This package is considered a duplicate. The official version of this package is found at:https://collinerickson.r-universe.dev/GauPro

Package: GauPro 0.2.13

Collin Erickson

GauPro: Gaussian Process Fitting

Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) <doi:10.1016/j.ejor.2017.10.002>.

Authors:Collin Erickson [aut, cre]

GauPro_0.2.13.tar.gz
GauPro_0.2.13.tar.gz(r-4.5-noble)GauPro_0.2.13.tar.gz(r-4.4-noble)
GauPro_0.2.13.tgz(r-4.4-emscripten)GauPro_0.2.13.tgz(r-4.3-emscripten)
GauPro.pdf |GauPro.html✨
GauPro/json (API)
NEWS

# Install 'GauPro' in R:

install.packages('GauPro', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/collinerickson/gaupro/issues

Uses libs:

openblas– Optimized BLAS
c++– GNU Standard C++ Library v3
openmp– GCC OpenMP (GOMP) support library

openblas cpp openmp

5.15 score 1 packages 94 scripts 456 downloads 1 mentions 71 exports 63 dependencies

Last updated 2 months agofrom:26480aa5c8. Checks:OK: 2. Indexed: no.

Target	Result	Date
Doc / Vignettes	OK	Nov 26 2024
R-4.5-linux-x86_64	OK	Nov 26 2024

Exports:arma_mult_cube_vec corr_cubic_matrix_symC corr_exponential_matrix_symC corr_gauss_dCdX corr_gauss_matrix corr_gauss_matrix_armaC corr_gauss_matrix_sym_armaC corr_gauss_matrix_symC corr_gauss_matrixC corr_latentfactor_matrix_symC corr_latentfactor_matrixmatrixC corr_matern32_matrix_symC corr_matern52_matrix_symC corr_orderedfactor_matrix_symC corr_orderedfactor_matrixmatrixC Cubic Exponential FactorKernel GauPro GauPro_base GauPro_Gauss GauPro_Gauss_LOO GauPro_kernel_model GauPro_kernel_model_LOO Gaussian Gaussian_devianceC Gaussian_hessianC Gaussian_hessianCC Gaussian_hessianR GowerFactorKernel gpkm gradfuncarray gradfuncarrayR IgnoreIndsKernel k_Cubic k_Exponential k_FactorKernel k_Gaussian k_GowerFactorKernel k_IgnoreIndsKernel k_LatentFactorKernel k_Matern32 k_Matern52 k_OrderedFactorKernel k_Periodic k_PowerExp k_RatQuad k_Triangle k_White kernel_cubic_dC kernel_exponential_dC kernel_gauss_dC kernel_latentFactor_dC kernel_matern32_dC kernel_matern52_dC kernel_orderedFactor_dC kernel_product kernel_sum LatentFactorKernel Matern32 Matern52 OrderedFactorKernel Periodic PowerExp RatQuad sqrt_matrix trend_0 trend_c trend_LM Triangle White

Dependencies:base64enc bslib cachem cli colorspace cpp11 digest dplyr evaluate fansi farver fastmap fontawesome fs generics ggplot2 glue gtable highr htmltools isoband jquerylib jsonlite knitr labeling lattice lbfgs lifecycle magrittr MASS Matrix memoise mgcv mime mixopt munsell nlme numDeriv pillar pkgconfig purrr R6 rappdirs RColorBrewer Rcpp RcppArmadillo rlang rmarkdown sass scales splitfngr stringi stringr tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun yaml

A Guide to the GauPro R package

Collin Erickson

Rendered fromGauPro.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2022-11-14
Started: 2017-08-17

Derivatives for estimating Gaussian process parameters

Collin Erickson

Rendered fromderivatives.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2024-09-27
Started: 2017-08-17

Introduction to Gaussian Processes

Collin Erickson

Rendered fromIntroductionToGPs.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2023-02-27
Started: 2017-08-17

Leave-one-out cross-validation and error correction

Collin Erickson

Rendered fromCrossValidationErrorCorrection.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2022-11-14
Started: 2017-08-17

Spatial derivatives of Gaussian process models

Collin Erickson

Rendered fromsurface_derivatives.Rmdusingknitr::rmarkdownon Nov 26 2024.

