quantile-regression

There are 99 repositories under quantile-regression topic.

  • ShangtongZhang/DeepRL

    Modularized Implementation of Deep RL Algorithms in PyTorch

    Language:Python3.2k9092688

  • qfettes/DeepRL-Tutorials

    Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

    Language:Jupyter Notebook1.1k3010323
  • crepes

    henrikbostrom/crepes

    Python package for conformal prediction

    Language:Python46642135

  • cyoon1729/RLcycle

    A library for ready-made reinforcement learning agents and reusable components for neat prototyping

    Language:Python30013663

  • yromano/cqr

    Conformalized Quantile Regression

    Language:Jupyter Notebook2588649

  • zillow/quantile-forest

    Quantile Regression Forests compatible with scikit-learn.

    Language:Python216112325
  • Conffusion

    eliahuhorwitz/Conffusion

    Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.

    Language:Python137515

  • superlinear-ai/conformal-tights

    šŸ‘– Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster

    Language:Python90383

  • FilippoMB/Ensemble-Conformalized-Quantile-Regression

    Valid and adaptive prediction intervals for probabilistic time series forecasting

    Language:Jupyter Notebook87279

  • liquidSVM/liquidSVM

    Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

    Language:C++665169

  • aangelopoulos/im2im-uq

    Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with rigorous, distribution-free uncertainty quantification.

    Language:Python544510

  • saattrupdan/doubt

    Bringing back uncertainty to machine learning.

    Language:Python503293

  • wwiecek/baggr

    R package for Bayesian meta-analysis models, using Stan

    Language:R48213412

  • getzze/RobustModels.jl

    A Julia package for robust regressions using M-estimators and quantile regressions

    Language:Julia36561

  • ErezSC42/qr_forcaster

    Our implementation of the paper "A Multi-Horizon Quantile Recurrent Forecaster"

    Language:Python31119

  • RichardFindlay/day-ahead-probablistic-forecasting-with-quantile-regression

    Using an integrated pinball-loss objective function in various recurrent based deep learning architectures made with keras to simultaneously produce probabilistic forecasts for UK wind, solar, demand and price forecasts.

    Language:Python29514

  • BayerSe/l1qr

    Lasso Quantile Regression

    Language:Python28638

  • Buczman/CaviaR

    R code for CAViaR model

    Language:R285014

  • lorismichel/quantregForest

    R packageĀ -Ā QuantileĀ RegressionĀ Forests, a tree-based ensemble method for estimation of conditional quantiles (Meinshausen, 2006).

    Language:C264136

  • RektPunk/MQBoost

    Multiple quantiles estimation model maintaining non-crossing condition (or monotone quantile condition) using LightGBM and XGBoost

    Language:Python26244

  • dannysdeng/dqn-pytorch

    PyTorch - Implicit Quantile Networks - Quantile Regression - C51

    Language:Python23302

  • gcampanella/pydata-london-2018

    Slides and notebooks for my tutorial at PyData London 2018

    Language:Jupyter Notebook22406

  • msesia/chr

    Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems

    Language:Python21106
  • SGP

    CenterForAssessment/SGP

    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 the coefficient matrices derived from the quantile regression analyses and specify what percentile growth is required for students to reach future achievement targets.

    Language:R20132121

  • fmpr/DeepJMQR

    Deep joint mean and quantile regression for spatio-temporal problems

    Language:Jupyter Notebook15347

  • SSS135/aiqn-vae

    VAE + Quantile Networks for MNIST

    Language:Python12201

  • TeaPearce/Censored_Quantile_Regression_NN

    NeurIPS paper 'Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis'

    Language:Jupyter Notebook12102

  • yatshunlee/CAViaR

    Measure market risk by CAViaR model

    Language:Jupyter Notebook11252

  • CY-dev/hqreg

    Regularization Paths for Huber Loss Regression and Quantile Regression Penalized by Lasso or Elastic-Net

    Language:C10352

  • opardo/GPDPQuantReg

    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.

    Language:R10100

  • elifyilmaz2027/traffic_flow_forecasting_methods

    The repository gives case studies on short-term traffic flow forecasting strategies within the scope of my master thesis.

    Language:Jupyter Notebook9101

  • adrian-lison/interval-scoring

    This repository contains python implementations of scoring rules for forecasts provided in a prediction interval format.

    Language:Jupyter Notebook8102

  • Jbrich95/pinnEV

    Partially-Interpretable Neural Networks for Extreme Value modelling

    Language:R8201

  • RektPunk/mcqrnn

    Monotone composite quantile regression neural network (MCQRNN) with tensorflow 2.x.

    Language:Python8202

  • anhdanggit/non-parametric-econometrics

    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

    Language:R7208

  • luca-pernigo/kernel_quantile_regression

    Kernel quantile regression

    Language:Jupyter Notebook5201