A group of sample points are evaluted against a user-defined expression,
the sample points are lists of parameters with values that may be substituted into
that expression. The genetic algorithm attmepts to make the result of the expression
as low as possible (usually this would be a squares of residuals value).

A group of sample points are evaluted against a user-defined expression,
the sample points are lists of parameters with values that may be substituted into
that expression. The genetic algorithm attmepts to make the result of the expression
as low as possible (usually this would be the sum of residuals squared).

A group of sample points are evaluted against a
user-defined expression, the sample points are lists of
parameters with values that may be substituted into that
expression. The genetic algorithm attmepts to make the result
of the expression as low as possible (usually this would be the
sum of residuals squared).

A group of sample points are evaluated against a
user-defined expression, the sample points are lists of
parameters with values that may be substituted into that
expression. The genetic algorithm attempts to make the result
of the expression as low as possible (usually this would be the
sum of residuals squared).

Genetic and Isotopic Assignment Accounting for Habitat
Suitability
Tools for using genetic markers, stable isotope data, and habitat
suitability data to calculate posterior probabilities of breeding origin of
migrating birds.
c(person(c('Eric', 'C.'), 'Anderson', role = c('cre', 'aut'),
email = 'eric.anderson@noaa.gov'))
dplyr, geosphere, ggplot2, lazyeval, magrittr, maptools,
raster, rgeos, sp, stringr, tidyr

dplyr, geosphere, magrittr, maptools, raster, rlang, sp

Machine Learning-Based Analysis of Potential Power Gain from
Passive Device Installation on Wind Turbine Generators
c(
person("Hoon", "Hwangbo", email = "hhwangb1@utk.edu", role = c("aut","cre")),
person("Yu", "Ding", email = "yuding@tamu.edu", role = c("aut")),
person("Daniel", "Cabezon", email = "Daniel.Cabezon@edpr.com", role = c("aut")),
person("Texas A&M University", role = c("cph")),
person("EDP Renewables", role = c("cph")))
Hoon Hwangbo [aut, cre],
Yu Ding [aut],
Daniel Cabezon [aut],
Texas A&M University [cph],
EDP Renewables [cph]
Copyright (c) 2019 Y. Ding, H. Hwangbo, Texas A&M
University, D. Cabezon, and EDP Renewables
Provides an effective machine learning-based tool that quantifies the gain of passive device installation on wind turbine generators.
H. Hwangbo, Y. Ding, and D. Cabezon (2019) <arXiv:1906.05776>.
fields (>= 9.0), FNN (>= 1.1), utils, stats