Supplies a set of functions to interface with bikeshare data
    following the General Bikeshare Feed Specification, allowing users to query
    and accumulate tidy datasets for specified cities/bikeshare programs.

A high performance interface to the Global Biodiversity
  Information Facility, 'GBIF'.  In contrast to 'rgbif', which can
  access small subsets of 'GBIF' data through web-based queries to
  a central server, 'gbifdb' provides enhanced performance for R users
  performing large-scale analyses on servers and cloud computing
  providers, providing full support for arbitrary 'SQL' or 'dplyr'
  operations on the complete 'GBIF' data tables (now over 1 billion
  records, and over a terabyte in size). 'gbifdb' accesses a copy
  of the 'GBIF' data in 'parquet' format, which is already readily
  available in commercial computing clouds such as the Amazon Open
  Data portal and the Microsoft Planetary Computer, or can be 
  accessed directly without downloading, or downloaded
  to any server with suitable bandwidth and storage space.
  The high-performance techniques for local and remote access 
  are described in <https://duckdb.org/why_duckdb>
  and <https://arrow.apache.org/docs/r/articles/fs.html> respectively.

Offers the Generalized Berk-Jones (GBJ) test for set-based inference in genetic
	association studies. The GBJ is designed as an alternative to tests such as Berk-Jones (BJ),
    Higher Criticism (HC), Generalized Higher Criticism (GHC), Minimum p-value (minP), and Sequence
    Kernel Association Test (SKAT). All of these other methods (except for SKAT) are also implemented 
    in this package, and we additionally provide an omnibus test (OMNI) which integrates information from each of the tests.
    The GBJ has been shown to outperform other tests in genetic association studies when signals
    are correlated and moderately sparse. Please see the vignette for a quickstart guide or Sun and Lin
    (2017) <arXiv:1710.02469> for more details.

An implementation of extensions to Freund and Schapire's AdaBoost 
  algorithm and Friedman's gradient boosting machine. Includes regression 
  methods for least squares, absolute loss, t-distribution loss, quantile 
  regression, logistic, multinomial logistic, Poisson, Cox proportional hazards 
  partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and 
  Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.
  Newer version available at github.com/gbm-developers/gbm3.