'fastText' Wrapper for Text Classification and Word
Representation
c(person("Michaël", "Benesty", role = c("aut", "cre"), email = "michael@benesty.fr"),
person("Facebook, Inc", role = c("cph"), email = "bojanowski@fb.com"))
'fastText' is an open-source, free, lightweight library that allows
users to learn text representations and text classifiers.
It transforms text into continuous vectors that can later
be used on many language related task.
It works on standard, generic hardware (no 'GPU' required).
It also includes model size reduction feature.
'fastText' original source code is available
at <https://github.com/facebookresearch/fastText>.
Michaël Benesty [aut, cre],
Facebook, Inc [cph]

c(person("Michaël", "Benesty", role = c("aut", "cre", "cph"), email = "michael@benesty.fr"),
person("Facebook, Inc", role = c("cph"), email = "bojanowski@fb.com"))
Learning text representations and text classifiers may rely
on the same simple and efficient approach. 'fastText' is an open-source, free,
lightweight library that allows users to perform both tasks.
It transforms text into continuous vectors that can later
be used on many language related task.
It works on standard, generic hardware (no 'GPU' required).
It also includes model size reduction feature.
'fastText' original source code is available
at <https://github.com/facebookresearch/fastText>.
Michaël Benesty [aut, cre, cph],
Facebook, Inc [cph]

methods, Rcpp (>= 0.12.12), RcppThread, assertthat

person("Brandon", "Greenwell", email = "greenwell.brandon@gmail.com", role =
c("aut", "cre"), comment = c(ORCID = "0000-0002-8120-0084"))
Computes fast approximate Shapley values for any supervised
learning model. Shapely values help to explain the predictions from any black box model using ideas
from game theory; see Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> for details.
ggplot2, gridExtra, plyr, Rcpp (>= 1.0.1), tibble
earth, mlbench, tinytest, covr, knitr, rmarkdown

Computes fast approximate Shapley values for any supervised
learning model. Shapley values help to explain the predictions from any black box model using ideas
from game theory; see Strumbel and Kononenko (2014) <doi:10.1007/s10115-013-0679-x> for details.