coin-package | R Documentation |

The coin package provides an implementation of a general framework for
conditional inference procedures commonly known as *permutation tests*.
The framework was developed by Strasser and Weber (1999) and is based on a
multivariate linear statistic and its conditional expectation, covariance and
limiting distribution. These results are utilized to construct tests of
independence between two sets of variables.

The package does not only provide a flexible implementation of the abstract
framework, but also provides a large set of convenience functions implementing
well-known as well as lesser-known classical and non-classical test procedures
within the framework. Many of the tests presented in prominent text books,
such as Hollander and Wolfe (1999) or Agresti (2002), are immediately
available or can be implemented without much effort. Examples include linear
rank statistics for the two- and `K`

-sample location and scale problem
against ordered and unordered alternatives including post-hoc tests for
arbitrary contrasts, tests of independence for contingency tables, two- and
`K`

-sample tests for censored data, tests of independence between two
continuous variables as well as tests of marginal homogeneity and symmetry.
Approximations of the exact null distribution via the limiting distribution or
conditional Monte Carlo resampling are available for every test procedure,
while the exact null distribution is currently available for univariate
two-sample problems only.

The salient parts of the Strasser-Weber framework are elucidated by Hothorn
*et al.* (2006) and a thorough description of the software implementation
is given by Hothorn *et al.* (2008).

This package is authored by

Torsten Hothorn <Torsten.Hothorn@R-project.org>,

Kurt Hornik <Kurt.Hornik@R-project.org>,

Mark A. van de Wiel <Mark.vdWiel@vumc.nl>,

Henric Winell <Henric.Winell@statistics.uu.se> and

Achim Zeileis <Achim.Zeileis@R-project.org>.

Agresti, A. (2002). *Categorical Data Analysis*, Second Edition.
Hoboken, New Jersey: John Wiley & Sons.

Hollander, M. and Wolfe, D. A. (1999). *Nonparametric Statistical
Methods*, Second Edition. New York: John Wiley & Sons.

Hothorn, T., Hornik, K., van de Wiel, M. A. and Zeileis, A. (2006). A Lego
system for conditional inference. *The American Statistician*
**60**(3), 257–263. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/000313006X118430")}

Hothorn, T., Hornik, K., van de Wiel, M. A. and Zeileis, A. (2008).
Implementing a class of permutation tests: The coin package. *Journal of
Statistical Software* **28**(8), 1–23. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v028.i08")}

Strasser, H. and Weber, C. (1999). On the asymptotic theory of permutation
statistics. *Mathematical Methods of Statistics* **8**(2), 220–250.

```
## Not run:
## Generate doxygen documentation if you are interested in the internals:
## Download source package into a temporary directory
tmpdir <- tempdir()
tgz <- download.packages("coin", destdir = tmpdir, type = "source")[2]
## Extract contents
untar(tgz, exdir = tmpdir)
## Run doxygen (assuming it is installed)
wd <- setwd(file.path(tmpdir, "coin"))
system("doxygen inst/doxygen.cfg")
setwd(wd)
## Have fun!
browseURL(file.path(tmpdir, "coin", "inst",
"documentation", "html", "index.html"))
## End(Not run)
```

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