(>= 2.0), dplyr, rlang, magrittr, homomorpheR, gmp
Suggests: opencpu, knitr, covr, rmarkdown

person("Rich", "FitzJohn", role = c("aut"),
person("Thibaut", "Jombart", role = "aut"))
Description: Creates discretised versions of continuous
distribution functions by mapping continuous values
to an underlying discrete grid, based on a (uniform)
frequency of discretisation, a valid discretisation
point, and an integration range. For a review of

Title: Distributional Method for the Dichotomisation of Continuous
Description: Contains a range of functions covering the present
development of the distributional method for the dichotomisation of continuous outcomes.
The method provides estimates with standard error of a comparison of proportions
(difference, odds ratio and risk ratio) derived, with similar precision, from a comparison of means.
See the URL below or <arXiv:1809.03279> for more information.

Title: Download Occurrence Data of Vascular Plants in Germany from the
Authors@R: person("Nicolas", "Friess", email = "nico.friess@posteo.de", role = c("aut", "cre"))
Description: Download data from the FLORKART database on the distribution of vascular plants in Germany in a convenient way.

Description: Download data from the FlorKart database of the floristic field mapping in Germany in a convenient way. The database incorporates distribution data for plants in Germany on the basis of quadrants on a topographical map with a resolution of 1 : 25000 (TK 25). The data is owned and provided by the German Federal Agency for Nature Conservation (BfN) and the Network Phytodiversity in Germany (NetPhyD). For further information please visit <http://www.floraweb.de/pflanzenarten/hintergrundtexte_florkart_organisation.html>. The author of this package is in no way associated with the BfN or NetPhyD.

Title: Distribution-free confidence region (distfree.cr)
Description: An R package that is developed for constructing confidence
regions without the need to know the sampling distribution of

Description: Constructs confidence regions without the need to know the sampling distribution of bivariate data. The method was proposed by Zhiqiu Hu & Rong-cai Yang (2013) <doi:10.1371/journal.pone.0081179.g001>.