'DataInput.R' 'downscale.R' 'OptimiseParameters.R'
'OptimiseParametersGNB.R' 'OptimiseParametersFNB.R'
'OptimiseParametersLogis.R' 'OptimiseParametersThomas.R'
'PredictFunctions.R' 'ResidFunctions.R' 'StartingParams.R'
'ThomasFunctions.R' 'plot.predict.downscale.R'
'predict.downscale.R' 'ensemble.downscale.R' 'upgrain.R'
'hui.downscale.R' 'HuiFunctions.R' 'upgrain.threshold.R'

Downscales species occupancy at coarse grain sizes to predict species occupancy at fine grain sizes. Ten models are provided to fit and extrapolate the occupancy-area relationship, as well as methods for preparing atlas data for modelling. See Marsh et. al. (2018) doi:10.18637/jss.v086.c03.

Uses species occupancy at coarse grain sizes to predict species occupancy at fine grain sizes. Ten models are provided to fit and extrapolate the occupancy-area relationship, as well as methods for preparing atlas data for modelling. See Marsh et. al. (2018) <doi:10.18637/jss.v086.c03>.
rgeos, rgdal, rgbif (>= 0.9.2), knitr, rmarkdown, bookdown

Downscale of RS Images using Deep Learning
Downscaling the coarse-resolution remote sensing
images into fined-resolution images is important for
preprocessing. This package provides the functions for this
with high accuracy. Currently, the resautonet is used
as the deep learning algorithm for downscaling.
This work was done based on this paper,
Lianfa Li (2019) <doi:10.3390/rs11111378>.

A Tool to Scale Down Large Workflows for Testing
person("Landau", "William", email = "will.landau@gmail.com",
role = c("aut", "cre"))
Toggles the test and production versions of a large workflow.

A Tool to Downsize Large Workflows for Testing
person(family = "Landau", given = "William",
email = "will.landau@gmail.com", role = c("aut", "cre"))

person(given = c("William", "Michael"), family = "Landau",
email = "will.landau@gmail.com", role = c("aut", "cph", "cre"))

c(person("Kenneth", "Hoehn", role=c("aut", "cre"),
email="kenneth.hoehn@yale.edu"),
person("Susanna", "Marquez", role=c("ctb"),
email="susanna.marquez@yale.edu"),
person("Jason", "Vander Heiden", role=c("ctb"),
email="jason.vanderheiden@gmail.com"),
person("Steven", "Kleinstein", role=c("aut", "cph"),
email="steven.kleinstein@yale.edu"))
Provides a set of functions for inferring, visualizing, and analyzing B cell phylogenetic trees.
Provides methods to 1) reconstruct unmutated ancestral sequences,
2) build B cell phylogenetic trees using multiple methods,
3) visualize trees with metadata at the tips,
4) reconstruct intermediate sequences,
5) detect biased ancestor-descendant relationships among metadata types
Workflow examples available at documentation site (see URL).
Citations:
Hoehn et al (2020) <doi:10.1101/2020.05.30.124446>,
Hoehn et al (2021) <doi:10.1101/2021.01.06.425648>.
alakazam (>= 1.1.0), ape (>= 5.5), Biostrings, dplyr (>=
0.8.1), ggtree, graphics, gridExtra, markdown, methods,
phangorn (>= 2.7.1), phylotate, RColorBrewer, rlang, shazam (>=
1.1.0), stats, stringr, tidyselect, tidyr, utils
'Data.R' 'Dowser.R' 'Clones.R' 'Classes.R' 'Plotting.R'
'Germlines.R' 'Statistics.R' 'TreeFunctions.R'
Kenneth Hoehn [aut, cre],
Susanna Marquez [ctb],
Jason Vander Heiden [ctb],
Steven Kleinstein [aut, cph]