changes requested by CRAN

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: ddst
 Title: Data Driven Smooth Tests
-Version: 1.6.9
+Version: 1.6.10
 Authors@R: c(person("Przemyslaw", "Biecek", email = "przemyslaw.biecek@gmail.com", role = c("aut", "cre")),
              person("Teresa", "Ledwina", email = "ledwina@impan.pl", role = c("aut")),
              person("Grzegorz", "Wylupek", role = c("aut"))
@@ -12,7 +12,6 @@ Description: Smooth tests are data driven (alternative hypothesis is dynamically
     and uniform distribution.
     Among nonparametric tests there are tests for stochastic dominance,
     k-sample test, test with umbrella alternatives and test for change-point problems.
-    Find a detailed description for these tests at TODO.
 License: GPL-2
 Encoding: UTF-8
 LazyData: true
@@ -26,7 +25,7 @@ Imports:
 Suggests:
     rmutil,
     knitr
-RoxygenNote: 7.1.1
 URL: https://pbiecek.github.io/ddst/
 BugReports: https://github.com/pbiecek/ddst/issues
 VignetteBuilder: knitr
+RoxygenNote: 7.2.3

NEWS.md

@@ -1,8 +1,7 @@
-ddst.normbounded.test - już nie ma argumentu B/nr ponieważ p-wartość liczona jest analitycznie
-argument B zamieniony na nr
-domyślna wartość B zamieniona z 10000 na 100000
-compute.p zamienione z FALSE na TRUE
+ddst 1.6.10
+----------------
 
+* changes requested by CRAN
 
 ddst 1.6.7
 ----------------

R/ddst.evd.test.R

@@ -49,6 +49,7 @@
 #' # for given vector of 19 numbers
 #' z <- c(13.41, 6.04, 1.26, 3.67, -4.54, 2.92, 0.44, 12.93, 6.77, 10.09,
 #'       4.10, 4.04, -1.97, 2.17, -5.38, -7.30, 4.75, 5.63, 8.84)
+#' \dontrun{
 #' t <- ddst.evd.test(z, compute.p = TRUE, d.n = 10)
 #' t
 #' plot(t)
@@ -64,6 +65,7 @@
 #' t <- ddst.evd.test (x, compute.p = TRUE, d.n = 10)
 #' t
 #' plot(t)
+#' }
 #' @keywords htest
 `ddst.evd.test` <-
   function(x,

R/ddst.exp.test.R

@@ -32,6 +32,7 @@
 #' set.seed(7)
 #' # H0 is true
 #' z <- rexp(80,4)
+#' \dontrun{
 #' t <- ddst.exp.test (z, compute.p = TRUE, d.n = 10)
 #' t
 #' plot(t)
@@ -41,7 +42,7 @@
 #' (t = ddst.exp.test (z, compute.p = TRUE, d.n = 10))
 #' t$p.value
 #' plot(t)
-#'
+#' }
 #' @keywords htest
 #' @aliases ddst.exp.Nk
 #'

R/ddst.forstochdom.test.R

@@ -17,7 +17,7 @@
 #' @param compute.p a logical value indicating whether to compute a p-value or not
 #' @param compute.cv a logical value indicating whether to compute a critical value corresponding to the significance level alpha or not
 #'
-#' @references Nonparametric tests for stochastic ordering. Ledwina and Wyłupek (2012) \url{https://doi.org/10.1007/s11749-011-0278-7}
+#' @references Nonparametric tests for stochastic ordering. Ledwina and Wyłupek (2012) <doi:10.1007/s11749-011-0278-7>
 #' @export
 #' @examples
 #' set.seed(7)

R/ddst.ksample.test.R

@@ -53,7 +53,7 @@
 ##      n <- c(length(x),
 ##             sapply(list(...), length))
 ##    }
-    coord = ddst.ksample.Nk(x.vector, n, d_N = d.n, c = c)
+    coord = ddst.ksample.Nk(x.vector, n, d_N = d.N, c = c)
 
     l = coord$score
     attr(l, "names") = "WT"
@@ -67,8 +67,8 @@
     result$data.name = paste(paste(as.character(substitute(x)), collapse = ""),
                              ", c: ",
                              c,
-                             ", d.n: ",
-                             d.n,
+                             ", d.N: ",
+                             d.N,
                              sep = "")
     class(result) = c("htest", "ddst.test", "ddst.ksample.test")
 

R/ddst.normunbounded.test.R

@@ -15,7 +15,6 @@
 #' @param nr an integer specifying the number of runs for a critical value computation
 #' @param compute.cv a logical value indicating whether to compute a critical value corresponding to the significance level alpha or not
 #' @param n sample size
-#' @param ... further arguments
 #'
 #' @export
 #'
@@ -31,6 +30,7 @@
 #' # let's look on first 20 coordinates
 #' d.n <- 20
 #'
+#' \dontrun{
 #' # calculate finite sample corrections
 #' # see 6.2. Composite null hypothesis H in the appendix materials
 #' e.v <- ddst.normunbounded.bias(n = length(z))
@@ -68,6 +68,7 @@
 #' t <- ddst.normubounded.test(z, d.n, e.v$e.0, e.v$v.0, r.alpha, s)
 #' t
 #' plot(t)
+#' }
 #' @rdname ddst.normunbounded.test
 #'
 `ddst.normubounded.test` <-

R/ddst.twosample.test.R

@@ -8,7 +8,7 @@
 #' @param y a (non-empty) numeric vector of data
 #' @param d.N an integer specifying the maximum dimension considered, only for advanced users
 #' @param c a calibrating parameter in the penalty in the model selection rule
-#' @param nr an integer specifying the number of runs for a p-value and a critical value computation if any
+#' @param B an integer specifying the number of runs for a p-value and a critical value computation if any
 #' @param compute.p a logical value indicating whether to compute a p-value or not
 #' @param alpha a significance level
 #' @param compute.cv a logical value indicating whether to compute a critical value corresponding to the significance level alpha or not
@@ -41,7 +41,7 @@
            compute.p = TRUE,
            alpha = 0.05,
            compute.cv = TRUE) {
-    res <- ddst.ksample.test(list(x, y), d.N = d.N, c = c, nr = nr, compute.p = compute.p,
+    res <- ddst.ksample.test(list(x, y), d.N = d.N, c = c, nr = B, compute.p = compute.p,
                              alpha = alpha, compute.cv = compute.cv)
     res$coordinates <- res$coordinates[1,]
     res$method <- "Data Driven Smooth Test for Two-Sample Problem"

R/ddst.uniform.test.R

@@ -36,6 +36,7 @@
 #' set.seed(7)
 #' # H0 is true
 #' z <- runif(80)
+#' \dontrun{
 #' t <- ddst.uniform.test(z, compute.p = TRUE, d.n = 10)
 #' t
 #' plot(t)
@@ -51,6 +52,7 @@
 #' (t <- ddst.uniform.test(z, compute.p = TRUE, d.n = 10))
 #' t$p.value
 #' plot(t)
+#' }
 #' @keywords htest
 `ddst.uniform.test` <-
   function(x,