c(person("Olaf", "Mersmann", role=c("aut")),
person("Claudia", "Beleites", role=c("ctb")),
person("Rainer", "Hurling", role=c("ctb")),
person("Ari", "Friedman", role=c("ctb")),
person(given=c("Joshua","M."), family="Ulrich", role="cre",
email="josh.m.ulrich@gmail.com"))
Olaf Mersmann [aut],
Claudia Beleites [ctb],
Rainer Hurling [ctb],
Ari Friedman [ctb],
Joshua M. Ulrich [cre]

On a Unix-alike, one of the C functions
mach_absolute_time (macOS), clock_gettime or gethrtime. If none
of these is found, the obsolescent POSIX function gettimeofday
will be tried.

Do 16s Data Analysis and Generate Figures
c(person("Kai","Guo",email = "guokai8@gmail.com",
role = c("aut", "cre")
),
person("Pan","Gao",role = "aut"))
Provides functions to enhance the available
statistical analysis procedures in R by providing simple functions to
analysis and visualize the 16S rRNA data.Here we present a tutorial
with minimum working examples to demonstrate usage and dependencies.
dplyr, plyr, magrittr, broom, phyloseq, vegan, rlang, tools,
ggplot2, ggpubr, DESeq2, dada2, SummarizedExperiment,
S4Vectors, Biostrings, rstatix, tidyr, DECIPHER, phangorn,
MASS, randomForest, edgeR, knitr

dplyr, plyr, magrittr, broom, phyloseq, vegan, rlang, ggplot2,
ggpubr, DESeq2, dada2, SummarizedExperiment, S4Vectors,
Biostrings, rstatix, tidyr, DECIPHER, phangorn, MASS,
randomForest, edgeR, testthat

dplyr, plyr, magrittr, broom, phyloseq, vegan, rlang, ggplot2,
ggpubr, DESeq2, SummarizedExperiment, S4Vectors, rstatix,
tidyr, phangorn, randomForest, edgeR, testthat

Methods for Taxonomic Classification of Prokaryotes
Kristian Hovde Liland, Hilde Vinje, Lars Snipen
Functions for assigning 16S sequence data to a
taxonomic level in the tree-of-life for prokaryotes.
Rcpp (>= 0.11.1), RcppEigen, RcppParallel

Rcpp (>= 0.11.1), RcppArmadillo, RcppEigen, RcppParallel,
progress

R (>= 3.5.0), microseq, microcontax, dplyr, stringr, rlang

Entity Resolution with Random Partition Priors for
Microclustering
c(
person(given=c("Rebecca", "C"), family="Steorts", role = c("aut","cre"), email="beka@stat.duke.edu"),
person("Brenda", "Betancourt", email = "bbetancourt@ufl.edu", role = c("aut")),
person("Giacomo", "Zanella", email = "zanella.gcm@gmail.com", role = c("aut"))
)
An implementation of the model in Betancourt, Zanella, Steorts (2020) <arXiv:2004.02008>, which performs microclustering models for categorical data. The package provides a vignette for two proposed methods in the paper as well as two standard Bayesian non-parametric clustering approaches for entity resolution. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.
Rebecca C Steorts [aut, cre],
Brenda Betancourt [aut],
Giacomo Zanella [aut]