Auto, Cross and Multi-dimensional recurrence quantification analysis.
Different methods for computing recurrence, cross vs. multidimensional
or profile it -only looking at the diagonal recurrent points-
as well as functions for optimization and plotting are proposed.
in-depth measures of the whole cross-recurrence plot,
Please refer to Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510>
and Wallot (2018) <doi:10.1080/00273171.2018.1512846>
for further details about the method.

Matrix, pracma, tseriesChaos, gplots, plot3D, rdist

Cross-Recurrence Quantification Analysis for Dynamic Natural
Language Processing
Cross-recurrence quantification analysis
for word series, from text, known as categorical
recurrence analysis. Uses the 'crqa' R package
by Coco and Dale (2014) <doi:10.3389/fpsyg.2014.00510>.
Functions are wrappers to facilitate exploration
of the sequential properties of text.
R (>= 3.0.0), crqa, tm, corpus, gutenbergr, RCurl

Competing risks regression for Stratified and Clustered data
Extension of cmprsk to Stratified and Clustered data

Competing Risks Regression for Stratified and Clustered Data
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>,
Zhou et al. (2013) <doi: 10.1002/sim.5815>.

Extension of cmprsk to Stratified and Clustered data.
Goodness of fit test for Fine-Gray model.

Bias-Corrected Variance for Competing Risks Regression with
Clustered Data
c(person("Xinyuan", "Chen", role = c("aut","cre","cph"),
email = "xchen@math.msstate.edu"),
person("Fan", "Li", role = "aut"))
A user friendly function 'crrcbcv' to compute bias-corrected variances for competing risks regression models using proportional subdistribution hazards with small-sample clustered data. Four types of bias correction are included: the MD-type bias correction by Mancl and DeRouen (2001) <doi:10.1111/j.0006-341X.2001.00126.x>, the KC-type bias correction by Kauermann and Carroll (2001) <doi:10.1198/016214501753382309>, the FG-type bias correction by Fay and Graubard (2001) <doi:10.1111/j.0006-341X.2001.01198.x>, and the MBN-type bias correction by Morel, Bokossa, and Neerchal (2003) <doi:10.1002/bimj.200390021>.
R (>= 3.5.0), crrSC, abind, pracma, survival, stats
Xinyuan Chen [aut, cre, cph],
Fan Li [aut]