Sinan Shi, David Perez-Suarez, Steve Harris, Niall MacCallum, David
Brealey, Mervyn Singer, James Hetherington
An electronic health care record (EHR) data cleaning and processing
platform. It focus on heterogeneous high resolution longitudinal data. It works with
Critical Care Health Informatics Collaborative (CCHIC) dataset. It is
created to address various data reliability and accessibility problems of
EHRs as such.
data.table, XML, yaml, Rcpp, ggplot2, methods, pander, knitr
'RcppExports.R' 'ccRecord.R' 'ccTable.R' 'cchic_xml.R'
'data.R' 'data.quality.report.R' 'deltaTime.R' 'demographics.R'
'filter.categorical.R' 'filter.missingness.R' 'filter.range.R'
'imputation.R' 'stdid.R' 'utilities.R' 'zzz.R'

A Tidy Data Model for Natural Language Processing
Provides a set of fast tools for converting a textual corpus into a set of normalized tables. The underlying natural language processing pipeline utilizes the Stanford's CoreNLP library. Exposed annotation tasks include tokenization, part of speech tagging, named entity recognition, entity linking, sentiment analysis, dependency parsing, coreference resolution, and information extraction. Several datasets containing token unigram, part of speech tag, and dependency type frequencies are also included to assist with analyses. Currently supports parsing text in English, French, German, and Spanish.
Java (>= 7.0); Stanford CoreNLP
<http://nlp.stanford.edu/ software/corenlp.shtml> (>= 3.5.2)

c(person(given = "Taylor B.", family = "Arnold", email = "taylor.arnold@acm.org", role = c("aut", "cre")))
Provides a set of fast tools for converting a textual corpus into a set of normalized
tables. Users may make use of a Python back end with 'spaCy' <https://spacy.io>
or the Java back end 'CoreNLP' <http://stanfordnlp.github.io/CoreNLP/>. A minimal back
end with no external dependencies is also provided. Exposed annotation tasks include
tokenization, part of speech tagging, named entity recognition, entity linking, sentiment
analysis, dependency parsing, coreference resolution, and word embeddings. Summary
statistics regarding token unigram, part of speech tag, and dependency type frequencies
are also included to assist with analyses.
dplyr (>= 0.5.0), readr (>= 1.1.0), Matrix (>= 1.2), stringi,
stats, methods, utils
Python (>= 2.7.0); spaCy <https://spacy.io/> (>=
1.8); Java (>= 7.0); Stanford CoreNLP
<http://nlp.stanford.edu/software/corenlp.shtml> (>= 3.7.0)