Estimate Realtime Case Counts and Time-varying Epidemiological Parameters
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Updated
May 21, 2024 - R
Estimate Realtime Case Counts and Time-varying Epidemiological Parameters
Simple Time Series Modelling Using Gaussian Processes
Clustering and Predictions with Multi-Task Gaussian Processes
An R package for I-prior regression
R interface to 'dgpsi' for deep and linked Gaussian process emulations
This instruction aims to reproduce the results in the paper “Mesh-clustered Gaussian process emulator for partial differential equation boundary value problems”(2024) to appear in Technometrics.
Radial neighbors GP
A multi-target regression algorithm based on Gaussian process regression
This R package allows the emulation using a mesh-clustered Gaussian process (mcGP) model for partial differential equation (PDE) systems.
R-package for interpretable nonparametric modeling of longitudinal data using additive Gaussian processes. Contains functionality for inferring covariate effects and assessing covariate relevances. Various models can be specified using a convenient formula syntax.
This instruction aims to reproduce the results in the paper “Functional-Input Gaussian Processes with Applications to Inverse Scattering Problems” proposed by Sung, Wang, Cakoni, Harris, and Hung.
VSPsnap is a collection of R and Python code for Gaussian Process regression in a kriging-like setting (i.e. two features (X,Y) and a target (Z)) - with a focus on SARS-CoV2 data (genomic/IR/FR).
AvGPR is a package that calculates a weighted average Gaussian Process regression model over 5 implementations from packages in both R and Python.
This R package allows the estimation and prediction for a clustered Gaussian process model proposed by Sung, Haaland, Hwang, and Lu (2023) in Statistica Sinica
Companion R code for the book Bayesian Optimization with Application to Computer Experiments
Regularised B-splines projected Gaussian Process priors
The R package varycoef implements Gaussian processes spatially varying coefficient models.
This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our paper "Accounting for Gaussian Process Imprecision in Bayesian Optimization"
This repository is out of date. See instead: https://github.com/paigejo/ELK
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