Software

The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. The toolbox is the most versatile toolbox for GP modeling.

The GPstuff toolbox works (at least) with Matlab versions r2009b (7.9) or newer and most of the functionality works also with Octave (3.6.4 or newer, see release notes for details). GPstuff can also be called from R with RcppOctave package. Most of the code is written in m-files but some of the most computationally critical parts have been coded in C.

Stable releases are available at Aalto PML group and the developer version at GitHub. We encourage to use the developer version since it includes the latest updates.

Reference
Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari (2013). GPstuff: Bayesian Modeling with Gaussian Processes. Journal of Machine Learning Research, 14:1175-1179. [Link]

GPQTLmapping is a code package to do Gaussian process (GP) modeling and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotype data. It uses GPs to model the continuously varying coefficients which describe how the effects of molecular markers on the quantitative trait are changing over time.

The code is available through GitHub: https://github.com/jpvanhat/GPQTLmapping

Reference
Jarno Vanhatalo, Zitong Li and Mikko Sillanpää (in press). A Gaussian process model and Bayesian variable selection for mapping function-valued quantitative traits with incomplete phenotype data. Bioinformatics, https://doi.org/10.1093/bioinformatics/btz164

An R software to quantify ecological memory functions for a subset of covariates within a linear or generalized linear model using penalized splines. Ecological memory functions indicate the length of persistent responses and the relative importance of past environmental conditions of current ecosystem function. Memory functions are used to generate weighted covariate values reflecting the cumulative effect of environmental conditions over time.

The software is available at https://github.com/msitter/EcoMem

Reference
Malcolm S. Itter, Jarno Vanhatalo and Andrew O. Finley (in press). EcoMem: An R package for quantifying ecological memory. Environmental Modelling & Software,

Demo code for the inversion of differential mobility particle sizer (DMPS) data and its extension SMPS inversion are available from here: DMPS and SMPS

Reference
Bjarke Mølgaard, Jarno Vanhatalo, Pasi Aalto, Nönne L. Prisle and Kaarle Hämeri (2016). Notably improved inversion of Differential Mobility Particle Sizer data obtained under conditions of fluctuating particle number concentrations.. Atmospheric Measurement Techniques, 9:741-751. [Link]

A demo code for GP priors for heterogeneous Student-t model with Laplace approximation and natural gradients.

Available here

Reference
Marcelo Hartmann and Jarno Vanhatalo (in press). Laplace approximation and natural gradient for Gaussian process regression with heteroscedastic Student-t model. Statistics and Computing. https://doi.org/10.1007/s11222-018-9836-0