A computer program to detect markers responding to selection
In the new era of population genomics, surveys of genetic
polymorphism ("genome scans") offer the opportunity to
distinguish locus-specific from genome wide effects at many
loci. Identifying presumably neutral regions of the genome that
are assumed to be influenced by genome-wide effects only, and
excluding presumably selected regions, is therefore critical to
infer population demography and phylogenetic history reliably.
Conversely, detecting locus-specific effects may help identify
those genes that have been, or still are, targeted by natural
selection. The software package DetSel has been developed to
identify markers that show deviation from neutral expectation
in pairwise comparisons of diverging populations.

A Computer Program to Detect Markers Responding to Selection
In the new era of population genomics, surveys of genetic polymorphism ("genome scans") offer the opportunity to distinguish locus-specific from genome wide effects at many loci. Identifying presumably neutral regions of the genome that are assumed to be influenced by genome-wide effects only, and excluding presumably selected regions, is therefore critical to infer population demography and phylogenetic history reliably. Conversely, detecting locus-specific effects may help identify those genes that have been, or still are, targeted by natural selection. The software package DetSel has been developed to identify markers that show deviation from neutral expectation in pairwise comparisons of diverging populations.

Develop Treatment Rules with Observational Data
c(person("Jeremy", "Roth", email = "jhroth@uw.edu", role=c("cre", "aut")),
person("Noah", "Simon", email = "nrsimon@uw.edu", role="aut"))
Develop and evaluate treatment rules based on: (1) the standard indirect approach of split-regression, which fits regressions separately in both treatment groups and assigns an individual to the treatment option under which predicted outcome is more desirable; (2) the direct approach of outcome-weighted-learning proposed by Yingqi Zhao, Donglin Zeng, A. John Rush, and Michael Kosorok (2012) <doi:10.1080/01621459.2012.695674>; (3) the direct approach, which we refer to as direct-interactions, proposed by Shuai Chen, Lu Tian, Tianxi Cai, and Menggang Yu (2017) <doi:10.1111/biom.12676>. Please see the vignette for a walk-through of how to start with an observational dataset whose design is understood scientifically and end up with a treatment rule that is trustworthy statistically, along with an estimation of rule benefit in an independent sample.
Jeremy Roth [cre, aut],
Noah Simon [aut]