mean regression, quantile regression, boostraps) with both practical applications on data and simulations
D-Vine GAM Copula based Quantile Regression
R package for estimating quantile regression coefficients via the quantile spacing method
Random or Extremely Random Forest for censored quantile regression.
The goal of esreg is to simultaneously model the quantile and the expected shortfall of a response variable given a set of covariates.
R Package: Adaptively weighted group lasso for semiparametic quantile regression models
Kaggle competition "Net-Load Forecasting During the "Sobriety" Period"
Learning Multiple Quantiles With Neural Networks
Block bootstrap methods for quantile regression in time series
Projeto de Monografia apresentado como requisito parcial para conclusão do curso de Bacharelado em Estatística pela UFES.
Predicting March Madness results using quantile regression with train data on tournament teams from 2002-2022
Presented workshop at the Society for Epidemiologic Research (SER) 2023 conference
test the phenomenon of PEAD in China
R/jags model code for hierarchical Bayesian quantile regression
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