There are two main functions: (1) To estimate the power of testing for linkage using an
        affected sib pair design, as a function of the recurrence risk
        ratios. We will use analytical power formulae as implemented in
        R. These are based on a Mathematica notebook created by Martin
        Farrall. (2) To examine how the power of the transmission
        disequilibrium test (TDT) depends on the disease allele
        frequency, the marker allele frequency, the strength of the
        linkage disequilibrium, and the magnitude of the genetic
        effect. We will use an R program that implements the power
        formulae of Abel and Muller-Myhsok (1998). These formulae allow
        one to quickly compute power of the TDT approach under a
        variety of different conditions. This R program was modeled on
        Martin Farrall's Mathematica notebook.

It estimates power and sample size for Partial Least Squares-based methods described in Andreella, et al., (2024), <doi:10.48550/arXiv.2403.10289>.

Implements conjugate power priors for efficient Bayesian analysis 
    of normal data. Power priors allow principled incorporation of historical 
    information while controlling the degree of borrowing through a discounting 
    parameter (Ibrahim and Chen (2000) <doi:10.1214/ss/1009212519>). This 
    package provides closed-form conjugate representations for both univariate 
    and multivariate normal data using Normal-Inverse-Chi-squared and 
    Normal-Inverse-Wishart distributions, eliminating the need for MCMC sampling. 
    The conjugate framework builds upon standard Bayesian methods described in 
    Gelman et al. (2013, ISBN:978-1439840955).

A set of functions designed to calculate the standardised
    precipitation and standardised precipitation evapotranspiration indices
    using NASA POWER data as described in Blain et al. (2023)
    <doi:10.2139/ssrn.4442843>.  These indices are calculated using a reference
    data source.  The functions verify if the indices' estimates meet the
    assumption of normality and how well NASA POWER estimates represent
    real-world data.  Indices are calculated in a routine mode.  Potential
    evapotranspiration amounts and the difference between rainfall and potential
    evapotranspiration are also calculated.  The functions adopt a basic time
    scale that splits each month into four periods.  Days 1 to 7, days 8 to 14,
    days 15 to 21, and days 22 to 28, 29, 30, or 31, where 'TS=4' corresponds to
    a 1-month length moving window (calculated 4 times per month) and 'TS=48'
    corresponds to a 12-month length moving window (calculated 4 times per
    month).