Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. **Superseded by the models-by-example repo**.
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Updated
Nov 25, 2020 - R
Code that might be useful to others for learning/demonstration purposes, specifically along the lines of modeling and various algorithms. **Superseded by the models-by-example repo**.
Estimate Realtime Case Counts and Time-varying Epidemiological Parameters
By-hand code for models and algorithms. An update to the 'Miscellaneous-R-Code' repo.
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.
Variational Inference for Langevin Equations
An R package that implements the methods of geostatistics for functional data
Clustering and Predictions with Multi-Task Gaussian Processes
Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning
The R package varycoef implements Gaussian processes spatially varying coefficient models.
Software package for Gaussian Process (GP) modelling written in R language. The core functions are coded in C++ and based on the EIGEN library (through RcppEigen).
Missing data imputation for longitudinal multi-variable EHR data. Paper in JAMIA.
Regularised B-splines projected Gaussian Process priors
R package for Partially Separable Multivariate Functional Data and Functional Graphical Models
Companion R code for the book Bayesian Optimization with Application to Computer Experiments
This R package allows the emulation using a mesh-clustered Gaussian process (mcGP) model for partial differential equation (PDE) systems.
Gaussian processes for machine learning in R and FORTRAN.
R package for nonstationary spatial modeling with covariate-based covariance functions
This repository contains Prior-RObust Bayesian Optimization (PROBO) as introduced in our paper "Accounting for Gaussian Process Imprecision in Bayesian Optimization"
Multivariate Gaussian Subspatial Regression R package
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