Last update: 2022-11-14
Started: 2017-08-17

Citation

Readme and manuals

Help Manual

Help page	Topics
Kernel product	*.GauPro_kernel
Kernel sum	+.GauPro_kernel
Cube multiply over first dimension	arma_mult_cube_vec
Correlation Cubic matrix in C (symmetric)	corr_cubic_matrix_symC
Correlation Gaussian matrix in C (symmetric)	corr_exponential_matrix_symC
Correlation Gaussian matrix gradient in C using Armadillo	corr_gauss_dCdX
Gaussian correlation	corr_gauss_matrix
Correlation Gaussian matrix in C using Armadillo	corr_gauss_matrix_armaC
Correlation Gaussian matrix in C using Armadillo (symmetric)	corr_gauss_matrix_sym_armaC
Correlation Gaussian matrix in C (symmetric)	corr_gauss_matrix_symC
Correlation Gaussian matrix in C using Rcpp	corr_gauss_matrixC
Correlation Latent factor matrix in C (symmetric)	corr_latentfactor_matrix_symC
Correlation Latent factor matrix in C (symmetric)	corr_latentfactor_matrixmatrixC
Correlation Matern 3/2 matrix in C (symmetric)	corr_matern32_matrix_symC
Correlation Gaussian matrix in C (symmetric)	corr_matern52_matrix_symC
Correlation ordered factor matrix in C (symmetric)	corr_orderedfactor_matrix_symC
Correlation ordered factor matrix in C (symmetric)	corr_orderedfactor_matrixmatrixC
Cubic Kernel R6 class	Cubic k_Cubic
Exponential Kernel R6 class	Exponential k_Exponential
Factor Kernel R6 class	FactorKernel k_FactorKernel
GauPro_selector	GauPro
Class providing object with methods for fitting a GP model	GauPro_base
Corr Gauss GP using inherited optim	GauPro_Gauss
Corr Gauss GP using inherited optim	GauPro_Gauss_LOO
Kernel R6 class	GauPro_kernel
Beta Kernel R6 class	GauPro_kernel_beta
Gaussian process model with kernel	GauPro_kernel_model
Corr Gauss GP using inherited optim	GauPro_kernel_model_LOO
Trend R6 class	GauPro_trend
Gaussian Kernel R6 class	Gaussian k_Gaussian
Calculate the Gaussian deviance in C	Gaussian_devianceC
Calculate Hessian for a GP with Gaussian correlation	Gaussian_hessianC
Gaussian hessian in C	Gaussian_hessianCC
Calculate Hessian for a GP with Gaussian correlation	Gaussian_hessianR
Gower factor Kernel R6 class	GowerFactorKernel k_GowerFactorKernel
Gaussian process regression model	gpkm
Calculate gradfunc in optimization to speed up. NEEDS TO APERM dC_dparams Doesn't need to be exported, should only be useful in functions.	gradfuncarray
Calculate gradfunc in optimization to speed up. NEEDS TO APERM dC_dparams Doesn't need to be exported, should only be useful in functions.	gradfuncarrayR
Kernel R6 class	IgnoreIndsKernel k_IgnoreIndsKernel
Derivative of cubic kernel covariance matrix in C	kernel_cubic_dC
Derivative of Matern 5/2 kernel covariance matrix in C	kernel_exponential_dC
Derivative of Gaussian kernel covariance matrix in C	kernel_gauss_dC
Derivative of covariance matrix of X with respect to kernel parameters for the Latent Factor Kernel	kernel_latentFactor_dC
Derivative of Matern 5/2 kernel covariance matrix in C	kernel_matern32_dC
Derivative of Matern 5/2 kernel covariance matrix in C	kernel_matern52_dC
Derivative of covariance matrix of X with respect to kernel parameters for the Ordered Factor Kernel	kernel_orderedFactor_dC
Gaussian Kernel R6 class	kernel_product
Gaussian Kernel R6 class	kernel_sum
Latent Factor Kernel R6 class	k_LatentFactorKernel LatentFactorKernel
Matern 3/2 Kernel R6 class	k_Matern32 Matern32
Matern 5/2 Kernel R6 class	k_Matern52 Matern52
Ordered Factor Kernel R6 class	k_OrderedFactorKernel OrderedFactorKernel
Periodic Kernel R6 class	k_Periodic Periodic
Power Exponential Kernel R6 class	k_PowerExp PowerExp
Predict for class GauPro	predict.GauPro
Print summary.GauPro	print.summary.GauPro
Rational Quadratic Kernel R6 class	k_RatQuad RatQuad
Find the square root of a matrix	sqrt_matrix
Summary for GauPro object	summary.GauPro
Trend R6 class	trend_0
Trend R6 class	trend_c
Trend R6 class	trend_LM
Triangle Kernel R6 class	k_Triangle Triangle
White noise Kernel R6 class	k_White White

Package: GauPro 0.2.13

GauPro: Gaussian Process Fitting

A Guide to the GauPro R package

Derivatives for estimating Gaussian process parameters

Introduction to Gaussian Processes

Leave-one-out cross-validation and error correction

Spatial derivatives of Gaussian process models

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)