Depends: R(>= 2.10.0), VIM, mi, mice, Amelia
Description: Plots to visualize missing data and datasets that were
produced by Multiple Imputation. The idea behind this package
is to visualize MI objects produced by the R packages mi, mice
or Amelia II to get a better understanding of the imputed data

Depends: R(>= 2.10.0), VIM, mi, mice, Amelia, lattice, vcd
or Amelia II to provide a better understanding of the imputed

Title: Genome-Wide Discovery of Pre-miRNAs with few Labeled Examples
Description: Machine learning method specifically designed for
pre-miRNA prediction. It takes advantage of unlabeled sequences to improve
the prediction rates even when there are just a few positive examples, when
the negative examples are unreliable or are not good representatives of
its class. Furthermore, the method can automatically search for negative
examples if the user is unable to provide them. MiRNAss can find a good
boundary to divide the pre-miRNAs from other groups of sequences; it
automatically optimizes the threshold that defines the classes boundaries,
and thus, it is robust to high class imbalance. Each step of the method is
scalable and can handle large volumes of data.
Imports: Matrix, stats, Rcpp, CORElearn, RSpectra,

Title: MicroRNA Microarray Data Analysis
microRNA profiling data analyses.  Analytical platforms include
traditional hybridization microarray, CGH, beads-based

Title: Left-Censored Recurrent Events Survival Models
Description: Fitting recurrent events survival models for
left-censored data with multiple imputation of the number of previous episodes.
See Hernández-Herrera G, Moriña D, Navarro A. (2020) <arXiv:2007.15031>.
Depends: R (>= 3.5.0), COMPoissonReg, matrixStats, survival, stringi
Author: David Moriña [aut, cre] (<https://orcid.org/0000-0001-5949-7443>),

Description: 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/>.
1.1.3), tidytext (>= 0.3.1), topicmodels (>= 0.2.12), wordcloud
Suggests: kableExtra, knitr, reshape2, rmarkdown, testthat