Receives two vectors, computes appropriate function for group comparison (i.e., t-test, Mann-Whitney; equality of variances), and reports the findings (mean/median, standard deviation, test statistic, p-value, effect size) in APA format (Fay, M.P., & Proschan, M.A. (2010)<DOI: 10.1214/09-SS051>).

Methods for dividing data into groups. 
    Create balanced partitions and cross-validation folds. 
    Perform time series windowing and general grouping and splitting of data. 
    Balance existing groups with up- and downsampling or collapse them to fewer groups.

To aggregate a hyper data frame, defined in the package 'spatstat.geom', according to a grouping structure.  To facilitate downstream analysis based on a "grouped" hyper data frame.  The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications.  Backward compatibility with user-code published in <doi:10.1093/bioinformatics/btaf430> is not maintained in versions >= 0.4.0 (June 2026) of this package. The authors of those publications are the appropriate contacts for reproducibility inquiries.

An intuitive interface to simulate superimposed (marked) point patterns with vectorized parameterization of random point pattern and distribution of marks.   The author has retired from academic research. Accordingly, this package should not be considered a validated tool for use in peer-reviewed publications or as the basis for grant applications.

These 'Rcpp'-based functions compute the efficient score statistics for grouped time-to-event data (Prentice and Gloeckler, 1978), with the optional inclusion of baseline covariates. Functions for estimating the parameter of interest and nuisance parameters, including baseline hazards, using maximum likelihood are also provided. A parallel set of functions allow for the incorporation of family structure of related individuals (e.g., trios). Note that the current implementation of the frailty model (Ripatti and Palmgren, 2000) is sensitive to departures from model assumptions, and should be considered experimental. For these data, the exact proportional-hazards-model-based likelihood is computed by evaluating multiple variable integration. The integration is accomplished using the 'Cuba' library (Hahn, 2005), and the source files are included in this package. The maximization process is carried out using Brent's algorithm, with the C++ code file from John Burkardt and John Denker (Brent, 2002).