c(person(given = "Matthew",
family = "Heun",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-7438-214X"),
email = "matthew.heun@me.com"))

dplyr, knitr, rmarkdown, spelling, stringr, testthat (>=
3.0.0), tibble

Tools for Manipulating and Summarizing CMIP5 Data
Working with CMIP5 data can be tricky, forcing scientists to write
custom scripts and programs. The `RCMIP5` package aims to ease this
process, providing a standard, robust, and high-performance set of scripts
to (i) explore what data have been downloaded, (ii) identify missing data,
(iii) average (or apply other mathematical operations) across experimental
ensembles, (iv) produce both temporal and spatial statistical summaries,
(v) regrid data, and (vi) produce easy-to-work-with graphical and data
summaries.
c( person("Ben", "Bond-Lamberty", role = c("aut")),
person("Kathe", "Todd-Brown", role = c("aut", "cre"), email =
"ktoddbrown@gmail.com") )
Ben Bond-Lamberty [aut],
Kathe Todd-Brown [aut, cre]

Working with CMIP5 data can be tricky, forcing scientists to write
custom scripts and programs. The `RCMIP5` package aims to ease this
process, providing a standard, robust, and high-performance set of scripts
to (i) explore what data have been downloaded, (ii) identify missing data,
(iii) average (or apply other mathematical operations) across experimental
ensembles, (iv) produce both temporal and spatial statistical summaries,
and (v) produce easy-to-work-with graphical and data summaries.

abind (>= 1.4), dplyr (>= 0.5), assertthat (>= 0.1), digest,
Matrix

Random Coefficient Minification Time Series Models
c(person("L.", "Han", role = c("aut", "cre"),
email = "lengyi.han@ubc.ca"))
Functions and data sets for simulating and fitting minification and random coefficient minification time series models. The nth term in the model X[n] is a nonnegative random variable defined as A[n]*min(X[n-1], Y[n]), where the A's and Y's are sequences of independent random variables.  Example data sets are provided where this kind of model can be justified on physical grounds and fits well.  Functions for simulating minification time series and for maximum  likelihood estimation are included. More information can be found in Han, Braun and Loeppky (2018) <doi:10.1007/s00362-018-1000-6>.