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.

Convert semi-structured log files (such as 'Apache' access.log files)
    into a tabular format (data.frame) using a standard template system.

Tabu search algorithm for binary configurations. A basic version of the algorithm as described by Fouskakis and Draper (2007) <doi:10.1111/j.1751-5823.2002.tb00174.x>.

Make it easy to deal with multiple cross-tables in data exploration, by
  creating them, manipulating them, and adding color helpers to highlight
  important informations (differences from totals, comparisons between lines or
  columns, contributions to variance, confidence intervals, odds ratios, etc.). 
  All functions are pipe-friendly and render data frames which can be easily 
  manipulated. In the same time, time-taking operations are done with 'data.table'
  to go faster with big dataframes. Tables can be exported with formats and colors 
  to 'Excel', plot and html.

To facilitate the analysis of positron emission tomography (PET) 
    time activity curve (TAC) data, and to encourage open science and 
    replicability, this package supports data loading and analysis of multiple 
    TAC file formats. Functions are available to analyze loaded TAC data for 
    individual participants or in batches. Major functionality includes weighted
    TAC merging by region of interest (ROI), calculating models including 
    standardized uptake value ratio (SUVR) and distribution volume ratio (DVR, 
    Logan et al. 1996 <doi:10.1097/00004647-199609000-00008>), basic plotting 
    functions and calculation of cut-off values (Aizenstein et al. 2008
    <doi:10.1001/archneur.65.11.1509>). Please see the walkthrough vignette for
    a detailed overview of 'tacmagic' functions.