Provides access to the 'Leanpub' API
<https://leanpub.com/help/api> for gathering information about
publications and submissions to the 'Leanpub' platform.

latent effect adjustment after primary projection
Yunting Sun <yuntings@stanford.edu> , Nancy R.Zhang
<nzhang@stanford.edu>, Art B.Owen <owen@stanford.edu>
These functions take a gene expression value matrix, a
primary covariate vector, an additional known covariates
matrix.  A two stage analysis is applied to counter the effects
of latent variables on the rankings of hypotheses.  The
estimation and adjustment of latent effects are proposed by
Sun, Zhang and Owen (2011).  "leapp" is developed in the
context of microarray experiments, but may be used as a general
tool for high throughput data sets where dependence may be
involved.

Yunting Sun <yunting.sun@gmail.com> , Nancy R.Zhang
<nzhang@stanford.edu>, Art B.Owen <owen@stanford.edu>

Latent Effect Adjustment After Primary Projection

Thomas Lumley <thomas@biostat.washington.edu> using Fortran code
by Alan Miller
Regression subset selection including exhaustive search

Thomas Lumley <tlumley@u.washington.edu> using Fortran code
by Alan Miller

Thomas Lumley <tlumley@u.washington.edu> using Fortran code by
Alan Miller

Thomas Lumley based on Fortran code by Alan Miller
Regression subset selection, including exhaustive search.

Implementations of several basic neural network concepts in R, as based on posts on \url{http://qua.st/}.

Population Genetic Simulations & Numerical Analysis
grDevices, graphics, gtools, methods, phytools, stats
Conducts various numerical analyses and simulations in population genetics and evolutionary theory, primarily for the purpose of teaching (and learning about) key concepts in population & quantitative genetics, and evolutionary theory.

An Implementation of Crawford's and Wright's Learning Curve
Production Functions
c(
person('Jason', 'Freels', email = 'auburngrads@live.com', role = c('aut', 'cre')),
person('Bradley', 'Boehmke', email = 'bradleyboehmke@gmail.com', role = c('aut')))
An implementation of Crawford's and Wright's learning curve production functions. It provides unit and cumulative block estimates for time (or cost) of units along with an aggregate learning curve. It also provides delta and error functions and some basic learning curve plotting functions.
Jason Freels [aut, cre],
Bradley Boehmke [aut]