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This R package implements several non-parametric tests in chapters 1-5 of Higgins (2004), including tests for one sample, two samples, k samples, paired comparisons, blocked designs, trends and association. Built with Rcpp for efficiency and R6 for flexible, object-oriented design, it provides a unified framework for performing or creating custom permutation tests.
Install the stable version from CRAN:
install.packages("LearnNonparam")
Install the development version from Github:
# install.packages("remotes")
::install_github("qddyy/LearnNonparam") remotes
library(LearnNonparam)
options(LearnNonparam.pmt_progress = TRUE)
Construct a test object
<- Wilcoxon$new(n_permu = 1e6) t
pmt
(permutation test)
wrapper# recommended for a unified API
<- pmt("twosample.wilcoxon", n_permu = 1e6) t
Provide it with samples
set.seed(-1)
$test(rnorm(10, 1), rnorm(10, 0)) t
Check the results
$statistic t
$p_value t
options(digits = 3)
$print() t
::theme_set(ggplot2::theme_minimal())
ggplot2
$plot(style = "ggplot2", binwidth = 1) t
Modify some settings and observe the change
$type <- "asymp"
t$p_value t
pmts()
for tests implemented in this package.
key | class | test |
---|---|---|
onesample.quantile | Quantile | Quantile Test |
onesample.cdf | CDF | Inference on Cumulative Distribution Function |
twosample.difference | Difference | Two-Sample Test Based on Mean or Median |
twosample.wilcoxon | Wilcoxon | Two-Sample Wilcoxon Test |
twosample.scoresum | ScoreSum | Two-Sample Test Based on Sum of Scores |
twosample.ansari | AnsariBradley | Ansari-Bradley Test |
twosample.siegel | SiegelTukey | Siegel-Tukey Test |
twosample.rmd | RatioMeanDeviance | Ratio Mean Deviance Test |
twosample.ks | KolmogorovSmirnov | Two-Sample Kolmogorov-Smirnov Test |
ksample.oneway | OneWay | One-Way Test for Equal Means |
ksample.kw | KruskalWallis | Kruskal-Wallis Test |
ksample.jt | JonckheereTerpstra | Jonckheere-Terpstra Test |
multcomp.studentized | Studentized | Multiple Comparison Based on Studentized Statistic |
paired.sign | Sign | Two-Sample Sign Test |
paired.difference | PairedDifference | Paired Comparison Based on Differences |
rcbd.oneway | RCBDOneWay | One-Way Test for Equal Means in RCBD |
rcbd.friedman | Friedman | Friedman Test |
rcbd.page | Page | Page Test |
association.corr | Correlation | Test for Association Between Paired Samples |
table.chisq | ChiSquare | Chi-Square Test on Contingency Table |
define_pmt
allows users to define new permutation tests.
Take the two-sample Cramér-Von Mises test as an example:
<- define_pmt(
t # this is a two-sample permutation test
inherit = "twosample",
statistic = function(x, y) {
# (optional) pre-calculate certain constants that remain invariant during permutation
<- length(x)
n_x <- length(y)
n_y <- seq_len(n_x) / n_x
F_x <- seq_len(n_y) / n_y
G_y # return a closure to calculate the test statistic
function(x, y) {
<- sort.int(x)
x <- sort.int(y)
y <- approxfun(x, F_x, "constant", 0, 1)
F <- approxfun(y, G_y, "constant", 0, 1)
G sum(c(F_x - G(x), G_y - F(y))^2)
}
},# reject the null hypothesis when the test statistic is large
rejection = "r",
name = "Two-Sample Cramér-Von Mises Test",
alternative = "samples are from different continuous distributions"
)
$test(rnorm(10), runif(10))$print() t
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.