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.

This analytical framework is based on an analysis of the shape of the trait abundance distributions to better understand community assembly processes, and predict community dynamics under environmental changes. This framework mobilized a study of the relationship between the moments describing the shape of the distributions: the skewness and the kurtosis (SKR). The SKR allows the identification of commonalities in the shape of trait distributions across contrasting communities. Derived from the SKR, we developed mathematical parameters that summarise the complex pattern of distributions by assessing (i) the R², (ii) the Y-intercept, (iii) the slope, (iv) the functional stability of community (TADstab), and, (v) the distance from specific distribution families (i.e., the distance from the skew-uniform family a limit to the highest degree of evenness: TADeve).

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.

General framework to organize data, methods, and results used in
  reproducible 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, the TAF package comes with no
  dependencies. TAF forms a base layer for the 'icesTAF' package and other
  scientific applications.