person("Gordon", "McDonald", , "gordon.mcdonald@sydney.edu.au", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-2890-527X"))
Allows you to easily execute expensive compute operations only once, and save the resulting object to disk.
Gordon McDonald [aut, cre] (<https://orcid.org/0000-0002-2890-527X>)

c(person(given = "Danielle",
family = "Maeser",
role = c("aut", "cre"),
email = "maese005@umn.edu",
comment = c(ORCID = "0000-0002-3890-887X")),
person(given = 'Robert',
family = 'Gruener',
role = 'ctb',
email = 'rgruener@chicago.edu'))
Bridges in vitro drug screening with in vivo drug and biomarker discovery. Specifically, predicts in vivo or cancer patient drug response and biomarkers to enrich for response from cell line screening data. Builds model using ridge regression, and enables biomarker discovery by imputing drug response in large cancer molecular datasets. It also enables drug specific biomarker identification by correcting for general level of drug sensitivity shared among the population.
sva, preprocessCore, stringr, biomaRt, genefilter,
org.Hs.eg.db, GenomicFeatures,
TxDb.Hsapiens.UCSC.hg19.knownGene, genefilter, TCGAbiolinks,
BiocGenerics, GenomicRanges, IRanges, S4Vectors
parallel, ridge, car, glmnet, pls, sva, preprocessCore,
GenomicFeatures, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg19.knownGene, maftools, genefilter, gdata,
tidyverse, readxl, TCGAbiolinks, BiocGenerics, GenomicRanges,
IRanges, S4Vectors
Danielle Maeser [aut, cre] (<https://orcid.org/0000-0002-3890-887X>),
Robert Gruener [ctb]

sva, preprocessCore, stringr, biomaRt, genefilter,
GenomicFeatures, genefilter, BiocGenerics, GenomicRanges,
IRanges, S4Vectors, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg19.knownGene, maftools
parallel, ridge, car, glmnet, pls, sva, preprocessCore,
GenomicFeatures, genefilter, gdata, tidyverse, readxl,
BiocGenerics, GenomicRanges, IRanges, S4Vectors, org.Hs.eg.db,
TxDb.Hsapiens.UCSC.hg19.knownGene, maftools

Maximum likelihood tree models for oncogenesis
Anja von Heydebreck <anja.von.heydebreck@merck.de>,
contributions from Christiane Heiss <christiane.heiss@web.de>
Computing probabilistic tree models for oncogenesis based
on genetic data using maximum likelihood.