assertthat, utils, stats, plyr, rmeta, Rcpp (>= 0.12.11),
mvtnorm, abind, softImpute
MASS, REBayes, corrplot, testthat, kableExtra, knitr,
rmarkdown, profmem, flashr (>= 0.6.7)

MASS, REBayes, corrplot (>= 0.90), testthat, kableExtra,
knitr, rmarkdown, profmem, flashr (>= 0.6.7)

c(person('Cory','Merow',email='cory.merow@gmail.com',role=c('aut','cre')),
person('Peter J.', 'Galante',email='pgalante@amnh.org',role=c('aut')),
person("Jamie M.","Kass",email='jamie.m.kass@gmail.com',role=c('aut')),
person("Cecina","Babich Morrow",email='cbabichmorrow@amnh.org',role=c('aut')),
person("Valentina", 'Grisales Betancur',email='vgrisale@eafit.edu.com',role=c('aut')))
Mask ranges based on expert
knowledge or remote sensing layers. These tools can
be combined to quantitatively and reproducibly
generate a new map or to update an existing map.
Methods include expert opinion and data-driven
tools to generate thresholds for binary masks.
gdalUtils, dismo, dplyr, e1071, lubridate, magrittr, maptools,
raster, rgdal, sp, stats, tidyverse, utils, virtualspecies
Cory Merow [aut, cre],
Peter J. Galante [aut],
Jamie M. Kass [aut],
Cecina Babich Morrow [aut],
Valentina Grisales Betancur [aut]

dplyr, e1071, lubridate, magrittr, raster, sp, stats, utils,
dismo, testthat, knitr, rmarkdown, wallace

Build Data Structures for Common Statistical Analysis
Use a consistent syntax to create data structures of common
statistical techniques that can be continued in a pipe chain.
Design the analysis, add settings and variables, construct the results, and
polish the final structure. Rinse and repeat for any number of statistical
techniques.
dplyr, tidyr, broom, assertive, magrittr, stats, utils,
lazyeval, MASS

dplyr, tidyr, broom, assertive, magrittr, stats, utils,
lazyeval
knitr, geepack, rmarkdown, testthat, MASS

dplyr, tidyr, broom, magrittr, stats, utils, lazyeval
knitr, geepack, pls, rmarkdown, testthat, MASS, covr

person(given = "Luke",
family = "Johnston",
role = c("aut", "cre"),
email = "lwjohnst@gmail.com",
comment = c(ORCID = "0000-0003-4169-2616"))
Use a consistent syntax to create data structures
of common statistical techniques that can be continued in a pipe
chain.  Design the analysis, add settings and variables, construct the
results, and polish the final structure. Rinse and repeat for any
number of statistical techniques.
broom, dplyr, magrittr, purrr, rlang, stats, tibble, tidyr,
tidyselect, utils
covr, geepack, knitr, MASS, pls, rmarkdown, roxygen2,
testthat