Perform mathematical operations on R formula (add, subtract, multiply, etc.) and substitute parts of formula.

This presents a comprehensive set of tools for the analysis and visualization of drug formulation data. It includes functions for statistical analysis, regression modeling, hypothesis testing, and comparative analysis to assess the impact of formulation parameters on drug release and other critical attributes. Additionally, the package offers a variety of data visualization functions, such as scatterplots, histograms, and boxplots, to facilitate the interpretation of formulation data. With its focus on usability and efficiency, this package aims to streamline the drug formulation process and aid researchers in making informed decisions during formulation design and optimization.

Simplifies the creation and customization of forest plots (alternatively called dot-and-whisker plots). Input classes accepted by 'forplo' are data.frame, matrix, lm, glm, and coxph. 'forplo' was written in base R and does not depend on other packages.

Forensic applications of pedigree analysis, including
    likelihood ratios for relationship testing, general relatedness
    inference, marker simulation, and power analysis. 'forrel' is part of
    the 'pedsuite', a collection of packages for pedigree analysis,
    further described in the book 'Pedigree Analysis in R' (Vigeland,
    2021, ISBN:9780128244302). Several functions deal specifically with
    power analysis in missing person cases, implementing methods described
    in Vigeland et al. (2020) <doi:10.1016/j.fsigen.2020.102376>. Data
    import from the 'Familias' software (Egeland et al. (2000)
    <doi:10.1016/S0379-0738(00)00147-X>) is supported through the
    'pedFamilias' package.

Identifies potential data outliers and their impact on estimates and 
         analyses. Uses the forward search approach of Atkinson and Riani, "Robust 
         Diagnostic Regression Analysis", 2000,<ISBN: o-387-95017-6> to prepare 
         descriptive statistics of a dataset that is to be analyzed by stats::lm(), 
         stats::glm(), stats::nls(), nlme::lme() or survival::coxph().  Includes 
         graphics functions to display the descriptive statistics.