Tools to analyze point patterns in space occurring over planar network structures derived from graph-related intensity measures for undirected, directed, and mixed networks. 
    This package is based on the following research: Eckardt and Mateu (2018) <doi:10.1080/10618600.2017.1391695>. Eckardt and Mateu (2021) <doi:10.1007/s11749-020-00720-4>.

An R implementation of Matthew Thomas's 'Python' library 'inteq'. First, this solves Fredholm integral equations of the first kind ($f(s) = \int_a^b K(s, y) g(y) dy$) using methods described by Twomey (1963) <doi:10.1145/321150.321157>. Second, this solves Volterra integral equations of the first kind ($f(s) = \int_0^s K(s,y) g(t) dt$) using methods from Betto and Thomas (2021) <doi:10.48550/arXiv.2106.08496>. Third, this solves Voltera integral equations of the second kind ($g(s) = f(s) + \int_a^s K(s,y) g(y) dy$) using methods from Linz (1969) <doi:10.1137/0706034>.

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. 
    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>.

Produces a publication-ready table that includes all effect estimates necessary for full reporting effect modification and interaction analysis as recommended by Knol and Vanderweele (2012) [<doi:10.1093/ije/dyr218>]. It also estimates confidence interval for the trio of additive interaction measures using the delta method (see Hosmer and Lemeshow (1992), [<doi:10.1097/00001648-199209000-00012>]), variance recovery method (see Zou (2008), [<doi:10.1093/aje/kwn104>]), or percentile bootstrapping (see Assmann et al. (1996), [<doi:10.1097/00001648-199605000-00012>]).