Sparse and Dense Matrix Classes and Methods

c(person("Douglas","Bates", role="aut")
 , person("Martin","Maechler", role = c("aut","cre"), email="mmaechler+Matrix@gmail.com",
          comment = c(ORCID = "0000-0002-8685-9910"))
 , person("Timothy A.", "Davis", role="ctb",
           comment = c("SuiteSparse and 'cs' C libraries, notably CHOLMOD, AMD;
		       collaborators listed in
			dir(pattern = '^[A-Z]+[.]txt$', full.names=TRUE,
			    system.file('doc', 'SuiteSparse', package='Matrix'))"))
 , person("Jens", "Oehlschlägel", role="ctb", comment="initial nearPD()")
 , person("Jason", "Riedy", role="ctb",
          comment = c("condest() and onenormest() for octave",
 	  	      "Copyright: Regents of the University of California"))
 , person("R Core Team", role = "ctb", comment="base R matrix implementation")
 )

A rich hierarchy of matrix classes, including triangular,
   symmetric, and diagonal matrices, both dense and sparse and with
   pattern, logical and numeric entries.   Numerous methods for and
   operations on these matrices, using 'LAPACK' and 'SuiteSparse' libraries.

methods, graphics, grid, stats, utils, lattice

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

Douglas Bates [aut],
  Martin Maechler [aut, cre] (<https://orcid.org/0000-0002-8685-9910>),
  Timothy A. Davis [ctb] (SuiteSparse and 'cs' C libraries, notably
    CHOLMOD, 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] (condest() and onenormest() for octave, Copyright:
    Regents of the University of California),
  R Core Team [ctb] (base R matrix implementation)

Fast Implementation of a Key-Value Store

c(
    person("Winston", "Chang", email = "winston@rstudio.com", role = c("aut", "cre")),
    person(given = "RStudio", role = c("cph", "fnd")),
    person(given = "Tessil", role = "cph", comment = "hopscotch_map library")
    )

Fast implementation of a key-value store. Environments are commonly
    used as key-value stores, but every time a new key is used, it is added to
    R's global symbol table, causing a small amount of memory leakage. This can
    be problematic in cases where many different keys are used. Fastmap avoids
    this memory leak issue by implementing the map using data structures in C++.