Gaussian Process Fitting

---

Documentation for package ‘GauPro’ version 0.2.11

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

*.GauPro_kernel	Kernel product
+.GauPro_kernel	Kernel sum
arma_mult_cube_vec	Cube multiply over first dimension
corr_cubic_matrix_symC	Correlation Cubic matrix in C (symmetric)
corr_exponential_matrix_symC	Correlation Gaussian matrix in C (symmetric)
corr_gauss_dCdX	Correlation Gaussian matrix gradient in C using Armadillo
corr_gauss_matrix	Gaussian correlation
corr_gauss_matrixC	Correlation Gaussian matrix in C using Rcpp
corr_gauss_matrix_armaC	Correlation Gaussian matrix in C using Armadillo
corr_gauss_matrix_symC	Correlation Gaussian matrix in C (symmetric)
corr_gauss_matrix_sym_armaC	Correlation Gaussian matrix in C using Armadillo (symmetric)
corr_latentfactor_matrixmatrixC	Correlation Latent factor matrix in C (symmetric)
corr_latentfactor_matrix_symC	Correlation Latent factor matrix in C (symmetric)
corr_matern32_matrix_symC	Correlation Matern 3/2 matrix in C (symmetric)
corr_matern52_matrix_symC	Correlation Gaussian matrix in C (symmetric)
corr_orderedfactor_matrixmatrixC	Correlation ordered factor matrix in C (symmetric)
corr_orderedfactor_matrix_symC	Correlation ordered factor matrix in C (symmetric)
Cubic	Cubic Kernel R6 class
Exponential	Exponential Kernel R6 class
FactorKernel	Factor Kernel R6 class
GauPro	GauPro_selector
GauPro_base	Class providing object with methods for fitting a GP model
GauPro_Gauss	Corr Gauss GP using inherited optim
GauPro_Gauss_LOO	Corr Gauss GP using inherited optim
GauPro_kernel	Kernel R6 class
GauPro_kernel_beta	Beta Kernel R6 class
GauPro_kernel_model	Gaussian process model with kernel
GauPro_kernel_model_LOO	Corr Gauss GP using inherited optim
GauPro_trend	Trend R6 class
Gaussian	Gaussian Kernel R6 class
Gaussian_devianceC	Calculate the Gaussian deviance in C
Gaussian_hessianC	Calculate Hessian for a GP with Gaussian correlation
Gaussian_hessianCC	Gaussian hessian in C
Gaussian_hessianR	Calculate Hessian for a GP with Gaussian correlation
GowerFactorKernel	Gower factor Kernel R6 class
gpkm	Gaussian process regression model
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	Calculate gradfunc in optimization to speed up. NEEDS TO APERM dC_dparams Doesn't need to be exported, should only be useful in functions.
IgnoreIndsKernel	Kernel R6 class
kernel_cubic_dC	Derivative of cubic kernel covariance matrix in C
kernel_exponential_dC	Derivative of Matern 5/2 kernel covariance matrix in C
kernel_gauss_dC	Derivative of Gaussian kernel covariance matrix in C
kernel_latentFactor_dC	Derivative of covariance matrix of X with respect to kernel parameters for the Latent Factor Kernel
kernel_matern32_dC	Derivative of Matern 5/2 kernel covariance matrix in C
kernel_matern52_dC	Derivative of Matern 5/2 kernel covariance matrix in C
kernel_orderedFactor_dC	Derivative of covariance matrix of X with respect to kernel parameters for the Ordered Factor Kernel
kernel_product	Gaussian Kernel R6 class
kernel_sum	Gaussian Kernel R6 class
LatentFactorKernel	Latent Factor Kernel R6 class
Matern32	Matern 3/2 Kernel R6 class
Matern52	Matern 5/2 Kernel R6 class
OrderedFactorKernel	Ordered Factor Kernel R6 class
Periodic	Periodic Kernel R6 class
PowerExp	Power Exponential Kernel R6 class
predict.GauPro	Predict for class GauPro
print.summary.GauPro	Print summary.GauPro
RatQuad	Rational Quadratic Kernel R6 class
sqrt_matrix	Find the square root of a matrix
summary.GauPro	if (F) # Plot is automatically dispatched, same with print and format #' Plot for class GauPro #' #' @param x Object of class GauPro #' @param ... Additional parameters #' #' @return Nothing #' @export #' #' @examples #' n <- 12 #' x <- matrix(seq(0,1,length.out = n), ncol=1) #' y <- sin(2*pi*x) + rnorm(n,0,1e-1) #' gp <- GauPro(X=x, Z=y, parallel=FALSE) #' if (requireNamespace("MASS", quietly = TRUE)) #' plot(gp) #' #' plot.GauPro <- function(x, ...) x$plot(...) # if (x$D == 1) # x$cool1Dplot(...) # else if (x$D == 2) # x$plot2D(...) # else # # stop("No plot method for higher than 2 dimension") # x$plotmarginal() # Summary for GauPro object
trend_0	Trend R6 class
trend_c	Trend R6 class
trend_LM	Trend R6 class
Triangle	Triangle Kernel R6 class
White	White noise Kernel R6 class