Clustering for Data with Sensitive Attribute
Clustering for categorical and mixed-type of data, to preventing classification biases due to race,
gender or others sensitive attributes.
This algorithm is an extension of the methodology proposed by "Santos & Heras (2020) <doi:10.28945/4643>".
dplyr, irr, rlist, tidyr, parallel, magrittr, cluster, base,
data.table, foreach, doParallel

Simulation of Time-to-Event Family Data and Penetrance
Estimation
Yun-Hee Choi, Karen Kopciuk, Wenqing He, Laurent Briollais
Generates time-to-event family data associated with mutated genes arising from various family study designs such as population-based, clinic- based, or multi-stage designs and provides functions to fit family data accounting for the study design and estimate age-dependent penetrance functions. It also provides carrier probabilities for those with missing genotypes and penetrance estimations are based on an Expectation-Maximization algorithm when genotypes are missing.
R(>= 3.0.0), base, MASS, truncnorm, survival, kinship2

Family Age-at-Onset Data Simulation and Penetrance Estimation
Simulates age-at-onset traits associated with a segregating major gene in family data
obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment
correction is utilized to estimate age-dependent penetrance functions either parametrically from
the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm
can infer missing genotypes and carrier probabilities estimated from family's genotype and
phenotype information or from a fitted model. Plot functions include pedigrees of simulated
families and predicted penetrance curves based on specified parameter values.

Simulates age-at-onset traits associated with a segregating major gene in family data
obtained from population-based, clinic-based, or multi-stage designs. Appropriate ascertainment
correction is utilized to estimate age-dependent penetrance functions either parametrically from
the fitted model or nonparametrically from the data. The Expectation and Maximization algorithm
can infer missing genotypes and carrier probabilities estimated from family's genotype and
phenotype information or from a fitted model. Plot functions include pedigrees of simulated
families and predicted penetrance curves based on specified parameter values.
For more information see Choi, Y.-H., Briollais, L., He, W. and Kopciuk, K. (2021) FamEvent: An
R Package for Generating and Modeling Time-to-Event Data in Family Designs, Journal of Statistical
Software 97 (7), 1-30. <doi:10.18637/jss.v097.i07>.