Provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. In addition to its primary test, it also provides functions for simulation-based power analysis, reading in common data set formats, and visualizing results. Temporarily contains an edited version of function hr.mcp() from package 'wild1', written by Glen Sargeant. For tutorial see: http://evolve.zoo.ox.ac.uk/Evolve/Perspectev.html.

Provides a standardized framework to support the selection
    and evaluation of parametric survival models for time-to-event data.
    Includes tools for visualizing survival data, checking proportional
    hazards assumptions (Grambsch and Therneau, 1994,
    <doi:10.1093/biomet/81.3.515>), comparing parametric (Ishak and
    colleagues, 2013, <doi:10.1007/s40273-013-0064-3>), spline (Royston
    and Parmar, 2002, <doi:10.1002/sim.1203>) and cure models, examining
    hazard functions, and evaluating model extrapolation. Methods are
    consistent with recommendations in the NICE Decision Support Unit
    Technical Support Documents (14 and 21
    <https://sheffield.ac.uk/nice-dsu/tsds/survival-analysis>). Results
    are structured to facilitate integration into decision-analytic
    models, and reports can be generated with 'rmarkdown'. The package
    builds on existing tools including 'flexsurv' (Jackson, 2016,
    <doi:10.18637/jss.v070.i08>)) and 'flexsurvcure' for estimating cure
    models.

Compute personal values scores from various questionnaires based on the theoretical constructs proposed by professor Shalom H. Schwartz. Designed for researchers and practitioners in psychology, sociology, and related fields, the package facilitates the quantification and visualization of different dimensions related to personal values from survey data. It incorporates the recommended statistical adjustment to enhance the accuracy and interpretation of the results.

The perturbR() function incrementally perturbs network edges (using the   rewireR function)and compares the resulting community detection solutions 
    from the rewired networks with the solution found for the original network. 
    These comparisons aid in understanding the stability of the 
    original solution. The package requires symmetric, weighted (specifically, count) matrices/networks.