Access 'Amazon Web Service Simple Storage Service' ('S3') <https://aws.amazon.com/s3/>
    as if it were a file system. Interface based on the R package 'fs'.

Implements a set of distribution modeling methods that are suited to species with small sample sizes (e.g., poorly sampled species or rare species). While these methods can also be used on well-sampled taxa, they are united by the fact that they can be utilized with relatively few data points. More details on the currently implemented methodologies can be found in Drake and Richards (2018) <doi:10.1002/ecs2.2373>, Drake (2015) <doi:10.1098/rsif.2015.0086>, and Drake (2014) <doi:10.1890/ES13-00202.1>.

A new object oriented programming system designed to be a
    successor to S3 and S4. It includes formal class, generic, and method
    specification, and a limited form of multiple dispatch. It has been
    designed and implemented collaboratively by the R Consortium
    Object-Oriented Programming Working Group, which includes
    representatives from R-Core, 'Bioconductor', 'Posit'/'tidyverse', and
    the wider R community.

Perform joint segmentation on two signal dimensions derived from 
    total read depth (intensity) and allele specific read depth (intensity) for 
    whole genome sequencing (WGS), whole exome sequencing (WES) and SNP array data.

Calculates a degree of spatial association between regionalizations 
    or categorical maps using the information-theoretical V-measure 
    (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>). It also
    offers an R implementation of the MapCurve method 
    (Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).

Semiparametric empirical likelihood ratio
    based tests of change-point with one-change or epidemic alternatives
    with data-based model diagnostic are contained.

A modular and extendable approach to extract (micro)saccades from gaze samples via an ensemble of methods.
  Although there is an agreement about a general definition of a saccade, the more specific details are harder to agree upon.
  Therefore, there are numerous algorithms that extract saccades based on various heuristics, which differ in the assumptions about velocity,
  acceleration, etc. The package uses three methods (Engbert and Kliegl (2003) <doi:10.1016/S0042-6989(03)00084-1>,
  Otero-Millan et al. (2014)<doi:10.1167/14.2.18>, and Nyström and Holmqvist (2010) <doi:10.3758/BRM.42.1.188>)
  to label individual samples and then applies a majority vote approach to identify saccades. The package includes three
  methods but can be extended via custom functions. It also uses a modular approach to compute velocity and
  acceleration from noisy samples. Finally, you can obtain methods votes per gaze sample instead of saccades.