knitr, rmarkdown, testthat, vdiffr (>= 0.3.0)

Analysing Chemodiversity of Phytochemical Data
c(
person("Hampus", "Petrén", email = "hampus.petren@gmail.com",
role = c("aut", "cre"), comment = c(ORCID = "0000-0001-6490-4517")),
person("Tobias G.", "Köllner", email = "koellner@ice.mpg.de",
role = c("aut"), comment = c(ORCID = "0000-0002-7037-904X")),
person("Robert R.", "Junker", email = "robert.junker@uni-marburg.de",
role = c("aut"), comment = c(ORCID = "0000-0002-7919-9678")))
Quantify and visualise various measures of chemical diversity and
dissimilarity, for phytochemical compounds and other sets of chemical
composition data. Importantly, these measures can incorporate biosynthetic
and/or structural properties of the chemical compounds, resulting in a more
comprehensive quantification of diversity and dissimilarity.
jsonlite, httr, vegan, webchem, fmcsR, ChemmineR, hillR, ape,
GUniFrac, tidygraph, igraph, ggraph, ggplot2, gridExtra,
ggdendro, tidyr, rlang, curl

Quantify and visualise various measures of chemical diversity and
dissimilarity, for phytochemical compounds and other sets of chemical
composition data. Importantly, these measures can incorporate biosynthetic
and/or structural properties of the chemical compounds, resulting in a more
comprehensive quantification of diversity and dissimilarity. For details,
see Petrén, Köllner and Junker (2022) <doi:10.1101/2022.06.08.495236>.

Multivariate Statistical Analysis in Chemometrics
This package is the R companion to the book "Introduction to Multivariate Statistical Analysis in Chemometrics" written by K. Varmuza and P. Filzmoser, CRC Press

class, e1071, lars, MASS, mclust, nnet, pcaPP, pls, rpart, robustbase, som

class, e1071, lars, MASS, mclust, nnet, pcaPP, pls, rpart,
robustbase, som
This package is the R companion to the book "Introduction
to Multivariate Statistical Analysis in Chemometrics" written
by K. Varmuza and P. Filzmoser (2009)

class, e1071, gclus, lars, MASS, mclust, nnet, pcaPP, pls,
rpart, robustbase, som