In bulk epigenome/transcriptome experiments, molecular expression
is measured in a tissue, which is a mixture of multiple types of cells.
This package tests association of a disease/phenotype with a molecular marker
for each cell type.
The proportion of cell types in each sample needs to be given as input.
The package is applicable to epigenome-wide association study (EWAS) and
differential gene expression analysis.
Takeuchi and Kato (submitted)
"omicwas: cell-type-specific epigenome-wide and transcriptome association study".
broom, data.table, dplyr, ff, glmnet, magrittr, MASS,
matrixStats, parallel, PCDimension, rlang, sva, tidyr

broom, data.table, dplyr, ff, glmnet, magrittr, MASS,
matrixStats, parallel, purrr, rlang, tidyr

Helper Tools for Managing Data, Dates, Missing Values, and Text
c(
person(
given = 'Adam B.',
family = 'Smith',
role = c('cre', 'aut'),
email = 'adam.smith@mobot.org',
comment = c(ORCID = '0000-0002-6420-1659')
)
)
An assortment of helper functions for managing data (e.g.,
rotating values in matrices by a user-defined angle, switching from
row- to column-indexing), dates (e.g., intuiting year from messy date
strings), handling missing values (e.g., removing elements/rows across
multiple vectors or matrices if any have an NA), and text (e.g.,
flushing reports to the console in real-time).

Utility functions for querying electronic health record (EHR) data in
'OMOP' Common Data Model <https://www.ohdsi.org/data-standardization/the-common-data-model/>
databases using a 'tidyverse' approach based on 'dbplyr' lazy queries.
This allows efficient in-database querying and data wrangling without explicit
writing of 'SQL' queries.
R(>= 3.0.0), dbplyr, dplyr, DBI, RSQLite

Model and Solve Mixed Integer Linear Programs
person("Dirk", "Schumacher", email = "mail@dirk-schumacher.net",
role = c("aut", "cre", "cph"))
Model mixed integer linear programs in an algebraic way directly in R.
The model is solver-independent and thus offers the possibility
to solve a model with different solvers. It currently only supports
linear constraints and objective functions. See the 'ompr'
website <https://dirkschumacher.github.io/ompr> for more information,
documentation and examples.