c(
person(given = "Jonathan",
family = "Bratt",
role = c("aut", "cre"),
email = "jonathan.bratt@macmillan.com",
comment = c(ORCID = "0000-0003-2859-0076")),
person(given = "Jon",
family = "Harmon",
role = c("aut"),
email = "jonthegeek@gmail.com",
comment = c(ORCID = "0000-0003-4781-4346")),
person(given = "Bedford Freeman & Worth Pub Grp LLC DBA Macmillan Learning",
role = c("cph"))
)
Tokenize text into morphemes. The morphemepiece algorithm uses a
lookup table to determine the morpheme breakdown of words, and falls back on a
modified wordpiece tokenization algorithm for words not found in the lookup
table.
dlr (>= 1.0.0), magrittr, morphemepiece.data, piecemaker (>=
1.0.0), purrr (>= 0.3.4), rlang, stringr (>= 1.4.0)
dplyr, fs, ggplot2, here, knitr, remotes, rmarkdown, testthat
(>= 3.0.0), utils
Jonathan Bratt [aut, cre] (<https://orcid.org/0000-0003-2859-0076>),
Jon Harmon [aut] (<https://orcid.org/0000-0003-4781-4346>),
Bedford Freeman & Worth Pub Grp LLC DBA Macmillan Learning [cph]

dlr (>= 1.0.0), magrittr, morphemepiece.data, piecemaker (>=
1.0.0), purrr (>= 0.3.4), readr, rlang, stringr (>= 1.4.0)

dlr (>= 1.0.0), fastmatch, magrittr, memoise (>= 2.0.0),
morphemepiece.data, piecemaker (>= 1.0.0), purrr (>= 0.3.4),
readr, rlang, stringr (>= 1.4.0)

c(
person(given = "Jonathan",
family = "Bratt",
role = c("aut"),
email = "jonathan.bratt@macmillan.com",
comment = c(ORCID = "0000-0003-2859-0076")),
person(given = "Jon",
family = "Harmon",
role = c("aut", "cre"),
email = "jonthegeek@gmail.com",
comment = c(ORCID = "0000-0003-4781-4346")),
person(given = "Bedford Freeman & Worth Pub Grp LLC DBA Macmillan Learning",
role = c("cph"))
)
Provides data about morphemes, the smallest units of meaning in a
language.
Jonathan Bratt [aut] (<https://orcid.org/0000-0003-2859-0076>),
Jon Harmon [aut, cre] (<https://orcid.org/0000-0003-4781-4346>),
Bedford Freeman & Worth Pub Grp LLC DBA Macmillan Learning [cph]

Estimate Parameters of Mixtures of Logistic Regressions
Mixture of logistic regressions parameters (H)estimation with
(U)spectral methods. The main methods take d-dimensional inputs and a vector
of binary outputs, and return parameters according to the GLMs mixture model
(General Linear Model). For more details see chapter 3 in the PhD thesis of
Mor-Absa Loum: <http://www.theses.fr/s156435>, available here
<https://www.math.u-psud.fr/~loum/IMG/pdf/these.compressed-2.pdf>.
Benjamin Auder <Benjamin.Auder@u-psud.fr> [aut,cre],
Mor-Absa Loum <Mor-Absa.Loum@u-psud.fr> [aut]
devtools, flexmix, parallel, testthat, roxygen2, tensor,
nloptr
'utils.R' 'A_NAMESPACE.R' 'computeMu.R' 'multiRun.R'
'optimParams.R' 'plot.R' 'sampleIO.R'