grDevices, graphics, grid, lattice, stats, utils

not possible, since we use data/*.R and our S4 classes

Douglas Bates [aut] (<https://orcid.org/0000-0001-8316-9503>),
  Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>),
  Mikael Jagan [aut] (<https://orcid.org/0000-0002-3542-2938>),
  Timothy A. Davis [ctb] (<https://orcid.org/0000-0001-7614-6899>,
    SuiteSparse libraries, notably CHOLMOD and AMD, collaborators
    listed in dir(pattern="^[A-Z]+[.]txt$", full.names=TRUE,
    system.file("doc", "SuiteSparse", package="Matrix"))),
  Jens Oehlschlägel [ctb] (initial nearPD()),
  Jason Riedy [ctb] (<https://orcid.org/0000-0002-4345-4200>, GNU
    Octave's condest() and onenormest(), Copyright: Regents of the
    University of California),
  R Core Team [ctb] (base R's matrix implementation)

A S3 Class for Vectors of 64bit Integers

Jens Oehlschlägel [aut, cre], Leonardo Silvestri [ctb]

Package 'bit64' provides serializable S3 atomic 64bit (signed) integers. 
 These are useful for handling database keys and exact counting in +-2^63.
 WARNING: do not use them as replacement for 32bit integers, integer64 are not
 supported for subscripting by R-core and they have different semantics when 
 combined with double, e.g. integer64 + double => integer64. 
 Class integer64 can be used in vectors, matrices, arrays and data.frames. 
 Methods are available for coercion from and to logicals, integers, doubles, 
 characters and factors as well as many elementwise and summary functions. 
 Many fast algorithmic operations such as 'match' and 'order' support inter-
 active data exploration and manipulation and optionally leverage caching.

Unified Parallel and Distributed Processing in R for Everyone

digest, globals (>= 0.16.1), listenv (>= 0.8.0), parallel,
parallelly (>= 1.34.0), utils

The purpose of this package is to provide a lightweight and
    unified Future API for sequential and parallel processing of R
    expression via futures.  The simplest way to evaluate an expression
    in parallel is to use `x %<-% { expression }` with `plan(multisession)`.
    This package implements sequential, multicore, multisession, and
    cluster futures.  With these, R expressions can be evaluated on the
    local machine, in parallel a set of local machines, or distributed
    on a mix of local and remote machines.
    Extensions to this package implement additional backends for
    processing futures via compute cluster schedulers, etc.
    Because of its unified API, there is no need to modify any code in order
    switch from sequential on the local machine to, say, distributed
    processing on a remote compute cluster.
    Another strength of this package is that global variables and functions
    are automatically identified and exported as needed, making it
    straightforward to tweak existing code to make use of futures.