Estimates tree crown scorch from terrestrial lidar scans collected with a RIEGL vz400i. The methods follow those described in Cannon et al. (2025, Fire Ecology 21:71, <doi:10.1186/s42408-025-00420-0>).

Provides a function that performs the adaptive mean shift
    algorithm for individual tree crown delineation in 3D point clouds as
    proposed by 
    Ferraz et al. (2016) <doi:10.1016/j.rse.2016.05.028>, as well as supporting 
    functions.

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>.

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>.

Performs forward and backward stepwise regression for the 
    proportional subdistribution hazards model in competing risks 
    (Fine & Gray 1999). Procedure uses AIC, BIC and BICcr as selection 
    criteria. BICcr has a penalty of k = log(n*), where n* is the number 
    of primary events. This version includes improved handling of factors, 
    interactions, and polynomial terms.