c(person(given = "Michael", family = "Friendly", role=c("aut", "cre"), email="friendly@yorku.ca"),
person(given = "John", family = "Fox", role="aut"))
Michael Friendly [aut, cre], John Fox [aut]
Functions for computing and visualizing
generalized canonical discriminant analyses and canonical correlation analysis
for a multivariate linear model.
Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA'
design and is equivalent to canonical correlation analysis between a set of quantitative
response variables and a set of dummy variables coded from the factor variable.
The 'candisc' package generalizes this to higher-way 'MANOVA' designs
for all factors in a multivariate linear model,
computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D)
visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are
now provided for canonical correlation analysis when all predictors are quantitative.

rgl, corrplot, knitr, rmarkdown, MASS, rpart, rpart.plot

R (>= 3.5.0), car, heplots (>= 0.8-6), graphics, stats
Michael Friendly [aut, cre],
John Fox [aut]

Chemical Composition of Differential Protein Expression
Datasets are collected here for differentially (up- and down-)
expressed proteins identified in proteomic studies of cancer and in cell
culture experiments. Tables of amino acid compositions of proteins are
used for calculations of chemical composition, projected into selected
basis species. Plotting functions are used to visualize the compositional
differences and thermodynamic potentials for proteomic transformations.

Compositional Analysis of Differentially Expressed Proteins in
Cancer
c(
person("Jeffrey", "Dick", email = "j3ffdick@gmail.com", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-0687-5890")),
person("Ben", "Bolker", role = "ctb",
comment = c(ORCID = "0000-0002-2127-0443"))
)
CHNOSZ (>= 1.3.0), xtable, MASS, knitr, rmarkdown
Compositional analysis of differentially expressed proteins in
cancer and cell culture proteomics experiments. The data include lists of up-
and down-regulated proteins in different cancer types (breast, colorectal,
liver, lung, pancreatic, prostate) and laboratory conditions (hypoxia,
hyperosmotic stress, high glucose, 3D cell culture, and proteins secreted in
hypoxia), together with amino acid compositions computed for protein sequences
obtained from UniProt. Functions are provided to calculate compositional metrics
including protein length, carbon oxidation state, and stoichiometric hydration
state. In addition, phylostrata (evolutionary ages) of protein-coding genes are
compiled using data from Liebeskind et al. (2016) <doi:10.1093/gbe/evw113> or
Trigos et al. (2017) <doi:10.1073/pnas.1617743114>. The vignettes contain
plots of compositional differences, phylostrata for human proteins, and
references for all datasets.