CpGassoc is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest,
adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no
need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept.
CpGassoc also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots,
and scatterplots for individual CpG sites.

Association Between Methylation and a Phenotype of Interest
Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest,
adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no
need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept.
Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots,
and scatterplots for individual CpG sites.

Is designed to test for association between methylation at CpG sites across the genome and a phenotype of interest, adjusting for any relevant covariates. The package can perform standard analyses of large datasets very quickly with no need to impute the data. It can also handle mixed effects models with chip or batch entering the model as a random intercept. Also includes tools to apply quality control filters, perform permutation tests, and create QQ plots, manhattan plots, and scatterplots for individual CpG sites.

Calculation of Log2 Counts per Million Cutoff from ERCC Controls
c(person("Tyler", "Grimes", role = "aut", comment = c(ORCID = "0000-0002-5653-3418")),
person(c("Travis", "L."), "Jensen", role = "aut", comment = c(ORCID = "0000-0002-0322-0469")),
person("Kristen", "Steenbergen", role = c("ctb", "cre"), email = "ksteenbergen@emmes.com"),
person("The Emmes Company LLC", role = "cph", comment = "Copyright holder of CpmERCCutoff package (Copyright (C) 2022)"),
person(c("Johannes", "B."), "Goll", role = "aut", comment = c(ORCID = "0000-0002-9968-4080")))
Implementation of the empirical method to derive log2 counts per million (CPM) cutoff to filter out lowly expressed genes using ERCC spike-ins as described in Goll and Bosinger et.al (2022)<doi:10.1101/2022.06.23.497396>. This package utilizes the synthetic mRNA control pairs developed by the External RNA Controls Consortium (ERCC) (ERCC 1 / ERCC 2) that are spiked into sample pairs at known ratios at various absolute abundances.  The relationship between the observed and expected fold changes is then used to empirically determine an optimal log2 CPM cutoff for filtering out lowly expressed genes.
Tyler Grimes [aut] (<https://orcid.org/0000-0002-5653-3418>),
Travis L. Jensen [aut] (<https://orcid.org/0000-0002-0322-0469>),
Kristen Steenbergen [ctb, cre],
The Emmes Company LLC [cph] (Copyright holder of CpmERCCutoff package
(Copyright (C) 2022)),
Johannes B. Goll [aut] (<https://orcid.org/0000-0002-9968-4080>)