Sets the alpha level for coefficients in a regression model
    as a decreasing function of the sample size through the use of
    Jeffreys' Approximate Bayes factor. You tell alphaN() your sample
    size, and it tells you to which value you must lower alpha to avoid
    Lindley's Paradox. For details, see Wulff and Taylor (2024)
    <doi:10.1177/14761270231214429>. Alpha can also be calibrated to the
    effect-size and moment Bayes factors of Klauer, Meyer-Grant, and
    Kellen (2025) <doi:10.3758/s13423-024-02612-2>, which center the
    alternative hypothesis on an effect size of your choosing.

Given the parameters of a distribution, the package uses the concept of alpha-outliers by Davies and Gather (1993) to flag outliers in a data set. See Davies, L.; Gather, U. (1993): The identification of multiple outliers, JASA, 88 423, 782-792, <doi:10.1080/01621459.1993.10476339> for details.

A software that implements a method for partitioning genetic trends to
    quantify the sources of genetic gain in breeding programmes.
    The partitioning method is described in Garcia-Cortes et al.
    (2008) <doi:10.1017/S175173110800205X>. The package includes the
    main function AlphaPart for partitioning breeding values and auxiliary
    functions for manipulating data and summarizing, visualizing, and saving
    results.

Implements the alpha-power hazard model and regression
    frameworks for survival data based on the flexible hazard rate function
    h(x; alpha, beta) = alpha^x + x^(beta-1) (Pal et al., 2026
    <doi:10.1007/s41096-026-00297-5>). Provides standard distribution
    functions (d, p, q, r, h, H, s) and distributional properties including
    raw/central moments, variance, skewness, kurtosis, quantile statistics
    (Bowley's skewness, Moors's kurtosis), Lambert W hazard rate function
    minimum (Corless et al., 1996), order statistics, and stochastic ordering
    (Shaked & Shanthikumar, 1994). Computes five classical estimation
    methods for baseline parameters: Maximum Likelihood Estimation (Casella &
    Berger, 2002), Least Squares Estimation (Swain et al., 1988), Weighted
    Least Squares Estimation (Styan, 1973), Maximum Product of Spacings
    Estimation (Cheng & Amin, 1983 <doi:10.1111/j.2517-6161.1983.tb01241.x>),
    and Cramer-von Mises Estimation (Macdonald, 1971). Supports four
    hazard regression models (M1-M4) within proportional hazards and
    parametric frameworks across uncensored data, right censoring, left
    censoring, interval censoring, and progressive Type-I and Type-II
    censoring schemes (Lee & Wang, 2003; Lawless, 2011; Balakrishnan &
    Aggarwala, 2000). Includes comprehensive model diagnostics, Cox-Snell,
    martingale, deviance, standardized, and studentized residuals, leverage,
    Cook's distance, DFFITS, DFBETAS, model comparisons (AIC, BIC, WAIC),
    k-fold cross-validation, prediction suites, random data generators, and
    an eight-panel diagnostic visualization suite.