Provides a set of fonts with permissive licences. This is
    useful when you want to avoid system fonts to make sure your
    outputs are reproducible.

Aids in analysing data from a food frequency questionnaire known as 
    the Harvard Service Food Frequency Questionnaire (HSFFQ).
    Functions from this package use answers from the HSFFQ to generate 
    estimates of daily consumed micronutrients, calories, macronutrients on 
    an individual level.
    The package also calculates food quotients on individual and group levels. 
    Foodquotient calculation is an often tedious step in the calculation of 
    total human energy expenditure (TEE) using the doubly labeled water method, 
    which is the gold standard for measuring TEE.

Easily create graphs of the inter-relationships between
    functions in an environment.

Enhances the functionality of the mvbutils::foodweb() program. The matrix-format output of the original program contains identical row names and column names, each name representing a retrieved function. This format is enhanced by using the find_funs() program [see Sebastian (2017) <https://sebastiansauer.github.io/finds_funs/>] to concatenate the package name to the function name. Each package is assigned a unique color, that is used to color code the text naming the packages and the functions. This color coding is extended to the entries of value "1" within the matrix, indicating the pattern of ancestor and descendent functions.

Basic analysis of all penalties taken in the German men's Bundesliga
  between the start of its inaugural season and May 2017. The main functions are
  suitable printing and plotting functions. Flexible selection of a player is
  supported via grep. Missed penalties can easily be included or excluded, depending
  on the user's wishes.

This is the first package allowing for the estimation,
             visualization and prediction of the most well-known 
             football models: double Poisson, bivariate Poisson,
             Skellam, student_t, diagonal-inflated bivariate Poisson, and
             zero-inflated Skellam. It supports both maximum likelihood estimation (MLE, for 
             'static' models only) and Bayesian inference.
             For Bayesian methods, it incorporates several techniques:
             MCMC sampling with Hamiltonian Monte Carlo, variational inference using
             either the Pathfinder algorithm or Automatic Differentiation Variational
             Inference (ADVI), and the Laplace approximation.
             The package compiles all the 'CmdStan' models once during installation
             using the 'instantiate' package.
             The model construction relies on the most well-known football references, such as 
             Dixon and Coles (1997) <doi:10.1111/1467-9876.00065>,
             Karlis and Ntzoufras (2003) <doi:10.1111/1467-9884.00366> and
             Egidi, Pauli and Torelli (2018) <doi:10.1177/1471082X18798414>.