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, margins 
    of error, 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 to 'Excel' and in html with formats and colors.

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.

Extensions to 'lattice', providing new high-level
    functions, methods for existing functions, panel functions, and a theme.

Suite of tools to support the practice of tada science. It includes an
    engaging package roulette that is designed to facilitate learning about 
    new packages.

Functions to organize data, methods, and results used in scientific
  analyses. A TAF analysis consists of four scripts (data.R, model.R, output.R,
  report.R) that are run sequentially. Each script starts by reading files from
  a previous step and ends with writing out files for the next step. Convenience
  functions are provided to version control the required data and software, run
  analyses, clean residues from previous runs, manage files, manipulate tables,
  and produce figures. With a focus on stability and reproducible analyses, TAF
  is designed to have no package dependencies. TAF forms a base layer for the
  'icesTAF' package and other scientific applications.