Population genetic data such as 'Single Nucleotide
Polymorphisms' (SNPs) is often used to identify genomic regions
that have been under recent natural or artificial selection
and might provide clues about the molecular mechanisms of adaptation.
One approach, the concept of an 'Extended Haplotype Homozygosity' (EHH),
introduced by (Sabeti 2002) <doi:10.1038/nature01140>, has given rise to
several statistics designed for whole genome scans.
The package provides functions to compute three of these,
namely: 'iHS' (Voight 2006) <doi:10.1371/journal.pbio.0040072> for
detecting positive or 'Darwinian' selection within a single population as well as
'Rsb' (Tang 2007) <doi:10.1371/journal.pbio.0050171> and
'XP-EHH' (Sabeti 2007) <doi:10.1038/nature06250>, targeted
at differential selection between two populations.
Various plotting functions are included to facilitate
visualization and interpretation of these statistics.

c(person("Alexander", "Klassmann", role = c("aut", "cre"),
email = "rehh@oneoverx.eu"),
person("Mathieu", "Gautier", role = "aut",
email = "mathieu.gautier@inrae.fr"),
person("Renaud", "Vitalis", role = "aut"))
Alexander Klassmann [aut, cre],
Mathieu Gautier [aut],
Renaud Vitalis [aut]

Data Only: Searching for Footprints of Selection using Haplotype
Homozygosity Based Tests
Contains example data for the 'rehh' package.

Downloads Tweets from a List of Tweet IDs
c(
person("Kevin", "Coakley", email = "kcoakley@sdsc.edu", role = c("aut", "cre")),
person("Zachary", "Steinert-Threlkeld", email = "zst@luskin.ucla.edu", role = "ctb"))
Kevin Coakley [aut, cre],
Zachary Steinert-Threlkeld [ctb]
Facilitates replication of Twitter-based research by handling
common programming tasks needed when downloading tweets.  Specifically, it ensures a
user does not exceed Twitter’s rate limits, and it saves tweets in moderately sized files.
While a user could perform these tasks in their own code, doing so may be beyond the
capabilities of many users.

Reinforced Risk Prediction with Budget Constraint
c(person("Yinghao", "Pan", role = c("aut", "cre"),
email = "ypan8@uncc.edu"),
person("Yingqi", "Zhao", role = "aut"),
person("Eric", "Laber", role = "aut"))
Yinghao Pan [aut, cre],
Yingqi Zhao [aut],
Eric Laber [aut]
Traditional risk prediction only utilizes baseline factors known
to be associated with the disease. Given that longitudinal information are
routinely measured and documented for patients, it is worthwhile to make
full use of these data. The available longitudinal biomarker data will
likely improve prediction. However, repeated biomarker collection could be
costly and inconvenient, and risk prediction for patients at a later time
could delay necessary medical decisions. Thus, there is a trade-off between
high quality prediction and cost. This package implements a cost-effective
statistical procedure that recursively incorporates comprehensive
longitudinal information into the risk prediction model, taking into
account the cost of delaying the decision to a follow-up time when more
information is available. The statistical methods are described in the
following paper: Pan, Y., Laber, E., Smith, M., Zhao, Y. (2018). Reinforced
risk prediction with budget constraint: application to electronic health
records data. Manuscript submitted for publication.