Depends: R (>= 3.0.0), Matrix, tseriesChaos, fields

Depends: R (>= 3.0.0), Matrix, tseriesChaos, fields, pracma

Author: Moreno I. Coco and Rick Dale with contributions of James D. Dixon and John Nash
of two time-series, of either categorical or
Depends: R (>= 3.0.0), Matrix, tseriesChaos, fields, plot3D, pracma
Collate: 'ami.R' 'CTcrqa.R' 'calcphi.R' 'checkts.R' 'crqa.R'
'runcrqa.R' 'simts.R' 'spdiags.R' 'takephi.R' 'theiler.R'

e.g., recurrence rate. Please refer to by Coco and Dale (2014)
<doi:10.3389/fpsyg.2014.00510> for further details about the method.

Title: Recurrence Quantification Analysis for Categorical and
person("Moreno  I.", "Coco", role = c("cre","aut"),
person("James D.","Dixon", role = "ctb",
Auto, Cross and Multi-dimensional recurrence quantification analysis.
Different methods for computing recurrence, cross vs. multidimensional
or profile iti.e., only looking at the diagonal recurrent points,
as well as functions for optimization and plotting are proposed.
Please refer to by Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510>
Imports: Matrix, pracma, tseriesChaos, gplots, plot3D, FSA, rdist
Collate: 'crqa.R' 'crqa_helpers.R' 'drpfromts.R' 'lorenzattractor.R'
'plotRP.R' 'simts.R' 'spdiags.R' 'wincrqa.R' 'windowdrp.R'

or profile it -only looking at the diagonal recurrent points-
Please refer to Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510>

Imports: Matrix, pracma, tseriesChaos, gplots, plot3D, rdist

Title: Cross-Recurrence Quantification Analysis for Dynamic Natural
for word series, from text, known as categorical
recurrence analysis. Uses the 'crqa' R package
Functions are wrappers to facilitate exploration
Depends: R (>= 3.0.0), crqa, tm, corpus, gutenbergr, RCurl

Title: Competing risks regression for Stratified and Clustered data
Author: Bingqing Zhou and Aurelien Latouche
Description: Extension of cmprsk to Stratified and Clustered data

Title: Competing Risks Regression for Stratified and Clustered Data
Description: Extension of 'cmprsk' to Stratified and Clustered data.
A goodness of fit test for Fine-Gray model is also provided.
Methods are detailed in the following articles: Zhou et al. (2011) <doi:10.1111/j.1541-0420.2010.01493.x>,
Zhou et al. (2012) <doi:10.1093/biostatistics/kxr032>,