person("Tatsushi", "Oka", , "oka.econ@gmail.com", role = c("aut", "cre")),
person("Oliver", "Linton", role = "ctb"),
person("Yoon-Jae", "Whang", role = "ctb")
Description: Estimation and inference methods for the cross-quantilogram.
The cross-quantilogram is a measure of nonlinear dependence between
two variables, based on either unconditional or conditional quantile
functions.  The cross-quantilogram can be considered as an extension
of the correlogram, which is a correlation function over multiple lag
periods and mainly focuses on linear dependency.  One can use the
cross-quantilogram to detect the presence of directional
predictability from one time series to another.  This package provides
a statistical inference method based on the stationary bootstrap.  See
univariate time series analysis and Han, Linton, Oka and Whang (2016)

Description: Integration of the 'units' and 'errors' packages for a complete quantity
calculus system for R vectors, matrices and arrays, with automatic propagation,
conversion, derivation and simplification of magnitudes and uncertainties.
Suggests: tibble, pillar, dplyr, tidyr, testthat, knitr, rmarkdown

Description: Integration of the 'units' and 'errors' packages for a complete
quantity calculus system for R vectors, matrices and arrays, with automatic
propagation, conversion, derivation and simplification of magnitudes and
uncertainties. Documentation about 'units' and 'errors' is provided in the
papers by Pebesma, Mailund & Hiebert (2016, <doi:10.32614/RJ-2016-061>) and
by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in
those packages as vignettes; see 'citation("quantities")' for details.

Suggests: pillar, dplyr (>= 1.0.0), vctrs (>= 0.3.1), tidyr, testthat,

Title: Quantile Kriging for Stochastic Simulations with Replication
Authors@R: c(person(c("Kevin", "R."), "Quinlan", role = c("aut", "cre"),
person(c("Jim", "R."), "Leek", role = "aut"),
person("Lawrence Livermore National Security", role = "cph"))
Lawrence Livermore National Security [cph]
Maintainer: Kevin R. Quinlan <quinlan5@llnl.gov>
Description: A re-implementation of quantile kriging. Quantile kriging was described by Plumlee and Tuo (2014) <doi:10.1080/00401706.2013.860919>.  With computational savings when dealing with replication from the recent paper by Binois, Gramacy, and Ludovski (2018) <doi:10.1080/10618600.2018.1458625> it is now possible to apply quantile kriging to a wider class of problems.  In addition to fitting the model, other useful tools are provided such as the ability to automatically perform leave-one-out cross validation.