S original by Nicky Best, Kate Cowles and Karen Vines
R port by Martyn Plummer <plummer@iarc.fr>
Output analysis and diagnostics for Markov Chain
Monte Carlo simulations.

Martyn Plummer <plummer@iarc.fr>, Nicky Best, Kate Cowles, Karen Vines
Output analysis and diagnostics for Markov Chain Monte Carlo simulations.

A Basic Set of Functions for Compositional Data Analysis
c(person("Marc", "Comas-Cufí", role = c("aut", "cre"),
email = "mcomas@imae.udg.edu"))
A minimum set of functions to perform compositional data analysis
using the log-ratio approach introduced by John Aitchison (1982) <http://www.jstor.org/stable/2345821>. Main functions
have been implemented in c++ for better performance.

A minimum set of functions to perform compositional data analysis
using the log-ratio approach introduced by John Aitchison in 1982. Main functions
have been implemented in c++ for better performance.

A minimum set of functions to perform compositional data analysis
using the log-ratio approach introduced by John Aitchison (1982) <http:www.jstor.org/stable/2345821>. Main functions
have been implemented in c++ for better performance.

c(person("Marc", "Comas-Cufí", role = c("aut", "cre"),
email = "mcomas@imae.udg.edu", comment = c(ORCID = "0000-0001-9759-0622")))
Marc Comas-Cufí [aut, cre] (<https://orcid.org/0000-0001-9759-0622>)

knitr, rmarkdown, testthat (>= 2.1.0), ggplot2

Rcpp (>= 0.12.12), stats, Matrix, jsonlite

Compositional Data Analysis for Microbiome Studies
c(
person(
"Malu", "Calle",
email = "malu.calle@uvic.cat",
role = c("aut"),
comment = c(ORCID = "0000-0001-9334-415X")
),
person(
"Toni", "Susin",
email = "toni.susin@upc.edu",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-0874-2784")
)
)
Functions for microbiome data analysis that take into account its compositional nature. Performs variable selection through penalized regression for both, cross-sectional and longitudinal studies, and for binary and continuous outcomes.
corrplot, glmnet, plyr, pROC, ggpubr, ggplot2

corrplot, glmnet, plyr, pROC, ggpubr, ggplot2, qpdf