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Performs nonparametric analysis of longitudinal data in factorial experiments. Longitudinal data are those which are collected from the same subjects over time, and they frequently arise in biological sciences. Nonparametric methods do not require distributional assumptions, and are applicable to a variety of data types (continuous, discrete, purely ordinal, and dichotomous). Such methods are also robust with respect to outliers and for small sample sizes.
| Version: | 2.2 |
| Depends: | R (≥ 2.6.0), MASS |
| Published: | 2022-08-07 |
| Author: | Kimihiro Noguchi, Mahbub Latif, Karthinathan Thangavelu, Frank Konietschke, Yulia R. Gel, Edgar Brunner |
| Maintainer: | Frank Konietschke <frank.konietschke at charite.de> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Citation: | nparLD citation info |
| CRAN checks: | nparLD results |
| Reference manual: | nparLD.pdf |
| Package source: | nparLD_2.2.tar.gz |
| Windows binaries: | r-devel: nparLD_2.2.zip, r-release: nparLD_2.2.zip, r-oldrel: nparLD_2.2.zip |
| macOS binaries: | r-release (arm64): nparLD_2.2.tgz, r-oldrel (arm64): nparLD_2.2.tgz, r-release (x86_64): nparLD_2.2.tgz, r-oldrel (x86_64): nparLD_2.2.tgz |
| Old sources: | nparLD archive |
| Reverse suggests: | MANOVA.RM |
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