Package: InteractionPoweR
Title: Power Analyses for Interaction Effects in Cross-Sectional
        Regressions
Version: 0.2.4
Authors@R: 
    c(person(given = "David",
           family = "Baranger",
           role = c("aut", "cre"),
           email = "dbaranger@gmail.com" ),
     person(given = "Andrew",
           family = "Castillo",
           role = c("aut"),
           email = "casti179@purdue.edu"),
    person(given = "Brandon",
           family = "Goldstein",
           role = "ctb",
           email = "brandonlgoldstein@gmail.com")   , 
    person(given = "Megan",
           family = "Finsaas",
           role = "ctb",
           email = "megan.finsaas@gmail.com")  ,
    person(given = "Thomas",
           family = "Olino",
           role = "ctb",
           email = "thomas.olino@gmail.com")  ,
    person(given = "Colin",
           family = "Vize",
           role = "ctb",
           email = "vizece@upmc.edu")  ,
    person(given = "Don",
           family = "Lynam",
           role = "ctb",
           email = "dlynam@purdue.edu")  
           )
Description: Power analysis for regression models which test the interaction of
    two or three independent variables on a single dependent variable. Includes options 
    for correlated interacting variables and specifying variable reliability. 
    Two-way interactions can include continuous, binary, or ordinal variables.
    Power analyses can be done either analytically or via simulation.  Includes 
    tools for simulating single data sets and visualizing power analysis results.
    The primary functions are power_interaction_r2() and power_interaction() for two-way
    interactions, and power_interaction_3way_r2() for three-way interactions. The function 
    run_pos_power_search() provides a stability analysis for two-way interactions.
    Please cite as: Baranger DAA, Finsaas MC, Goldstein BL, Vize CE, Lynam DR,
    Olino TM (2023). "Tutorial: Power analyses for interaction effects in 
    cross-sectional regressions." <doi:10.1177/25152459231187531>. 
    If you use the stability analyses, please cite: Castillo A, Miller JD, Vize C, 
    Baranger DAA, Lynam DR. "When Do Interaction/Moderation Effects Stabilize in
    Linear Regression?"<doi:10.1177/25152459251407860>.
Maintainer: David Baranger <dbaranger@gmail.com>
URL: https://dbaranger.github.io/InteractionPoweR/,
        https://doi.org/10.1177/25152459231187531,
        https://doi.org/10.1177/25152459251407860
BugReports: https://github.com/dbaranger/InteractionPoweR/issues
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.1
Depends: R (>= 3.5.0)
Imports: dplyr, parallel, doParallel, foreach, ggplot2, polynom,
        chngpt, rlang, tidyr, stats, ggbeeswarm, Matrix
NeedsCompilation: no
Packaged: 2026-03-23 19:22:14 UTC; dbara
Author: David Baranger [aut, cre],
  Andrew Castillo [aut],
  Brandon Goldstein [ctb],
  Megan Finsaas [ctb],
  Thomas Olino [ctb],
  Colin Vize [ctb],
  Don Lynam [ctb]
Repository: CRAN
Date/Publication: 2026-03-24 06:10:45 UTC
Built: R 4.6.0; ; 2026-04-26 19:10:34 UTC; unix
