Authors@R: c(person("jesse","koops", role = c("aut", "cre"), email="jesse.koops@cito.nl"),
person("Eva","de Schipper", role = c("aut")),
person("Ivailo","Partchev", role = c("aut", "ctb")),
person("Gunter", "Maris", role = c("aut", "ctb")),
person("Timo", "Bechger", role = c("aut", "ctb")),
person("Gareth", "Watts", role = c("aut", "cph"), comment = "author of jquery.sparkline"),
person("Hakim", "El Hattab", role = c("aut", "cph"), comment = "author of zoom.js"))
Gareth Watts [aut, cph] (author of jquery.sparkline),
Hakim El Hattab [aut, cph] (author of zoom.js)

Imports: shiny (>= 1.3.0), shinyBS (>= 0.6), DT (>= 0.4), htmltools (>=
0.3.6), htmlwidgets (>= 1.3), shinyjs (>= 1.0), shinyFiles (>=
networkD3 (>= 0.4), Cairo, graphics, grDevices, methods, utils,

Imports: shiny (>= 1.3.0), shinyBS (>= 0.6), DT (>= 0.9), htmltools (>=
0.4.0), htmlwidgets (>= 1.3), shinyjs (>= 1.0), shinyFiles (>=

person("Gareth", "Watts", role = c("cph"), comment = "author of jquery.sparkline"),
person("Hakim", "El Hattab", role = c("cph"), comment = "author of zoom.js"))
Suggests: knitr, psych, sirt, MLCIRTwithin, rmarkdown
Gareth Watts [cph] (author of jquery.sparkline),
Hakim El Hattab [cph] (author of zoom.js)

Authors@R: c(person("Jesse","Koops", role = c("aut", "cre"), email="jesse.koops@cito.nl"),

0.4.0), htmlwidgets (>= 1.3), shinyjs (>= 2.0), shinyFiles (>=

Description: It handles numerics, characters, factors, and logicals.

Title: Compare Two Dataframes and Return Adds, Changes, and Deletes
Description: Compares two dataframes with a common key
and returns the delta records. The package will return
three dataframes that contain the added, changed,

Title: Dynamic Functional Connectivity Analysis
Authors@R: c(person("Zikai", "Lin", email = "ziklin@iu.edu", role = c("aut", "cre")),
person("Maria", "Kudela", email = "maria.kudela@gmail.com", role = c("aut")),
person("Jaroslaw","Harezlak",email = "harezlak@iu.edu", role = c("aut")),
person("Mario","Dzemidzic", email = "mdzemidz@iupui.edu", role = c("aut")))
Description: An implementation of multivariate linear process bootstrap (MLPB) method and sliding window technique to assess the dynamic functional connectivity (dFC) estimate by providing its confidence bands, based on Maria Kudela (2017) <doi: 10.1016/j.neuroimage.2017.01.056>.
It also integrates features to visualize non-zero coverage for selected a-priori regions of interest estimated by the dynamic functional connectivity model (dFCM) and dynamic functional connectivity (dFC) curves for reward-related a-priori regions of interest where the activation-based analysis reported.
Imports: doParallel, nlme, parallel, foreach, ggplot2, knitr,
RColorBrewer, fields, latex2exp, mgcv, gplots, splines, stats,
stringr, graphics, data.table, gtools, Rcpp (>= 0.12.18)