Spline Based Window Boundaries for Genomic Analyses
Defines window or bin boundaries for the analysis of genomic data.
Boundaries are based on the inflection points of a cubic smoothing spline
fitted to the raw data. Along with defining boundaries, a technique to
evaluate results obtained from unequally-sized windows is provided.
Applications are particularly pertinent for, though not limited to, genome
scans for selection based on variability between populations (e.g. using
Wright's fixations index, Fst, which measures variability in subpopulations
relative to the total population).

This is a Collection of Functions to Analyse Gender Differences
c(
person("Rita", "Giordano", email = "giordanor@rsc.org", role =  c("aut", "cre")),
person("Aileen", "Day", role = "aut"),
person("John", "Boyle", role = "aut"),
person("Colin", "Batchelor", role = "ctb"),
person("Royal Society of Chemistry", role = "cph"))
Implementation of functions, which combines binomial calculation
and data visualisation, to analyse the differences in publishing authorship
by gender described in Day et al. (2020) <doi:10.1039/C9SC04090K>.
It should only be used when self-reported gender is unavailable.
Rita Giordano [aut, cre],
Aileen Day [aut],
John Boyle [aut],
Colin Batchelor [ctb],
Royal Society of Chemistry [cph]

Li Xu, Zhang Wen, Siyan Chen, Hans Bibiko, Will Lowe
An implementation of the advanced gene
search in R. This package has basic
annotation information. Also, it contains
a relative intact gene database which was
obtained from the Human Gene Database
<https://www.genecards.org>. It allows
users to search gene symbol or alias
and convert gene interested to the
consistent gene symbols. It also
provides users with gene WIKI
introduction.

Gene Expression Clustering and Gene Network
Yaqun Wang, Zhengyang Shi and Xiang Zhan
Functions for clustering time-course gene expression and reconstructing of gene regulatory network based on Dynamic Bayesian Network.

Identification of Periodically Expressed Genes
Miika Ahdesmaki, Konstantinos Fokianos, and
Korbinian Strimmer.
The GeneCycle package implements the approaches of Wichert et al.
(2004) and Ahdesmaki et al. (2005) for detecting periodically
expressed genes from gene expression time series data.