c(person(given = "Julian",
family = "Friedrich",
role = c("aut", "cre"),
email = "julian.friedrich@medma.uni-heidelberg.de"),
person(given = "Hans-Peter",
family = "Hammes",
role = "aut"),
person(given = "Guido",
family = "Krenning",
role = "aut"))
Julian Friedrich [aut, cre],
Hans-Peter Hammes [aut],
Guido Krenning [aut]
Providing tools for microRNA (miRNA) text mining.
miRetrieve summarizes miRNA literature by extracting, counting, and
analyzing miRNA names, thus aiming at gaining biological insights into a
large amount of text within a short period of time. To do so, miRetrieve
uses regular expressions to extract miRNAs and tokenization to
identify meaningful miRNA associations. In addition, miRetrieve uses
the latest miRTarBase version 8.0 (Hsi-Yuan Huang et al. (2020)
"miRTarBase 2020: updates to the experimentally validated microRNA–target
interaction database" <doi:10.1093/nar/gkz896>) to display field-specific
miRNA-mRNA interactions. The most important functions are available as a
Shiny web application under <https://miretrieve.shinyapps.io/miRetrieve/>.
kableExtra, knitr, reshape2, rmarkdown, testthat

Stephan Artmann, Klaus Jung, Tim Beissbarth
globaltest, GlobalAncova, limma, RepeatedHighDim, corpcor,
MASS

Package for combined miRNA- and mRNA-testing.

Multiple Imputation using Weighted Quantile Sum Analysis
c( person("Paul M.", "Hargarten", email = "hargartenp@vcu.edu", role = c("aut", "cre")),
person("David C.", "Wheeler", email = "david.wheeler@vcuhealth.org ", role = c("aut","rev","ths")) )
spelling, knitr, mice (>= 3.3.0), pander (>= 0.6.3),
rmarkdown, testthat, wqs (>= 0.0.1)
Consider a set/mixture of continuous, correlated, and censored components/chemicals that are reasonable to combine in an index and share a common outcome. These components are also interval-censored between zero and upper thresholds, or detection limits, that may be different among the components. The `miWQS` package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes.  In summary, MI consists of three stages: (1) imputation, (2) analysis, and (3) pooling. First,  the missing values are imputed by bootstrapping (Lubin et.al (2004) <doi:10.1289/ehp.7199>), Bayesian imputation, or placing the below the detection limits in the first quantile (BDLQ1) (Ward et.al. (2014) <doi:10.1289/ehp.1307602>). Second, the estimate.wqs() function implements WQS regression if the components are complete, imputed, or missing (Carrico et.al. (2014) <doi:10.1007/s13253-014-0180-3>) . If the data is missing, BDLQ1 is automatically implemented.  Lastly, the pool.mi() function calculates the pooled statistics according to Rubin's rules (Rubin 1987).
Paul M. Hargarten [aut, cre],
David C. Wheeler [aut, rev, ths]