R package for Bayesian meta-analysis models, using Stan
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Updated
Dec 16, 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
Block bootstrap methods for quantile regression in time series
A repo for "Precipitation Scaling With Temperature in the Northeast US: Variations by Weather Regime, Season, and Precipitation Intensity"
Kaggle competition "Net-Load Forecasting During the "Sobriety" Period"
Learning Multiple Quantiles With Neural Networks
[JCGS 2021] Official Implement of the paper "Learning Multiple Quantiles With Neural Networks"
Predicting March Madness results using quantile regression with train data on tournament teams from 2002-2022
Projeto de Monografia apresentado como requisito parcial para conclusão do curso de Bacharelado em Estatística pela UFES.
Presented workshop at the Society for Epidemiologic Research (SER) 2023 conference
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