Description: Tools to simulate alphanumeric alleles, impute genetic missing data and reconstruct non-recombinant haplotypes from pedigree databases in a deterministic way. Allelic simulations can be implemented taking into account many factors (such as number of families, markers, alleles per marker,
probability and proportion of missing genotypes, recombination rate, etc).
Genotype imputation can be used with simulated datasets or real databases (previously loaded in .ped format). Haplotype reconstruction can be carried
out even with missing data, since the program firstly imputes each family genotype (without a reference panel), to later reconstruct the corresponding
haplotypes for each family member. All this considering that each individual (due to meiosis) should unequivocally have two alleles per marker (one inherited
from each parent) and thus imputation and reconstruction results can be deterministically calculated.

Title: Identification of unique multilocus genotypes
Description: This package provides tools for the identification of
unique of multilocus genotypes when both genotyping error and
missing data are present. The package is targeted at those
working with large datasets and databases containing multiple
samples of each individual, a situation that is common in
conservation genetics, and particularly in non-invasive
wildlife sampling applications. Functions explicitly
incorporate missing data, and can tolerate allele mismatches

Title: Identifying unique multilocus genotypes where genotyping error
missing data may be present. The package is targeted at those

Title: Identifying Unique Multilocus Genotypes where Genotyping Error
person("Paul", "Galpern", email = "pgalpern@gmail.com", role = "aut"),
person("Todd", "Cross", email = "todd.cross@gmail.com", role = c("cre", "ctb")),
person("Katie", "Zarn", email = "katie.zarn@gmail.com", role = "ctb")
Description: Tools for the identification of unique of multilocus genotypes when both genotyping error and missing data may be present. The package is targeted at those working with large datasets and databases containing multiple samples of each individual, a situation that is common in conservation genetics, and particularly in non-invasive wildlife sampling applications. Functions explicitly incorporate missing data, and can tolerate allele mismatches created by genotyping error. If you use this tool, please cite the package using the journal article in Molecular Ecology Resources (Galpern et al., 2012). Please use citation('allelematch') to call the full citation. For users with access to the associated journal article, tutorial material is also available as supplementary material to the article describing this software, the citation for which can be called using citation('allelematch').