Example Datasets for a Learning Guide to R
c(person("Remko", "Duursma", email = "remkoduursma@gmail.com", role = c("aut", "cre")), person("Jeff","Powell", role="ctb"))
A largish collection of example datasets, including several classics. Many of
these datasets are well suited for regression, classification, and visualization.
Remko Duursma [aut, cre],
Jeff Powell [ctb]

A set of methods for longitudinal data objects.
A very simple implementation of a class for longitudinal data.  See my paper in the DSC-2001 proceedings.

A very simple implementation of a class for longitudinal
data.  See my paper in the DSC-2001 proceedings.
Orphaned on 2013-12-20 as requested by the maintainer
<rgentlem@gmail.com>.

A very simple implementation of a class for
longitudinal data.

A Set of Methods for Longitudinal Data Objects

Fit Sex-Specific Life History Models with Missing
Classifications
c(
person("Coilin", "Minto", email = "coilin.minto@gmit.ie", role = c("aut", "cre")),
person("John", "Hinde", email = "john.hinde@nuigalway.ie", role = c("aut")),
person("Rui", "Coelho", email = " rpcoelho@ualg.pt", role = c("ctb", "dtc")))
Coilin Minto [aut, cre], John Hinde [aut], Rui Coelho [ctb, dtc]
Fits sex-specific life-history models for fish and other taxa where some of the individuals have unknown sex.

This package provides a number of methods for creating and augmenting Latin Hypercube Samples

This package provides a number of methods for creating and
augmenting Latin Hypercube Samples

Provides a number of methods for creating and augmenting Latin Hypercube Samples.

person(given = "Rob",
family = "Carnell",
role = c("aut", "cre"),
email = "bertcarnell@gmail.com")
testthat, DoE.base, knitr, rmarkdown, covr

Provides a number of methods for creating and augmenting Latin Hypercube Samples and Orthogonal Array Latin Hypercube Samples.

testthat, assertthat, DoE.base, knitr, rmarkdown, covr

testthat, assertthat, DoE.base, knitr, rmarkdown

Locally Induced Gaussian Process Regression
c(person(c("D.", "Austin"), "Cole", role = c("aut", "cre"),
email = "austin.cole8@vt.edu"),
person(c("Ryan", "B"), "Christianson", role = "cph"),
person(c("Robert", "B."), "Gramacy", role = "cph",
email = "rbg@vt.edu"))
Performs locally induced approximate GP regression for large computer experiments and spatial datasets following Cole D.A., Christianson, R., Gramacy, R.B. (2021) Statistics and Computing, 31(3), 1-21, <arXiv:2008.12857>. The approximation is based on small local designs combined with a set of inducing points (latent design points) for predictions at particular inputs. Parallelization is supported for generating predictions over an immense out-of-sample testing set. Local optimization of the inducing points design is provided based on variance-based criteria. Inducing point template schemes, including scaling of space-filling designs, are also provided.
D. Austin Cole [aut, cre],
Ryan B Christianson [cph],
Robert B. Gramacy [cph]