c(person("Lindsay V.", "Clark", email = "Lindsay.Clark@seattlechildrens.org", role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-3881-9252")),
person("Alistair J.", "Hall", role = "ctb",
comment = c(ORCID = "0000-0001-9293-8909")),
person("Handunnethi Nihal", "de Silva", role = "ctb"),
person("Tyler William", "Smith", role = "ctb",
comment = c(ORCID = "0000-0001-7683-2653")))

Polytomous logistic regression for fixed and mixed effects
Antti Arppe, with ideas and input from R. Harald Baayen
Logistic regression modeling for polytomous settings (more
than two categorical outcomes) with both fixed and mixed effect
predictors, and univariate analysis of categorical, unordered
data.

Ideas and input provided by R. Harald Baayen as well
as Terrance M. Nearey in the development of the polytomous
Poisson reformulation function allowing for mixed-effects
logistic regression modeling of polytomous outcome settings are
greatly appreciated.

Logistic regression modeling for polytomous settings (more than two categorical outcomes) with both fixed and mixed effect predictors, and univariate and bivariate analysis of categorical, unordered data.

Bootstrapped Basis Regression with Oracle Model Selection
Routines for flexible functional form estimation via basis
regression, with model selection via the adaptive LASSO or SCAD
to prevent overfitting.
glmnet (>= 1.5.1), ncvreg, Formula, Matrix
'helpers.r' 'fn_pred.r' 'fn_plot.r' 'fn_poly.r' 'polywog.r'

glmnet (>= 1.5.1), ncvreg, Formula, Matrix, miscTools
'helpers.r' 'fn_pred.r' 'fn_plot.r' 'fn_poly.r' 'fitters.r'
'polywog.r' 'fn_cv.r' 'fn_marg.r'

Routines for flexible functional form estimation via basis
regression, with model selection via the adaptive LASSO or SCAD to prevent
overfitting.
foreach, Formula, glmnet (>= 1.9-5), iterators, Matrix, ncvreg
(>= 2.4-0), Rcpp (>= 0.11.0), stringr

Fawn Hornsby, Ryan Nielson, and Trent McDonald
(www.west-inc.com)
This package fits a patch occupancy model

Graphical Check for Proportional Odds Assumption
person(given = "Melissa",
family = "Wong",
role = c("aut", "cre"),
email = "melissa.wong.stats@gmail.com",
comment = c(ORCID = "0000-0002-2973-6279"))
Implements the method described at the UCLA Statistical
Consulting site <https://stats.idre.ucla.edu/r/dae/ordinal-logistic-regression/>
for checking if the proportional odds assumption holds for a
cumulative logit model.
dplyr, tidyr, ggplot2, assertthat, stats, rlang, magrittr,
stringr