Authors@R: c(person("Davor", "Cubranic", role = "aut",
person("Mark", "Pickup", role = c("aut", "cre"),
person("Jonoska Stojkova", "Biljana", role = "ctb",

Title: Rice Microarray Probe ID Conversion, from Probe ID to RAP-DB ID
Authors@R: c(person("Youming", "Liu", role = c("aut", "cre","cph"),
Description: Microarray probe ID is not convenient for further enrichment analysis and target gene selection.
The package is created for the rice microarray probe ID conversion.
This package can convert microarray probe ID from GPL6864 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL6864>, GPL8852 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL8852>, and GPL2025 <https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL2025> platforms to RAP-DB ID. RAP-DB "The Rice Annotation Project Database" <https://rapdb.dna.affrc.go.jp> is a well-known database for rice Oryza sativa, and the gene ID in this database is widely used in many areas related to rice research.
For multiple probes representing a single gene, This package can merge them by taking the mean, max, or min value of these probes.
Or we can keep multiple probes by appending sequence numbers to duplicate the RAP-DB ID.

Title: Generation of Customizable, Discretized Time Series of
Authors@R: person("Arne", "Schoch", email = "arne_schoch@gmx.net", role = c("aut", "cre"))
Description: The supplied code allows for the generation of discrete time series of oscillating species. General shapes can be selected by means of individual functions, which are widely customizable by means of function arguments. All code was developed in the Biological Information Processing Group at the BioQuant Center at Heidelberg University, Germany.

Title: O-Stats, or Pairwise Community-Level Niche Overlap Statistics
Authors@R: c(person(c('Quentin', 'D.'), 'Read', role = c('aut', 'cre'), email = 'qread@sesync.org'),
person(c('Isadora', 'E.'), 'Fluck', role = 'aut'),
person('Benjamin', 'Baiser', role = 'aut'),
person(c('John', 'M.'), 'Grady', role = 'aut'),
person(c('Phoebe', 'L.'), 'Zarnetske', role = 'aut'),
Description: O-statistics, or overlap statistics, measure the degree of community-level trait overlap.
They are estimated by fitting nonparametric kernel density functions to each species’ trait
distribution and calculating their areas of overlap. For instance, the median pairwise overlap
for a community is calculated by first determining the overlap of each species pair
in trait space, and then taking the median overlap of each species pair in a community.
This median overlap value is called the O-statistic (O for overlap).
The Ostats() function calculates separate univariate overlap statistics for each trait,
while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits.
O-statistics can be evaluated against null models to obtain standardized effect sizes.
Ostats is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale
drivers of biodiversity across the National Ecological Observatory Network (NEON)."
For more information on this project, see the Macrosystems Biodiversity Website
(<https://neon-biodiversity.github.io/>). Calculation of O-statistics is described in
Read et al. (2018) <doi:10.1111/ecog.03641>, and a teaching module for introducing the
underlying biological concepts at an undergraduate level is described in Grady et al.
Imports: sfsmisc, matrixStats, circular, hypervolume, ggplot2,
Maintainer: Quentin D. Read <qread@sesync.org>