An easy way to examine archaeological count data. This
    package provides several tests and measures of diversity:
    heterogeneity and evenness (Brillouin, Shannon, Simpson, etc.),
    richness and rarefaction (Chao1, Chao2, ACE, ICE, etc.), turnover and
    similarity (Brainerd-Robinson, etc.). It allows to easily visualize
    count data and statistical thresholds: rank vs abundance plots,
    heatmaps, Ford (1962) and Bertin (1977) diagrams, etc.

Facilities to work with vector and raster data in efficient 
 repeatable and systematic work flow. Missing functionality in existing packages 
 is included here to allow extraction from raster data with 'simple features' and 
 'Spatial' types and to make extraction consistent and straightforward. Extract cell 
 numbers from raster data and  return the cells as a data frame 
 rather than as lists of matrices or vectors. The functions here allow spatial data 
 to be used without special handling for the format currently in use.

The 'tabularmap' is one of the visualization methods for 
    efficiently displaying data consisting of multiple elements by tiling them.
    When dealing with geospatial, it corrects for differences in visibility 
    between areas.

The maximum likelihood classifier (MLC) is one of the most common classifiers used for 
  remote sensing imagery. 
  This package uses 'RcppArmadillo' to provide a fast implementation of the MLC to train 
  and predict over tabular data (data.frame).
  The algorithms were based on Mather (1985) <doi:10.1080/01431168508948456> method.

Generates pretty console output for tables allowing for full customization
  of cell colors, font type, borders and many others attributes. It also supports 'multibyte'
  characters and nested tables.

Efficient tabulation with Stata-like output.
	For each unique value of the variable, it shows the number of 
	observations with that value, proportion of observations with that
	value, and cumulative proportion, in descending order of frequency.
	Accepts data.table, tibble, or data.frame as input. 
	Efficient with big data: if you give it a data.table, 
	tab() uses data.table syntax.