Title: Nonlinear least squares examples from NIST
Author: original from National Institutes for Standards and Technology (NIST)
R port by Douglas Bates <bates@stat.wisc.edu>
Description: Datasets for testing nonlinear regression routines.
LicenseDetails: The original data sets and descriptions are from the
NIST web site and were not covered by an explicit copyright.
Modifications for R data sets are covered by GPL (>= 2).
Packaged: Tue Feb 10 16:14:26 2009; bates

Author: original from National Institutes for Standards and Technology
port by Douglas Bates <bates@stat.wisc.edu>

License: Original pages from NIST web site.  Modifications for R covered by

Packaged: Sun Apr 18 11:30:17 2004; bates

Packaged: Mon Oct  4 06:50:42 2004; bates

Title: Fundamental Physical Constants and Unit Conversions from NIST
Authors@R: c(person(given = "Jose", family = "Gama", role = c("aut","cre"),email = "rxprtgama@gmail.com"))
Description: Fundamental physical constants (Quantity, Value, Uncertainty, Unit) for
SI (International System of Units) and non-SI, plus unit conversions
Based on the data from NIST (National Institute of Standards and Technology)

SI (International System of Units) and non-SI units, plus unit conversions
Based on the data from NIST (National Institute of Standards and Technology, USA)

Title: Finds the Best Subset of Points to Sample
Description: Given a few examples of experiments over a time (or spatial) course,
'NITPicker' selects a subset of points to sample in follow-up experiments,
which would (i) best distinguish between the experimental conditions and the
control condition (ii) best distinguish between two models of how the
experimental condition might differ from the control (iii) a combination of

Title: Simulating Neutral Landscape Models
Authors@R: c(person("Marco", "Sciaini", email = "sciaini.marco@gmail.com", role = c("aut", "cre"), comment = "Department of Ecosystem Modelling, University of Goettingen"),
person("Matthias", "Fritsch", email = "matthias.fritsch@forst.uni-goettingen.de", role =  c("aut"), comment = "Department of Ecosystem Modelling, University of Goettingen"),
person("Cédric", "Scherer", email = "cedricphilippscherer@gmail.com", role =  c("aut"), comment = "Leibniz Institute for Zoo and Wildlife Research, Berlin"),
person("Craig", "Simpkins", email = "simpkinscraig063@gmail.com", role =  c("aut"), comment = "Department of Ecosystem Modelling, University of Goettingen"))
Description: Provides neutral landscape models
existing landscape analyses. Neutral landscape models range from "hard"
neutral models (only random functions) to "soft" ones (with parameters) and
generate landscape patterns that are not grounded in ecological reasoning.
Thus, these patterns can be used as null models in landscape ecology. 'NLMR'
combines a large number of algorithms from published software (Saura &
al. 2015 <doi:10.1111/2041-210X.12308>) for simulating neutral landscapes and
includes utility functions to classify and combine the landscapes. The
simulation results are obtained in a geospatial data format (raster* objects
from the 'raster' package) and can, therefore, be used
in any sort of raster data operation that is performed with standard
Imports: checkmate, dismo, dplyr, ggplot2, gstat, igraph, lemon,
magrittr, maptools, purrr, RandomFields, raster, rasterVis,
R.utils, sp, spatstat, stats, tibble, viridis
Suggests: testthat, covr, knitr, rmarkdown, lintr
Author: Marco Sciaini [aut, cre] (Department of Ecosystem Modelling, University
Matthias Fritsch [aut] (Department of Ecosystem Modelling, University
Cédric Scherer [aut] (Leibniz Institute for Zoo and Wildlife Research,
Craig Simpkins [aut] (Department of Ecosystem Modelling, University of