Imports: RColorBrewer, data.table, celestial (>= 1.4.1)

Imports: RColorBrewer, data.table, celestial (>= 1.4.1), foreach
Suggests: ProFit, knitr, rmarkdown, EBImage, imager, LaplacesDemon,
Rfast, fastmatch, snow, doSNOW, bigmemory

Depends: R (>= 3.1), FITSio, magicaxis (>= 2.0.3)
Imports: RColorBrewer, data.table, celestial (>= 1.4.1), foreach,

Title: Biological Network Construction, Visualization and Analyses
Description: High-throughput experiments are now widely used in biological researches, which improves both the quality and quantity of omics data. Network-based presentation of these data has become a popular way in data analyses. This package mainly provides functions for biological network construction, visualization and analyses. Networks can be constructed either from experimental data or from a set of proteins and integrated PPI database. Based on them, users can perform traditional visualization, along with the subcellular localization based ones for Homo sapiens and Arabidopsis thaliana. Furthermore, analyses including topological statistics, functional module clustering and go profiling can also be achieved.

Title: Access Functions for ProPublica's APIs
person("Aleksander", "Dietrichson", email = "dietrichson@gmail.com", role=c("aut", "cre")),
person("Joselina", "Davit", email = "joselinadavyt@gmail.com", role = c("aut"))
Description: Provides wrapper functions to access the ProPublica's Congress and Campaign Finance APIs.
The Congress API provides near real-time access to legislative data from the House of
Representatives, the Senate and the Library of Congress.
The Campaign Finance API provides data from United States Federal Election Commission
filings and other sources. The API covers summary information for candidates and
committees, as well as certain types of itemized data.
For more information about these APIs go to: <https://www.propublica.org/datastore/apis>.
Imports: dplyr, stringr, httr, config, lubridate
Author: Aleksander Dietrichson [aut, cre],

Title: Penalized Regression with Second-Generation P-Values
person("Thomas", "Stewart", email = "thomas.stewart@vanderbilt.edu",
person("Jeffrey", "Blume", email = "j.blume@vanderbilt.edu",
Description: Implementation of penalized regression with second-generation p-values for variable
selection. The one-stage algorithm is extremely fast and the two-stage algorithm has lower
parameter estimation bias when data are highly correlated. S3 methods print(),
summary(), coef(), and predict() are available for both algorithms,
and S3 method plot() is available for the two-stage algorithm. Technical details
of the algorithms can be found at <arXiv:2012.07941>.
Packaged: 2021-01-04 14:04:41 UTC; buzzzuoyi