Description: A simple system for saving and loading objects in R. Long running
computations can be stashed after the first run and then reloaded the next
time. Dependencies can be added to ensure that a computation is re-run if
any of its dependencies or inputs have changed.
Imports: formatR (>= 1.7), readr (>= 1.3.0), digest (>= 0.6.0), tibble

Description: A simple system for saving and loading objects in
R. Long running computations can be stashed after the first run and
then reloaded the next time. Dependencies can be added to ensure that
a computation is re-run if any of its dependencies or inputs have
Author: Joshua H Cook [aut, cre] (<https://orcid.org/0000-0001-9815-6879>),

Depends: R (>= 3.2.0), Familias, paramlink, expm, IBDsim
Description: Main function LR2 calculates likelihood ratio for non-inbred relationships accounting for mutation, silent alleles and theta correction. Egeland, Pinto and Amorim (2017) <DOI:10.1016/j.fsigen.2017.04.018>.

Title: Decipher Mutational Signatures from Somatic Mutational Catalogs
Description: Cancer cells accumulate DNA mutations as result of DNA damage and DNA repair processes. This computational framework is aimed at deciphering DNA mutational signatures operating in cancer. The input is a numeric matrix of DNA mutation counts detected in a panel of cancer samples. The framework performs Non-negative Matrix Factorization to extract the most likely signatures explaining the observed set of DNA mutations. The framework relies on parallelization and is optimized for use on multi-core systems. This framework is an R-based implementation of the original MATLAB WTSI framework by Alexandrov LB et al (2013) <DOI:10.1016/j.celrep.2012.12.008>.
Depends: R(>= 3.1.0), doParallel, ggplot2, foreach

Author: Damiano Fantini, Vania Vidimar, Joshua J Meeks
Description: Cancer cells accumulate DNA mutations as result of DNA damage and DNA repair processes. This computational framework is aimed at deciphering DNA mutational signatures operating in cancer. The framework includes modules that support raw data import and processing, mutational signature extraction, and results interpretation and visualization. The framework accepts widely used file formats storing information about DNA variants, such as Variant Call Format files. The framework performs Non-Negative Matrix Factorization to extract mutational signatures explaining the observed set of DNA mutations. Bootstrapping is performed as part of the analysis. The framework supports parallelization and is optimized for use on multi-core systems. The software is based on a custom R-based implementation of the original MATLAB WTSI framework by Alexandrov LB et al (2013) <doi:10.1016/j.celrep.2012.12.008>.
Imports: graphics, stats, cluster, doParallel, ggplot2, pracma, proxy,
Suggests: dplyr, reshape2, kableExtra, gridExtra,