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. Tool for evaluation of study credibility. 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 functions lm {stats}, glm {stats}, 
         nls {stats}, lme {nlme}, or coxph {survival}, or their equivalent in
         another language.  Includes graphics functions to display the 
         descriptive statistics.

The goal of 'forstringr' is to enable complex string
    manipulation in R especially to those more familiar with LEFT(),
    RIGHT(), and MID() functions in Microsoft Excel. The package combines
    the power of 'stringr' with other manipulation packages such as
    'dplyr' and 'tidyr'.

Process automation of point cloud data derived from terrestrial-based technologies such as Terrestrial Laser Scanner (TLS) or Mobile Laser Scanner. 'FORTLS' enables (i) detection of trees and estimation of tree-level attributes (e.g. diameters and heights), (ii) estimation of stand-level variables (e.g. density, basal area, mean and dominant height), (iii) computation of metrics related to important forest attributes estimated in Forest Inventories at stand-level, and (iv) optimization of plot design for combining TLS data and field measured data. Documentation about 'FORTLS' is described in Molina-Valero et al. (2022, <doi:10.1016/j.envsoft.2022.105337>).