Title: Implement Download Buttons in 'rmarkdown'
Description: Implement download buttons in HTML output from 'rmarkdown' without the need for 'runtime:shiny'.
Imports: fs, readr, writexl, bsplus, mime, htmltools, base64enc,
Packaged: 2020-02-17 20:52:56 UTC; fmattioni
Author: Felipe Mattioni Maturana [aut, cre]
Maintainer: Felipe Mattioni Maturana <felipe.mattioni@med.uni-tuebingen.de>

Packaged: 2020-03-05 08:49:26 UTC; fmattioni

Packaged: 2020-03-30 13:46:31 UTC; fmattioni

Packaged: 2020-04-14 12:03:34 UTC; fmattioni

Packaged: 2020-05-04 18:55:53 UTC; fmattioni

Packaged: 2020-09-17 08:10:14 UTC; fmattioni

Imports: fs, readr, writexl, bsplus, mime, htmltools, magrittr, zip,
Suggests: knitr, rmarkdown, testthat (>= 3.0.1), spelling, covr
Packaged: 2022-02-04 12:42:46 UTC; fmattioni

Packaged: 2022-02-23 08:49:09 UTC; fmattioni

Packaged: 2022-08-16 07:11:22 UTC; fmattioni

Description: A set of functions that downscales species occupancy at
coarse grain sizes to predict species occupancy at fine grain sizes.
Imports: cubature (>= 1.1-2), graphics, grDevices, methods, minpack.lm
Suggests: rgeos, spocc (>= 0.4.0), spoccutils (>= 0.1.0)

Description: 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.

Description: 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>.
Suggests: rgeos, rgdal, rgbif (>= 0.9.2), knitr, rmarkdown, bookdown

Title: Downscale of RS Images using Deep Learning
Description: 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.