https://docs.ropensci.org/pdftools (website)
https://github.com/ropensci/pdftools#readme (devel)
https://poppler.freedesktop.org (upstream)

https://docs.ropensci.org/pdftools/ (website)
https://github.com/ropensci/pdftools#readme (devel)
https://poppler.freedesktop.org (upstream)

Poppler C++ API: libpoppler-cpp-dev (deb) or
poppler-cpp-devel (rpm), and poppler-data (rpm/deb) package.

Phenotypic Index Measures for Oak Decline Severity
person("Jasen", "Finch", email = "jsf9@aber.ac.uk", role = c("aut", "cre"),  comment = c(ORCID = "0000-0002-6070-7476"))
Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest.
dplyr, magrittr, purrr, randomForest, readxl, stringr, tibble,
tidyr, tidyselect

Oak declines are complex disease syndromes and consist of many visual indicators that include aspects of tree size, crown condition and trunk condition. This can cause difficulty in the manual classification of symptomatic and non-symptomatic trees from what is in reality a broad spectrum of oak tree health condition. Two phenotypic oak decline indexes have been developed to quantitatively describe and differentiate oak decline syndromes in Quercus robur. This package provides a toolkit to generate these decline indexes from phenotypic descriptors using the machine learning algorithm random forest. The methodology for generating these indexes is outlined in Finch et al. (2121) <doi:10.1016/j.foreco.2021.118948>.

Computes the euclidean distance between rows of a matrix X
and rows of another matrix Y.  Previously, this could only be
done by binding the two matrices together and calling 'dist',
but this creates unnecessary computation by computing the
distances between a row of X and another row of X, and likewise
for Y.