Lightweight Implementation for the Most Common Gaussian Process
Models
c(person("Juho", "Piironen", role = c("cre", "aut"), email = "juho.t.piironen@gmail.com"))
Implements the most common Gaussian process (GP) models using Laplace and
expectation propagation (EP) approximations, maximum marginal likelihood
(or posterior) inference for the hyperparameters, and sparse approximations
for larger datasets.

Implementation for the Most Common Gaussian Process Models

General Purpose Gaussian Process Modelling

Generalized partial linear models (GPLM)
Functions for estimating generalized partial linear models

Generalized Partial Linear Models (GPLM)
Provides functions for estimating a generalized partial
linear model, a semiparametric variant of the generalized linear model
(GLM) which replaces the linear predictor by the sum of a linear
and a nonparametric function.

Various functions to draw miscellaneous plots
Gregory R. Warnes.  Includes R source code and documentation
contributed by
- Ben Bolker,
- Lodewijk Bonebakker,
- Bendix Carstensen,
- R. Gentleman,
- Kjetil Halvorsen,
- W. Huber
- Nitin Jain,
- Andy Liaw,
- Don MacQueen,
- M. Maechler,
- Arni Magnusson,
- Jim Rogers,
- Marc Schwartz,
- William Venables,
- Ian Wilson,
and others (let me know if I missed you!), as well as the Perl
modules
- "IO-stringy" (copyright 1996 by Eryq, and 1999,2001 by
ZeeGee Software Inc),
- OLE::Storage_Lite (copyright 2000,2001 by Kawai Takanori),
- Spreadsheet::ParseExcel (copyright 2000-2002 by Kawai
Takanori )
under the terms of the GNU General Public Licence (GPL).

Various R programming tools for plotting data

R (>= 2.10), gtools, gdata, stats, caTools, grid, KernSmooth
Gregory R. Warnes. Includes R source code and/or documentation
contributed by (in alphabetical order): Ben Bolker, Lodewijk
Bonebakker, Robert Gentleman, Wolfgang Huber Andy Liaw, Thomas
Lumley, Martin Maechler, Arni Magnusson, Steffen Moeller, Marc
Schwartz, Bill Venables

R (>= 3.0), gtools, gdata, stats, caTools, grid, KernSmooth,
MASS, datasets