A set of tools that enables using 'OxCal' from within R. 'OxCal' (<https://c14.arch.ox.ac.uk/oxcal.html>) is a standard archaeological tool intended to provide 14C calibration and analysis of archaeological and environmental chronological information. 'OxcAAR' allows simple calibration with 'Oxcal' and plotting of the results as well as the execution of sophisticated ('OxCal') code and the import of the results of bulk analysis and complex Bayesian sequential calibration.

Provides utilities for processing of Oxy-Bisulfite microarray data 
  (e.g. via the Illumina Infinium platform, <http://www.illumina.com>) 
  with tandem arrays, one using conventional
  bisulfite conversion, the other using oxy-bisulfite conversion.

Collects a list of your third party R packages, and
    scans them with the 'OSS' Index provided by 'Sonatype', reporting back
    on any vulnerabilities that are found in the third party packages you
    use.

Functions for plotting Australia's coastline and state
  boundaries.

Maps of Australian coastline and administrative regions. Data 
 can be drawn or accessed directly as simple features objects. Includes
 simple functions for country or state maps of Australia and in-built data
 sets of administrative regions from the Australian Bureau of Statistics 
 <https://www.abs.gov.au/>. Layers include electoral divisions and local 
 government areas, simplified from the original sources but with sufficient 
 detail to allow mapping of a local municipality.

Contains functions for data preparation,
	prediction of transition probabilities,
	estimating semi-parametric regression models
	and for implementing nonparametric estimators
	for other quantities. See Meira-Machado and
	Roca-Pardiñas (2011) <doi:10.18637/jss.v038.i03>.

It provides tools for conducting performance attribution for equity portfolios. The package uses two methods: the Brinson method and a regression-based analysis.

Implements partition-assisted clustering and multiple alignments of networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set. Please see Li et al. (2017) <doi:10.1371/journal.pcbi.1005875> for detail method description.