A Tool for Standardizing Amphibian Taxonomy
Provides a workflow for combining amphibian taxonomic data sets from different sources that use different taxonomic nomenclature, by suggesting the most current nomenclature used by the Amphibian Species of the World: an Online Reference, Frost (2017) <http://research.amnh.org/herpetology/amphibia/index.html> for a given input query of species names. It can also be used to extract specific systematic information (e.g. species downstream of a given taxonomic level).

c(
person("Andy", "Lyons", , "andlyons@ucanr.edu", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-7505-6709")),
person("Regents of the University of California", role = "cph")
)
Compute degree days from daily min and max temperatures for
modeling plant and insect development.
cimir, dplyr, knitr, testthat (>= 3.0.0), tidyr, units
Andy Lyons [aut, cre] (<https://orcid.org/0000-0002-7505-6709>),
Regents of the University of California [cph]

Detection of differentially expressed genes.
Who to complain to <Klaus.Jung@ams.med.uni-goettingen.de>
Detection of differentially expressed genes between two
distinct groups of samples.

Models for Skewed Count Distributions Relevant to Networks
Likelihood-based inference for skewed count distributions used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis.  For a list of functions type: help(package='degreenet')

Likelihood-based inference for skewed count distributions
used in network modeling. "degreenet" is a part of the
"statnet" suite of packages for network analysis.  For a list
of functions type: help(package='degreenet')

Likelihood-based inference for skewed count distributions
used in network modeling. "degreenet" is a part of the
"statnet" suite of packages for network analysis.

Likelihood-based inference for skewed count distributions used in network modeling. "degreenet" is a part of the "statnet" suite of packages for network analysis.

Density Estimation from GROuped Summary Statistics
Estimation of a density from grouped (tabulated) summary statistics evaluated in each of the big bins (or classes) partitioning the support of the variable. These statistics include class frequencies and central moments of order one up to four. The log-density is modelled using a linear combination of penalised B-splines. The multinomial log-likelihood involving the frequencies adds up to a roughness penalty based on the differences in the coefficients of neighbouring B-splines and the log of a root-n approximation of the sampling density of the observed vector of central moments in each class. The so-obtained penalized log-likelihood is maximized using the EM algorithm to get an estimate of the spline parameters and, consequently, of the variable density and related quantities such as quantiles, see Lambert, P. (2021) <arXiv:2107.03883> for details.