Thore Egeland and Magnus Dehli Vigeland.
Functionality provided for conditional simulation, likelihoods and plotting of pedigrees, mostly as a wrapper for 'paramlink'. Users typically start by exporting from the Windows version of 'Familias'.

Family Sequence Kernel Association Test for Rare and Common
Variants
FamSKAT-RC is a family-based association kernel test for both
rare and common variants. This test is general and several special cases
are known as other methods: famSKAT, which only focuses on rare variants
in family-based data, SKAT, which focuses on rare variants in
population-based data (unrelated individuals), and SKAT-RC, which focuses
on both rare and common variants in population-based data. When one
applies famSKAT-RC and sets the value of phi to 1, famSKAT-RC becomes
famSKAT. When one applies famSKAT-RC and set the value of phi to 1 and the
kinship matrix to the identity matrix, famSKAT-RC becomes SKAT. When one
applies famSKAT-RC and set the kinship matrix (fullkins) to the identity
matrix (and phi is not equal to 1), famSKAT-RC becomes SKAT-RC. We also
include a small sample synthetic pedigree to demonstrate the method with.
For more details see Saad M and Wijsman EM (2014) <doi:10.1002/gepi.21844>.

Includes FAME storage and retrieval function, as well as functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies.

Statistical Tests of Familial Hypotheses
person("Ryan", "Thompson",  email = "ryan.thompson@monash.edu", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-9002-0448"))
Provides functionality for testing familial hypotheses. Currently supports tests of
the Huber family of centers. 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.
Ryan Thompson [aut, cre] (<https://orcid.org/0000-0002-9002-0448>)

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.