Suggests: BIEN, biomod2, dismo, ecospat, googlesheets, knitr, rmarkdown

Title: Simple Tools for Defining Species Ranges
person("Marlon E.", "Cobos", email = "manubio13@gmail.com", role = c("aut", "cre"),
person("Alberto", "Jimenez-Valverde", email = "alberto.jimenez.valverde@gmail.com",
Description: A collection of tools to create species range maps based on
occurrence data, statistics, and spatial objects. Other tools in this
collection can be used to analyze the environmental characteristics of
the species ranges. Plotting options to represent results in various
manners are also available. Results obtained using these tools can be
used to explore the distribution of species and define areas of occupancy
and extent of occurrence of species. Other packages help to explore species
distributions using distinct methods, but options presented in this set of
tools (e.g., using trend surface analysis and concave hull polygons) are
exclusive. Description of methods, approaches, and comments for some of the
Peterson et al. (2011) <https://www.degruyter.com/princetonup/view/title/506966>,
Imports: concaveman (>= 1.0), dplyr (>= 0.8), maps (>= 3.3), maptools
(>= 0.9), methods, rnaturalearthdata (>= 0.1), raster (>= 3.0),
Author: Marlon E. Cobos [aut, cre] (<https://orcid.org/0000-0002-2611-1767>),

Title: Mid-Domain Effect and Species Richness Estimates
Description: Generates expected values of species richness, with continuous or
scattered ranges, for data across one or two dimensions.
Packaged: 2015-12-03 09:28:16 UTC; aniruddha

Title: Mid-Domain Effect and Species Richness Patterns
Author: Aniruddha Marathe, ATREE, Bangalore
Packaged: 2016-08-12 02:06:23 UTC; aniruddha

Title: Mid-Domain Effect and Species Richness
Packaged: 2018-08-18 17:52:52 UTC; aniruddha

Packaged: 2019-01-08 04:19:51 UTC; aniruddha

Packaged: 2016-02-09 06:48:16 UTC; aniruddha

Title: A Fast Implementation of Random Forests
Author: Marvin N. Wright [aut, cre], Stefan Wager [ctb], Philipp Probst [ctb]
Description: A fast implementation of Random Forests, particularly suited for high
dimensional data. Ensembles of classification, regression, survival and
probability prediction trees are supported. Data from genome-wide association
studies can be analyzed efficiently. In addition to data frames, datasets of
class 'gwaa.data' (R package 'GenABEL') and 'dgCMatrix' (R package 'Matrix')