Install and control 'Open Source Routing Machine' ('OSRM')
    backend executables to prepare routing data and run/stop a local
    'OSRM' server. For computations with the running server use the 'osrm'
    package for 'R' (<https://cran.r-project.org/package=osrm>).

Wrapper around the 'Open Source Routing Machine (OSRM)'
        API <http://project-osrm.org/>. 'osrmr' works with API versions
        4 and 5 and can handle servers that run locally as well as the
        'OSRM' webserver.

A system for calculating the minimum total sample size needed to achieve a prespecified power or the optimal allocation for each treatment group with a fixed total sample size to maximize the power.

Over 30 years of daily commit activity and contributor growth
    for 'FreeBSD', 'OpenBSD', 'NetBSD', and 'PostgreSQL'. Built from cloned git
    repositories for complete coverage -- not limited by API quotas.
    Includes daily commits, daily new committers, weekly aggregates, and
    'Phabricator' sign-up data. Designed for time series analysis, growth
    modeling, and cross-project comparison. Contains no personal data.

Identifies the optimal transformation of a surrogate marker and estimates the proportion of treatment explained (PTE) by the optimally-transformed surrogate at an earlier time point when the primary outcome of interest is a censored time-to-event outcome; details are described in Wang et al (2021) <doi:10.1002/sim.9185>.

O-statistics, or overlap statistics, measure the degree of community-level trait overlap. 
    They are estimated by fitting nonparametric kernel density functions to each species’ trait 
    distribution and calculating their areas of overlap. For instance, the median pairwise overlap 
    for a community is calculated by first determining the overlap of each species pair 
    in trait space, and then taking the median overlap of each species pair in a community. 
    This median overlap value is called the O-statistic (O for overlap).
    The Ostats() function calculates separate univariate overlap statistics for each trait, 
    while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits. 
    O-statistics can be evaluated against null models to obtain standardized effect sizes. 
    'Ostats' is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale 
    drivers of biodiversity across the National Ecological Observatory Network (NEON)." 
    For more information on this project, see the Macrosystems Biodiversity Website 
    (<https://neon-biodiversity.github.io/>). Calculation of O-statistics is described in
    Read et al. (2018) <doi:10.1111/ecog.03641>, and a teaching module for introducing the
    underlying biological concepts at an undergraduate level is described in Grady et al.
    (2018) <http://tiee.esa.org/vol/v14/issues/figure_sets/grady/abstract.html>.