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pervasive (version 1.0)

Pervasiveness Functions for Correlational Data

Description

Analysis of pervasiveness of effects in correlational data. The Observed Proportion (or Percentage) of Concordant Pairs (OPCP) is Kendall's Tau expressed on a 0 to 1 metric instead of the traditional -1 to 1 metric to facilitate interpretation. As its name implies, it represents the proportion of concordant pairs in a sample (with an adjustment for ties). Pairs are concordant when a participant who has a larger value on a variable than another participant also has a larger value on a second variable. The OPCP is therefore an easily interpretable indicator of monotonicity. The pervasive functions are essentially wrappers for the 'arules' package by Hahsler et al. (2025) and serve to count individuals who actually display the pattern(s) suggested by a regression. For more details, see the paper "Considering approaches to pervasiveness in the context of personality psychology" now accepted at the journal Personality Science.

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Version

Install

install.packages('pervasive')

Monthly Downloads

150

Version

1.0

License

MIT + file LICENSE

Maintainer

Denis Lajoie

Last Published

November 4th, 2025

Functions in pervasive (1.0)

OPCP

Calculate Observed Proportion of Concordant Pairs (OPCP)
pervasive_tric

Association Rule Mining With Trichotomized Data
pervasive_dic

Association Rule Mining With Dichotomized Data
pervasive_dic_glm

Association Rule Mining With Dichotomized Data
OPCP_mat

Calculate Observed Proportion of Concordant Pairs (OPCP)
OPCP_glm

Calculate Observed Proportion of Concordant Pairs (OPCP)
pervasive_tric_glm

Association Rule Mining With Trichotomized Data and a Binary outcome