Generate crosswords from a list of words.

Fits a constrained regression model for an ordinal response with ordinal predictors and possibly others, Espinosa and Hennig (2019) <DOI:10.1007/s11222-018-9842-2>. The parameter estimates associated with an ordinal predictor are constrained to be monotonic. If a monotonicity direction (isotonic or antitonic) is not specified for an ordinal predictor by the user, then one of the available methods will either establish it or drop the monotonicity assumption. Two monotonicity tests are also available to test the null hypothesis of monotonicity over a set of parameters associated with an ordinal predictor.

In order to facilitate analysis of datasets hosted on the Crunch
    data platform <https://crunch.io/>, the 'crplyr' package implements 'dplyr'
    methods on top of the Crunch backend. The usual methods 'select', 'filter',
    'group_by', 'summarize', and 'collect' are implemented in such a way as to
    perform as much computation on the server and pull as little data locally
    as possible.

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.
    in-depth measures of the whole cross-recurrence plot,
    Please refer to Coco and others (2021) <doi:10.32614/RJ-2021-062>,
    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.

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>.