pavo

An R package for the organization, visualisation, and analysis of spectral and spatial colour data.
Rafael Maia, Hugo Gruson, Thomas White

pavo offers a flexible and integrated workflow for working with spectral and spatial colour data. It includes functions that take advantage of new data classes to work seamlessly from importing raw spectra and images, to publication-quality visualisations, and analyses via a suite of analytical methods and visual models.

If you need help with the package, take a look at the documentation and extended vignettes, and keep an eye on the latest news for changes. We’re always happy to receive feedback and suggestions via personal email or the mailing list: r-pavo@googlegroups.com. If you have a bug to report, we’d appreciate it if you could also include a reproducible example when possible.

Current release: 2.2 (01/07/2019)

Development version: 2.3

Installation: The current release of pavo is available on CRAN and can be installed using install.packages('pavo') within R. The bleeding-edge version is on github, and is most easily installed with the remotes package, by running remotes::install_github('rmaia/pavo').

Citation: Maia R, Gruson H, Endler JA, White TE (2019) pavo 2: new tools for the spectral and spatial analysis of colour in R. Methods in Ecology and Evolution 10, 1097-1107.


lightr

Import spectral data and metadata in R
Hugo Gruson, Thomas White, Rafael Maia

lightr offers a unified, user-friendly interface for reading UV-VIS reflectance, transmittance, and/or absorbance spectral files and associated metadata from a suite of proprietary (and generally unfriendly) file formats, across all systems.

Current release: 0.1 (20/11/2019)

Development version: 0.2

Installation The current release of lightr is available on CRAN, and can be installed using install.packages('lightr') within R. The development version can be installed via GitHub using remotes::install_github("ropensci/lightr").

Citation: Gruson H, White TE, Maia R (2019) lightr: import spectral data and metadata in R. Journal of Open Source Software.


metadat

Meta-analytic datasets for R
Thomas White, Daniel Noble, Alistair Senior, W. Kyle Hamilton, Wolfgang Viechtbauer

metadat contains a large collection of meta-analytic datasets, useful for teaching purposes, illustrating/testing meta-analytic methods, and validating published analyses.

Development version: 0.1

Installation This package is not yet published on CRAN, but can be installed via GitHub using remotes::install_github("wviechtb/metadat").