Description: Cache the results of a function so that when you call it
again with the same arguments it returns the pre-computed

again with the same arguments it returns the pre-computed value.

person("Kirill", "Müller", , "krlmlr+r@mailbox.org", role = "aut"))

person("Kirill", "Müller", , "krlmlr+r@mailbox.org", role = "aut"),
person("Daniel", "Cook", , "danielecook@gmail.com", role = "aut"))

Description: Cache the results of a function so that when you
call it again with the same arguments it returns the pre-computed
Suggests: digest, aws.s3, covr, googleAuthR, googleCloudStorageR, httr,

call it again with the same arguments it returns the previously computed

Title: A 'rmarkdown' Template that Can be Highly Customized
person("Hao", "Zhu", email = "haozhu233@gmail.com", role = c("aut", "cre"),
person("Timothy", "Tsai", email = "TimothyTsai@hsl.harvard.edu", role = "aut"),
person("Thomas", "Travison", email = "tgt@hsl.harvard.edu", role = "aut"),
person("Mikhail", "Popov", role = "ctb")
Description: A 'rmarkdown' template that supports company logo, contact info,
watermarks and more. Currently restricted to 'Latex'/'Markdown'; a similar
'HTML' theme will be added in the future.

person("Timothy", "Tsai", email = "TimothyTsai@hsl.harvard.edu", role = "aut",

Title: Quantifying Ecological Memory in Palaeoecological Datasets and
Description: Tools to quantify ecological memory in long time-series with Random Forest models (Breiman 2001 <doi:10.1023/A:1010933404324>) fitted with the 'ranger' library (Wright and Ziegler 2017 <doi:10.18637/jss.v077.i01>). Particularly oriented to palaeoecological datasets and simulated pollen curves produced by the 'virtualPollen' package, but also applicable to other long time-series involving a set of environmental drivers and a biotic response.
Imports: ggplot2, ranger, cowplot, viridis, viridisLite, zoo, stringr,
Suggests: devtools, formatR, kableExtra, magrittr, knitr, rmarkdown,

Description: How much ram do you need to store a 100,000 by 100,000 matrix?
Learn the scintillating answer to this and many more such questions with
the memuse package.  This package gives the user the size (memory usage)
of an unallocated, in-core, dense, numeric matrix.  The output is an S4
class object that can be manipulated to be presented in different ways
(different units, short/long versions of those units, etc.).  The package
offers similar functionality to that of R's object.size() function, with
the key difference being that memuse does not require an object to already
be allocated.  So one can determine the size of enormous matrices even