Debugging pipe chains often consists of viewing the output after 
  each step. This package adds RStudio addins and two functions that allow 
  outputing each or select steps in a convenient way.

Provides a graphical user interface to integrate, visualize and explore results 
            from linkage and quantitative trait loci analysis, together with genomic information for autopolyploid 
            species. The app is meant for interactive use and allows users to optionally upload different sources 
            of information, including  gene annotation and alignment files, enabling the exploitation and search for 
            candidate genes in a genome browser. In its current version, 'VIEWpoly' supports inputs from 'MAPpoly', 
            'polymapR', 'diaQTL', 'QTLpoly', 'polyqtlR', 'GWASpoly', and 'HIDECAN' packages.

An advanced, interactive data table and data explorer for R,
    delivered as a modern, self-contained 'htmlwidget' with a high-performance
    virtualized grid. ViewR renders 'Kaggle'-style
    micro-dashboard column headers complete with data-type badges, mini
    distribution spark-histograms, and data-completeness (missingness) bars.
    It provides hover metadata cards, a sliding Data Insights drawer with
    interactive histograms and 'Pareto' category charts, a multi-condition
    visual query builder (AND/OR), a column visibility picker, and a
    reproducible code generator that emits 'dplyr', base R, and 'SQL' that
    matches the active filter and column state. The interface is implemented
    entirely in dependency-free vanilla 'JavaScript' (no 'React' or build
    toolchain) and works in the 'RStudio'/'Positron' Viewer, inside 'Shiny'
    apps, in 'R Markdown'/'Quarto', or as a portable standalone 'HTML' file.
    A single call to viewr() opens the explorer; the legacy 'Shiny'-gadget
    ViewR() editor remains available.

Provides tools for viewscape analysis from one or multiple viewpoints
    using a digital surface or elevation model. Core functionality includes computing
    viewsheds, quantifying visual magnitude, calculating a suite of viewscape
    configuration metrics (extent, depth, relief, Sky View Factor, skyline variation,
    and patch-based landscape structure), Shannon Diversity Index and land cover feature
    proportions within the visible area, pairwise intervisibility networks, panoramic
    view generation, and visualizing results as rasters or polygons. Viewscape
    configuration metrics follow the methods of Tabrizian et al. (2020)
    <doi:10.1016/j.landurbplan.2019.103704>. The viewshed algorithm is based on
    Franklin & Ray (1994) <https://api.semanticscholar.org/CorpusID:10680920>
    and Wang et al. (2000) <https://api.semanticscholar.org/CorpusID:131687018>.
    Visual magnitude is derived from Chamberlain & Meitner (2013)
    <doi:10.1016/j.landurbplan.2013.01.003>.
    Sky View Factor is computed following Oke (1981)
    <doi:10.1002/joc.3370010304> as implemented in the 'shadow' package
    (Dorman et al. 2019) <doi:10.32614/RJ-2019-024>.