Single unified interface for end-to-end modelling of regression, 
    categorical and time-to-event (survival) outcomes. Models created using
    familiar are self-containing, and their use does not require additional
    information such as baseline survival, feature clustering, or feature
    transformation and normalisation parameters. Model performance,
    calibration, risk group stratification, (permutation) variable importance,
    individual conditional expectation, partial dependence, and more, are
    assessed automatically as part of the evaluation process and exported in
    tabular format and plotted, and may also be computed manually using export
    and plot functions. Where possible, metrics and values obtained during the
    evaluation process come with confidence intervals.

An interface to the core 'Familias' functions which are
    programmed in C++. The implementation is described in Egeland, Mostad and Olaisen
    (1997) <doi:10.1016/S1355-0306(97)72202-0> and Simonsson and Mostad
    (2016) <doi:10.1016/j.fsigen.2016.04.005>.

Tools to study lineages, grandparenthood, loss of close relatives, kinship networks and other topics in multi-generation populations.

Fits probability distributions to data and plugs into the
    'probaverse' suite of R packages so distribution objects are ready for
    further manipulation and evaluation. Supports methods such as maximum
    likelihood and L-moments, and provides diagnostics including empirical
    ranking and quantile score.

Estimate parameters of univariate probability distributions 
  with maximum likelihood and Bayesian methods.

Given a set of parameters describing model dynamics and a corresponding cost function,
    FAMoS performs a dynamic forward-backward model selection on a specified selection
    criterion. It also applies a non-local swap search method. Works on any cost function. 
    For detailed information see Gabel et al. (2019) <doi:10.1371/journal.pcbi.1007230>.

Given vectors of family sizes and number of affecteds per family, calculates the risk of disease recurrence in an unaffected person, conditional on a family having at least k affected members. Methods also model heterogeneity of disease risk across families by fitting a mixture model, allowing for high and low risk families.