c(
person("James Joseph", "Balamuta",
email = "balamut2@illinois.edu",
role = c("aut", "cre", "cph"),
comment = c(ORCID = "0000-0003-2826-8458")
),
person("Steven Andrew", "Culpepper",
email = "sculpepp@illinois.edu",
role = c("aut", "cph"),
comment = c(ORCID = "0000-0003-4226-6176")
),
person("Jeffrey Alan", "Douglas",
email = "jeffdoug@illinois.edu",
role = c("aut", "cph")
)
)
Collection of data sets from various assessments that can be used to
evaluate psychometric models. These data sets have been analyzed in the
following papers that introduced new methodology as part of the application section:
Chen, Y., Culpepper, S. A., & Liang, F. (2020) <doi:10.1007/s11336-019-09693-2>
Culpepper, S. A. (2019a) <doi:10.1007/s11336-019-09683-4>,
Culpepper, S. A. (2019b) <doi:10.1007/s11336-018-9643-8>,
Culpepper, S. A., & Chen, Y. (2019) <doi:10.3102/1076998618791306>,
Culpepper, S. A., & Balamuta, J. J. (2017) <doi:10.1007/s11336-015-9484-7>,
and Culpepper, S. A. (2015) <doi:10.3102/1076998615595403>.
James Joseph Balamuta [aut, cre, cph]
(<https://orcid.org/0000-0003-2826-8458>),
Steven Andrew Culpepper [aut, cph]
(<https://orcid.org/0000-0003-4226-6176>),
Jeffrey Alan Douglas [aut, cph]

Collection of data sets from various assessments that can be used to
evaluate psychometric models. These data sets have been analyzed in the
following papers that introduced new methodology as part of the application section:
Yinghan Chen et al. (2021) <doi:10.1007/s11336-021-09750-9>,
Yinyin Chen et al. (2020) <doi:10.1007/s11336-019-09693-2>,
Culpepper, S. A. (2019a) <doi:10.1007/s11336-019-09683-4>,
Culpepper, S. A. (2019b) <doi:10.1007/s11336-018-9643-8>,
Culpepper, S. A., & Chen, Y. (2019) <doi:10.3102/1076998618791306>,
Culpepper, S. A., & Balamuta, J. J. (2017) <doi:10.1007/s11336-015-9484-7>,
and Culpepper, S. A. (2015) <doi:10.3102/1076998615595403>.

Empirical Differentially Methylated Regions Calculation
Optimized DMR analysis based on bimodal normal distribution
model and cost function for regional methylation analysis optimization.
It captures the regional methylation modification by taking the spatial
distribution of CpGs into account for the enrichment DNA methylation sequencing
data so as to optimize the definition of the empirical regions. Combined with
the dependent adjustment for regional p-value combination and DMR annotation.
c(person(given="Sheng", family="Li", role=c("aut","cre", "cph"), email = "shelly1436@gmail.com"),
person(given="Francine", family="Garrett-Bakelman", role="ctb"),
person(family="Akalin", given="Altuna", role="ctb"),
person(family="Zumbo", given="Paul", role="ctb"),
person(family="Melnick", given="Ari", role="ctb"),
person(family="Mason", given="Chris", role=c("ctb","ths")))
GenomicRanges, IRanges, data.table, mixtools, S4Vectors
'ACF.R' 'edmr.R' 'eDMR.sub.R' 'get.dist.cutoff.R' 'getDMR.R'
'getPeaks.R' 'helpers.R' 'myDiff.to.mixmdl.R' 'myDiffToDMR.R'
'plotCost.R' 'plotMdl1.R' 'filter.dmr.R' 'annotation.R'
'document_data.R'
Sheng Li [aut, cre, cph],
Francine Garrett-Bakelman [ctb],
Altuna Akalin [ctb],
Paul Zumbo [ctb],
Ari Melnick [ctb],
Chris Mason [ctb, ths]