This is an experimental package bundle based on the
original R package for genetic data analysis (R/gap), it currently
consists of R/gap, pathmix and pointer.

Jing Hua Zhao in collaboration with other colleagues, and with
help from Kurt Hornik and Brian Ripley of the R core development team
This is an integrated package for genetic data analysis
of both population and family data. Currently it contains
functions for sample size calculations of both population-based
and family-based designs, classic twin ACE/ADE/AE/CE models,
probability of familial disease aggregation, kinship calculation,
some statistics in linkage analysis, and association analysis
involving one or more genetic markers including haplotype
analysis with or without environmental covariates.
Programs included in this package by Jing Hua Zhao will be
under GPL. Specific requirement may be possible for programs
written by other authors.

It is designed as an integrated package for genetic data
analysis of both population and family data. Currently, it
contains functions for sample size calculations of both
population-based and family-based designs, classic twin models,
probability of familial disease aggregation, kinship calculation,
some statistics in linkage analysis, and association analysis
involving one or more genetic markers including haplotype
analysis with or without environmental covariates.

Jing Hua Zhao and colleagues with inputs
from Kurt Hornik and Brian Ripley

Jing Hua Zhao and colleagues with inputs from Kurt Hornik and
Brian Ripley
Design, Hmisc, haplo.stats, magic, pedigree, MASS
It is designed as an integrated package for genetic data
analysis of both population and family data. Currently, it
contains functions for sample size calculations of both
population-based and family-based designs, classic twin models,
probability of familial disease aggregation, kinship
calculation, some statistics in linkage analysis, and
association analysis involving one or more genetic markers
including haplotype analysis with or without environmental
covariates.