dfConn: Dynamic Functional Connectivity Analysis

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.

Version: 0.2.1
Depends: R (≥ 2.10)
Imports: doParallel, nlme, parallel, foreach, ggplot2, fields, gplots, splines, stats, stringr, graphics, data.table, gtools, Rcpp (≥ 0.12.18)
LinkingTo: Rcpp, RcppArmadillo
Suggests: iterators, testthat, itertools, mgcv, latex2exp
Published: 2019-06-13
Author: Zikai Lin [aut, cre], Maria Kudela [aut], Jaroslaw Harezlak [aut], Mario Dzemidzic [aut]
Maintainer: Zikai Lin <ziklin at iu.edu>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README
CRAN checks: dfConn results


Reference manual: dfConn.pdf
Package source: dfConn_0.2.1.tar.gz
Windows binaries: r-devel: dfConn_0.2.1.zip, r-release: dfConn_0.2.1.zip, r-oldrel: dfConn_0.2.1.zip
OS X binaries: r-release: dfConn_0.2.1.tgz, r-oldrel: dfConn_0.2.1.tgz
Old sources: dfConn archive


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