Contains selected variables from the time series profiles for statistical areas level 2 from the 2006, 2011, and 2016 censuses of population and housing, Australia. Also provides methods for viewing the questions asked for convenience during analysis.

A wrapper for the U.S. Census Bureau APIs that returns data frames of 
	Census data and metadata. Available datasets include the 
	Decennial Census, American Community Survey, Small Area Health Insurance Estimates,
	Small Area Income and Poverty Estimates, Population Estimates and Projections, and more.

Use the US Census API to collect summary data tables
    for SF1 and ACS datasets at arbitrary geographies.

Generates skeletons of closed 2D polygons using Voronoi diagrams. 
            It provides methods for 'sf', 'terra', and 'geos' objects to 
            compute polygon centerlines based on the generated skeletons.
            Voronoi, G. (1908) <doi:10.1515/crll.1908.134.198>.

Calculates centrality indices additional to the 'igraph' package centrality functions.

Generate mean and median weighted or unweighted spatial centers. 
    Functions are analogous to their identically named counterparts within 
    'ArcGIS Pro'. Median center methodology based off of Kuhn and Kuenne 
    (1962) <doi:10.1111/j.1467-9787.1962.tb00902.x>.

Find the numbers of test tubes that can be balanced in centrifuge
    rotors and show various ways to load them. Refer to Pham (2020)
    <doi:10.31224/osf.io/4xs38> for more information on package functionality.

Optimization solver based on the Cross-Entropy method.

It aims to find significant pathways through 
    network topology information. It has several advantages compared 
    with current pathway enrichment tools. First, pathway node instead 
    of single gene is taken as the basic unit when analysing networks 
    to meet the fact that genes must be constructed into complexes to 
    hold normal functions. Second, multiple network centrality measures are 
    applied simultaneously to measure importance of nodes from different 
    aspects to make a full view on the biological system. CePa extends 
    standard pathway enrichment methods, which include both over-representation 
    analysis procedure and gene-set analysis procedure. 
    <doi:10.1093/bioinformatics/btt008>.