The R package `robnptests`

contains different robust and
nonparametric tests for the two-sample location problem. The tests allow
for comparisons of either the location or the scale parameters of two
random samples.

The released version of `robnptests`

can be installed from
CRAN with

`install.packages("robnptests")`

To install the development version, the `devtools`

package
is required:

```
if (!require("devtools")) {
install.packages("devtools")
}
::install_github("s-abbas/robnptests")
devtools
library(robnptests)
```

The robust and nonparametric tests in this R package follow the construction principle of the popular t-test: A robust estimate for the location difference of the two samples is divided by a robust estimate of scale. The p-values can either be computed using the permutation principle, the randomization principle, or the asymptotic distribution of the estimators. If the principle to compute the p-value is not specified by the user, it will be selected automatically depending on the sample size. The functions used to compute the location and scale estimates are also made available to the user.

The following list shows the currently implemented tests in the package:

- tests based on the median (
`med_test`

), the one-sample Hodges-Lehmann estimator (`hl1_test`

), and the two-sample Hodges-Lehmann estimator (`hl2_test`

), scaled by robust estimators - Yuen’s t-test (
`trimmed_test`

) - tests based on the Huber-, Hampel- or Bisquare-M-estimator
(
`m_test`

).

Even though the test statistics compare location estimates of the
samples, they can be used to identify scale differences. This is
achieved by setting the argument `scale.test = TRUE`

, with
which the observations in the samples are log-transformed so that scale
differences between the original samples correspond to location
differences in the transformed samples.

Details on the tests and references can be found on the help pages of
the functions and the vignette `vignette("robnptests")`

.

```
set.seed(121)
<- rnorm(50); y <- rnorm(50)
x
hl2_test(x, y, method = "asymptotic")
#
# Asymptotic test based on HL2-estimator
#
# data: x and y
# D = 1.0916, p-value = 0.275
# alternative hypothesis: true location shift is not equal to 0
# sample estimates:
# HL2 of x and y
# 0.2048249
```

```
hl2_test(x, y, method = "asymptotic", scale.test = TRUE)
# Asymptotic test based on HL2-estimator
#
# data: x and y
# S = -0.24094, p-value = 0.8096
# alternative hypothesis: true ratio of variances is not equal to 1
# sample estimates:
# HL2 of log(x^2) and log(y^2)
# -0.1040422
```

We are grateful for any contribution to the further development of the R package. If you experience any problems using the package or have suggestions for new features, please open an issue in the issue tracker. Please consult our contribution guidelines before submitting a pull request.