Title: Large-Scale Social Network Analysis
person("Nazrul", "Shaikh", email = "networkgroupr@gmail.com", role = c("aut","cre")),
person("Xu", "Dong", email = "x.dong3@umiami.edu", role = "aut"),
person("Luis", "Castro", email = "luiscastroabril@miami.edu", role = "aut"),
person("Christian", "Llano", email = "cxl985@miami.edu", role = "ctb")
Description: We present an implementation of the algorithms required to simulate large-scale social networks and retrieve their most relevant metrics.
Imports: doParallel (>= 1.0.10), parallel (>= 3.3.2), foreach (>=

Imports: doParallel (>= 1.0.11), parallel (>= 3.4.3), foreach (>=

Description: We present an implementation of the algorithms required to simulate
large-scale social networks and retrieve their most relevant metrics. Details
can be found in the accompanying scientific paper on the Journal

Title: Finds the Critical Sequential Point of Stability for a Pearson
Authors@R: person("Johannes", "Titz", email = "johannes.titz@gmail.com",
Description: Finds the critical sample size ("point of stability") for a
correlation to stabilize in Schoenbrodt and Perugini's definition of
Imports: Rcpp (>= 1.0.1), plyr, MASS, pbapply

Description: Finds the critical sample size ("critical point of stability") for a

Imports: Rcpp (>= 1.0.1), plyr, MASS, future
Suggests: knitr, rmarkdown, testthat (>= 2.1.0), covr, microbenchmark

Imports: Rcpp, plyr, MASS, lifecycle, tibble, stats, pbapply
Author: Johannes Titz [aut, cre, cph] (<https://orcid.org/0000-0002-1102-5719>)

Imports: Rcpp, plyr, MASS, lifecycle, tibble, stats, pbmcapply

Authors@R: c(person("Dayne", "Batten", email = "dbatten@bandwidth.com", role = c("aut", "cre")), person(c("Maja", "Pohar", "Perme"), role="ctb", email="maja.pohar@mf.uni-lj.si"), person("Mette", "Gerster", role = "ctb", email="mgerster@health.sdu.dk"))
Description: Computes pseudo-observations for survival analysis on right-censored data based on restricted mean survival time.

Title: Quality Control of Sequencing Data
person("Alboukadel", "Kassambara", role = c("aut", "cre"), email = "alboukadel.kassambara@gmail.com")
Description: 'FASTQC' is the most widely used tool for evaluating the quality of high throughput sequencing data.
It produces, for each sample, an html report and a compressed file containing the raw data.
If you have hundreds of samples, you are not going to open up each 'HTML' page.
You need some way of looking at these data in aggregate.
'fastqcr' Provides helper functions to easily parse, aggregate and analyze
'FastQC' reports for large numbers of samples. It provides a convenient solution for building
a 'Multi-QC' report, as well as, a 'one-sample' report with result interpretations.
Imports: dplyr, grid, gridExtra, ggplot2, magrittr, readr, rmarkdown(>=
1.4), rvest, tibble, tidyr, scales, stats, utils, xml2
Collate: 'utilities.R' 'fastqc.R' 'fastqc_install.R' 'qc_aggregate.R'
Packaged: 2017-04-11 10:31:32 UTC; kassambara