initial commit

.Rbuildignore

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+test.R
+^.*\.Rproj$
+^\.Rproj\.user$
+^.*tar.gz$
+.gitignore
+.gitmodules
+README.md
+test_all.R
+.travis.yml
+appveyor.yml
+examples
+man-roxygen
+benchmark.R
+todo
+benchmark
+cran-comments.md
+^docs$
+bottleneck
+data-raw

.gitignore

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+.Rproj.user
+.Rhistory
+.RData
+ecr.Rcheck
+ecr.Rproj
+ecr_*.tar.gz
+playground.R
+test_specific.R
+Rplots.pdf
+*.o
+*.so
+inst/doc

DESCRIPTION

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+Package: sampling
+Title: Sampling Plans in R
+Description: Calculation of star discrepancy of a point set and methods for pseudo-random and quasi-random point set generation: uniform random sampling, latin hypercube sampling, Sobol and Halton sequences.
+Version: 1.0.0.0000
+Encoding: UTF-8
+Date: 2019-12-20
+Authors@R: c(
+    person("Jakob", "Bossek", email = "j.bossek@gmail.com", role = c("aut", "cre", "cph")),
+    person("Michael H.", "Buselli", role = c("ctb", "cph")))
+Maintainer: Jakob Bossek <j.bossek@gmail.com>
+License: GPL-3 | file LICENSE
+URL: https://github.com/jakobbossek/sampling
+BugReports: https://github.com/jakobbossek/sampling/issues
+Depends:
+    R (>= 2.10),
+    BBmisc (>= 1.6)
+Imports:
+    checkmate (>= 1.1),
+    randtoolbox,
+    lhs
+Suggests:
+    testthat (>= 0.9.1)
+ByteCompile: yes
+LazyData: yes
+RoxygenNote: 6.1.1

NAMESPACE

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+# Generated by roxygen2: do not edit by hand
+
+export(stardiscrepancy)
+import(BBmisc)
+import(checkmate)
+useDynLib(sampling, .registration = TRUE)

R/design.R

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+#' @title
+#' Create a design.
+#'
+#' @description
+#' To be written ...
+#'
+#' @param n [\code{integer(1)}]\cr
+#'   Number of design points (rows).
+#' @param k [\code{integer(1)}]\cr
+#'   Number of variables (columns).
+#' @param l [\code{numeric}]\cr
+#'   Lower bound for design points. Either a single numeric value
+#'   or a vector of length \code{k}.
+#'   Default is 0.
+#' @param u [\code{numeric}]\cr
+#'   Upper bound for design points. Either a single numeric value
+#'   or a vector of length \code{k}.
+#'   Default is 1.
+#' @param method [\code{character(1)}]\cr
+#'   Name of method used to generate the design. Possible values are
+#'   \describe{
+#'     \item{uniform}{Uniform random sampling.}
+#'     \item{improvedlhs}{Delegate to \code{\link[lhs]{improvedLHS}}.}
+#'     \item{maximinlhs}{Delegate to \code{\link[lhs]{maximinLHS}}.}
+#'     \item{geneticlhs}{Delegate to \code{\link[lhs]{geneticLHS}}.}
+#'     \item{halton}{Delegate to \code{\link[randtoolbox]{halton}}.}
+#'     \item{sobol}{Delegate to \code{\link[randtoolbox]{sobol}}.}
+#'   }
+#' @param as.df [\code{logical(1)}]\cr
+#'   Return points as data frame?
+#'   Default is \code{TRUE}.
+#' @param ... [any]\cr
+#'   Further parameters passed down to generator if applicable.
+#' @return Design points as a matrix or data.frame (see parameter \code{as.df}).
+#' @examples
+#' methods = getSupportedMethods()
+#' designs = lapply(methods, function(method) {
+#'   design(n = 100, k = 2, method = method, l = -5, upper = c(5, 10), as.df = FALSE)
+#' })
+#'
+#' # pass down options to generator
+#' d = design(n = 50, k = 4, method = "sobol", scrambling = 2, seed = 123)
+#' print(d)
+#' @export
+design = function(n, k, method, l = 0, u = 1, as.df = TRUE, ...) {
+  n = checkmate::asInt(n, lower = 2L, na.ok = FALSE)
+  k = checkmate::asInt(k, lower = 1L, na.ok = FALSE)
+  checkmate::assertChoice(method, choices = getSupportedMethods())
+  checkmate::assertFlag(as.df)
+
+  if (length(l) == 1L)
+    l = rep(l, k)
+  if (length(u) == 1L)
+    u = rep(u, k)
+
+  checkmate::assertNumeric(l, len = k, any.missing = FALSE, all.missing = FALSE)
+  checkmate::assertNumeric(u, len = k, any.missing = FALSE, all.missing = FALSE)
+
+  if (any(l >= u))
+    BBmisc::stopf("[sampling::design] Lower bounds must be strictly lower than upper bounds.")
+
+  des = if (method == "uniform") {
+    matrix(runif(n * k), nrow = n)
+  } else if (method == "improvedlhs") {
+    lhs::improvedLHS(n = n, k = k, ...)
+  } else if (method == "maximinlhs") {
+    lhs::maximinLHS(n = n, k = k, ...)
+  } else if (method == "improvedlhs") {
+    lhs::geneticLHS(n = n, k = k, ...)
+  } else if (method == "halton") {
+    randtoolbox::halton(n = n, dim = k, ...)
+  } else if (method == "sobol") {
+    randtoolbox::sobol(n = n, dim = k, scrambling = 3, seed = ceiling(runif(1L, min = 1, max = 1000000)))
+  } else {
+    BBmisc::stopf("[sampling::design] Unsupported method '%s'.", method)
+  }
+
+  # linear transformation
+  des = t(t(des) * (u - l) + l)
+
+  if (as.df) {
+    des = as.data.frame(des)
+    colnames(des) = paste0("x", seq_len(k))
+  }
+
+  return(des)
+}
+
+#' Get character vector of supported design generators.
+#' @export
+getSupportedMethods = function() {
+  c("uniform", "improvedlhs", "maximinlhs", "geneticlhs" "halton", "sobol")
+}

R/stardiscrepancy.R

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+#' @title
+#' Calculate star discrepancy of a set of points.
+#'
+#' @description
+#' To be written ...
+#'
+#' @param x [\code{matrix(n, d)}]\cr
+#'   An \eqn{n \times d} matrix where \eqn{n} is the number of points and \eqn{d}
+#'   is the dimension of the search space.
+#' @return [\numeric(1)]\cr Star discrepancy of \code{x}.
+#' @export
+stardiscrepancy = function(x) {
+  if (checkmate::testDataFrame(x))
+    x = unname(as.matrix(x))
+
+  checkmate::assertMatrix(x, min.rows = 2L, min.cols = 2L, any.missing = FALSE, all.missing = FALSE, mode = "numeric")
+  return(.Call("starDiscrepancyC", t(x)))
+}

R/zzz.R

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+#' @import BBmisc
+#' @import checkmate
+#' @useDynLib sampling, .registration = TRUE
+NULL

lhs.csv

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