c(person("Tyler", "Rinker", email = "tyler.rinker@gmail.com", role = c("aut", "cre")))
Harness the power of 'quanteda', 'data.table' & 'stringi' to quickly generate 'tm' DocumentTermMatrix
and TermDocumentMatrix data structures.

Goodness-of-Fit Measures for Categorical Response Models
person(given = "Ejike R.",
family = "Ugba",
role = c("aut", "cre", "cph"),
email = "ejike.ugba@outlook.com",
comment = c(ORCID = "0000-0003-2572-0023"))
A post-estimation method for categorical response models (CRM).
Inputs from objects of class serp(), clm(), polr(), multinom(), mlogit(),
vglm() and glm() are currently supported. Available tests include the
Hosmer-Lemeshow tests for the binary, multinomial and ordinal logistic
regression; the Lipsitz and the Pulkstenis-Robinson tests for the ordinal
models. The proportional odds, adjacent-category, and constrained continuation-ratio
models are particularly supported at ordinal level. Tests for the proportional
odds assumptions in ordinal models are also possible with the Brant and the
Likelihood-Ratio tests. Moreover, several summary measures of predictive strength
(Pseudo R-squared), and some useful error metrics, including, the brier
score, misclassification rate and logloss are also available for the
binary, multinomial and ordinal models. Ugba, E. R. and Gertheiss, J. (2018)
<http://www.statmod.org/workshops_archive_proceedings_2018.html>.
methods, utils, crayon, stats, Matrix, epiR, reshape, stringr,
VGAM (>= 1.1-4)
serp, dfidx, mlogit, nnet, ordinal, MASS, testthat, covr

utils, crayon, stats, Matrix, epiR, reshape, stringr, VGAM (>=
1.1-4)

Goodness-of-Fit Tests for Functional Data
c(
person(given = "Eduardo", family = "García-Portugués",
role = c("aut", "cre"), email = "edgarcia@est-econ.uc3m.es"),
person("Javier", "Álvarez-Liébana", role = "aut",
email = "alvarezljavier@uniovi.es"),
person("Gonzalo", "Álvarez-Pérez", role = "ctb",
email = "gonzaloalvarez@uniovi.es"),
person("Manuel", "Febrero-Bande", role = "ctb",
email = "manuel.febrero@usc.es")
)
Implementation of several goodness-of-fit tests for functional
data. Currently, mostly related with the functional linear model with
functional/scalar response and functional/scalar predictor. The package
allows for the replication of the data applications considered in
García-Portugués, Álvarez-Liébana, Álvarez-Pérez and González-Manteiga
(2019) <arXiv:1909.07686>.
Eduardo García-Portugués [aut, cre],
Javier Álvarez-Liébana [aut],
Gonzalo Álvarez-Pérez [ctb],
Manuel Febrero-Bande [ctb]