Provides functionality for testing familial hypotheses. Supports testing centers 
    belonging to the Huber family. Testing is carried out using the Bayesian bootstrap. One- and 
    two-sample tests are supported, as are directional tests. Methods for visualizing output are 
    provided.

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.

Grows families of features by selecting features that maximize a weighted score calculated from empirical feature scores and graphical knowledge. The final weighted score for a feature is determined by summing a feature's family-weighted scores across all families in which the feature appears.

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>.