WarpKriging: Create a WarpKriging model

In rlibkriging: Kriging Models using the 'libKriging' Library

Create a WarpKriging model

Description

Kriging with per-variable input warping. Each input dimension is independently transformed before the GP kernel is evaluated. Supports continuous, categorical, ordinal variables and joint deep kernel learning.

Usage

WarpKriging(
  y,
  X,
  warping,
  kernel = "gauss",
  regmodel = "constant",
  normalize = FALSE,
  optim = "BFGS+Adam",
  objective = "LL",
  parameters = NULL,
  noise = NULL
)

Arguments

y	numeric vector of observations (n)
X	numeric matrix of inputs (n x d)
warping	character vector of warp specifications, one per column of X. Use warp_*() helpers or plain strings: "none", "affine", "boxcox", "kumaraswamy", "neural_mono(8)", "mlp(16:8,2,selu)", "knots(3)", "knots(0.25:0.5:0.75)", "categorical(5,2)", "ordinal(4)".
kernel	covariance kernel: "gauss", "matern3_2", "matern5_2", "exp"
regmodel	trend: "constant", "linear", "quadratic"
normalize	logical; normalise continuous inputs?
optim	optimiser (currently only one bi-level strategy)
objective	"LL" (log-likelihood)
parameters	optional named list of tuning parameters, e.g. list(max_iter_adam = "300", adam_lr = "0.001", max_iter_bfgs = "50")
noise	Either a numeric vector of per-observation noise variances, or "nugget" to estimate a homogeneous nugget, or NULL (default) for noise-free interpolation.

Value

An S3 object of class "WarpKriging".

Examples

# Continuous with Kumaraswamy warping
X <- as.matrix(c(0.0, 0.25, 0.5, 0.75, 1.0))
f <- function(x) 1 - 1/2 * (sin(12*x)/(1+x) + 2*cos(7*x)*x^5 + 0.7)
y <- f(X)
k <- WarpKriging(y, X, warping = "kumaraswamy", kernel = "gauss")
print(k)

# Mixed: 1 continuous + 1 categorical (3 levels)
n <- 15
X_mix <- cbind(runif(n), rep(0:2, length.out = n))
y_mix <- sin(2 * pi * X_mix[, 1]) * c(1, 2, 0.5)[X_mix[, 2] + 1]
k2 <- WarpKriging(y_mix, X_mix,
         warping = c("mlp(16:8,2,selu)", "categorical(3,2)"),
         kernel = "matern5_2")

rlibkriging documentation built on May 14, 2026, 1:06 a.m.