Title: Create and Query a Local 'PubTator' Database
Description: 'PubTator' <https://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/PubTator/> is a National Center for Biotechnology Information (NCBI) tool that enhances the annotation of articles on PubMed <https://www.ncbi.nlm.nih.gov/pubmed/>. It makes it possible to rapidly identify potential relationships between genes or proteins using text mining techniques. In contrast, manually searching for and reading the annotated articles would be very time consuming. 'PubTator' offers both an online interface and a RESTful API, however, neither of these approaches are well suited for frequent, high-throughput analyses. The package 'pubtatordb' provides a set of functions that make it easy for the average R user to download 'PubTator' annotations, create, and then query a local version of the database.
Imports: DBI, R.utils, RSQLite, assertthat, dplyr, readr

Description: R interface to pullword service for Natural Language Processing
in Chinese. It enables users to extract valuable words from text by deep learning models.
For more details please visit the official site (in Chinese) http://pullword.com/.

Description: R Interface to Pullword Service for natural language processing

For more details please visit the official site (in Chinese) <http://www.pullword.com/>.

Title: Partitioning Using Local Subregions
Description: A method of clustering functional data using
subregion information of the curves. It is intended to supplement the
'fda' and 'fda.usc' packages in functional data object clustering. It
also facilitates the printing and plotting of the results in a tree
format and limits the partitioning candidates into a specific set of
ggplot2, graphics, grDevices, monoClust (>= 1.2.0), purrr (>=
Suggests: covr, knitr, lubridate, rmarkdown, testthat, vdiffr
Author: Mark Greenwood [aut] (<https://orcid.org/0000-0001-6933-1201>),

ggplot2, graphics, monoClust (>= 1.2.0), purrr (>= 0.3.0),

Title: Parallel Utilities for Lambda Selection along a Regularization
Authors@R: c(person("Zachary", "Kurtz", role = c("aut", "cre"), email="zdkurtz@gmail.com"),
person("Christian", "M\u00FCller", role = c("aut", "ctb"), email="cmueller@simonsfoundation.org"))
Description: Model selection for penalized graphical models using the Stability Approach to Regularization Selection ('StARS'), with options for speed-ups including Bounded StARS (B-StARS), batch computing, and other stability metrics (e.g., graphlet stability G-StARS).
Suggests: BatchJobs, orca, huge, QUIC, glmnet, network, cluster,
Imports: methods, parallel, graphics, stats, utils, Matrix