Maximum Matching for General Weighted Graph
Computes the maximum matching for unweighted graph and maximum
matching for (un)weighted bipartite graph efficiently.

Fitting 'Maxent' Species Distribution Models with 'glmnet'
Procedures to fit species distributions models from occurrence records and environmental variables, using 'glmnet' for model fitting. Model structure is the same as for the 'Maxent' Java package, version 3.4.0, with the same feature types and regularization options.  See the 'Maxent' website <http://biodiversityinformatics.amnh.org/open_source/maxent> for more details.

Approximate Maximisation of Nestedness in Bipartite Graphs
c(person("Christoph ", "Hoeppke", email = "christoph.hoeppke@googlemail.com", role = c("aut", "cre")),
person("Benno ", "Simmons", email = "benno.simmons@gmail.com", role = c("aut")))
Functions to generate graphs that maximise the NODF (nestedness metric based
on overlap and decreasing fill) metric for a given number of rows, columns
and links. NODF was originally defined by Almeida-Neto et al. (2008)
<doi:10.1111/j.0030-1299.2008.16644.x>. As nestedness in ecological networks
depends on the size of the networks we require normalisation to make them
comparable. We offer three highly optimised algorithms to find the
optimising graphs so that users can choose an appropriate trade off between
computation time and NODF value for the task at hand.
Christoph Hoeppke [aut, cre],
Benno Simmons [aut]

Maximally Selected Rank- and Gauss Statistics
Maximally selected rank and Gauss statistics with
several p-value approximations.

Torsten Hothorn <Torsten.Hothorn@rzmail.uni-erlangen.de>
Berthold Lausen <Berthold.Lausen@rzmail.uni-erlangen.de>

Maximally selected rank statistics with
several p-value approximations.

Maximally selected rank statistics with several p-value
approximations.

exactRankTests(>= 0.8-23), mvtnorm(>= 0.5-10), stats, graphics

person(given = "Andrew",
family = "McNeil",
role = c("aut", "cre"),
email = "andrew.richard.mcneil@gmail.com")
The maybe type represents the possibility of some value or nothing.
It is often used instead of throwing an error or returning `NULL`.
The advantage of using a maybe type over `NULL` is that it is both composable
and requires the developer to explicitly acknowledge the potential absence
of a value, helping to avoid the existence of unexpected behaviour.