Structural Handling of Finnish Personal Identity Codes
c(
person(given = "Pyry",
family = "Kantanen",
email = "pyry.kantanen@gmail.com",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0003-2853-2765")),
person(given = "Mans",
family = "Magnusson",
role = "aut"),
person(given = "Jussi",
family = "Paananen",
role = "aut"),
person(given = "Juho",
family = "Kopra",
role = "ctb"),
person(given = "Oskari",
family = "Luomala",
role = "ctb"),
person(given = "Tuomo",
family = "Nieminen",
role = "ctb"),
person(given = "Leo",
family = "Lahti",
role = "aut",
comment = c(ORCID = "0000-0001-5537-637X"))
)
Structural handling of Finnish identity codes (natural persons and
organizations); extract information, check ID validity and diagnostics.
Cairo, knitr, testthat, rmarkdown, covr, dplyr
Pyry Kantanen [aut, cre] (<https://orcid.org/0000-0003-2853-2765>),
Mans Magnusson [aut],
Jussi Paananen [aut],
Juho Kopra [ctb],
Oskari Luomala [ctb],
Tuomo Nieminen [ctb],
Leo Lahti [aut] (<https://orcid.org/0000-0001-5537-637X>)

Heuristics Including Take the Best and Unit-Weight Linear
c(
person("Jean", "Whitmore", email = "jeanimal@gmail.com",
role = c("aut", "cre")),
person("Daniel", "Barkoczi", email = "barkoczi@mpib-berlin.mpg.de",
role = "aut")
)
Implements various heuristics like Take The Best and
unit-weight linear, which do two-alternative choice: which of
two objects will have a higher criterion?  Also offers functions
to assess performance, e.g. percent correct across all row pairs
in a data set and finding row pairs where models disagree.
New models can be added by implementing a fit and predict function--
see vignette.
devtools, ggplot2, glmnet, knitr, plyr, reshape, reshape2,
testthat, rmarkdown
Jean Whitmore [aut, cre],
Daniel Barkoczi [aut]

c(
person("Jean", "Whitmore", email = "jeanimal@gmail.com",
role = c("aut", "cre")),
person("Daniel", "Barkoczi", email = "dnlbrkc@gmail.com",
role = "aut")
)
devtools, ggplot2, glmnet, knitr, plyr, reshape, reshape2,
rmarkdown, testthat

Implements various heuristics like Take The Best and
unit-weight linear, which do two-alternative choice: which of
two objects will have a higher criterion?  Also offers functions
to assess performance, e.g. percent correct across all row pairs
in a data set and finding row pairs where models disagree.
New models can be added by implementing a fit and predict function--
see vignette.  Take The Best was first described in: Gigerenzer, G.
& Goldstein, D. G. (1996) <doi:10.1037/0033-295X.103.4.650>.  All
of these heuristics were run on many data sets and analyzed in:
Gigerenzer, G., Todd, P. M., & the ABC Group (1999).
<ISBN:978-0195143812>.