Bindings for Generalized Additive Models (GAM)
c(person(given = "Hamada S.",
family = "Badr",
email = "badr@jhu.edu",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-9808-2344")))
Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels'
via 'additive' <doi:10.5281/zenodo.4882764>. 'tidymodels' is a collection of
packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
The technical details of 'mgcv' are described in Wood (2017)
<doi:10.1201/9781315370279>.
dplyr, purrr, rlang, stats, tibble, utils
covr, devtools, knitr, recipes, rmarkdown, roxygen2,
spelling, testthat, workflows

Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels'
via 'additive' <doi:10.5281/zenodo.5091179>. 'tidymodels' is a collection of
packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
The technical details of 'mgcv' are described in Wood (2017)
<doi:10.1201/9781315370279>.

Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels'
via 'additive' <doi:10.5281/zenodo.5539481>. 'tidymodels' is a collection of
packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
The technical details of 'mgcv' are described in Wood (2017)
<doi:10.1201/9781315370279>.

Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels'
via 'additive' <doi:10.5281/zenodo.5884867>. 'tidymodels' is a collection of
packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
The technical details of 'mgcv' are described in Wood (2017)
<doi:10.1201/9781315370279>.

Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels'
via 'additive' <doi:10.5281/zenodo.6654298>. 'tidymodels' is a collection of
packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>).
The technical details of 'mgcv' are described in Wood (2017)
<doi:10.1201/9781315370279>.

Additive Data Envelopment Analysis Models
Provides functions for calculating efficiency with two types of additive Data Envelopment Analysis models: (i) Generalized Efficiency Measures: unweighted additive model (Cooper et al., 2007 <doi:10.1007/978-0-387-45283-8>), Range Adjusted Measure (Cooper et al., 1999, <doi:10.1023/A:1007701304281>), Bounded Adjusted Measure (Cooper et al., 2011 <doi:10.1007/s11123-010-0190-2>), Measure of Inefficiency Proportions (Cooper et al., 1999 <doi:10.1023/A:1007701304281>), and the Lovell-Pastor Measure (Lovell and Pastor, 1995 <doi:10.1016/0167-6377(95)00044-5>); and (ii) the Slacks-Based Measure (Tone, 2001 <doi:10.1016/S0377-2217(99)00407-5>). The functions provide several options: (i) constant and variable returns to scale; (ii) fixed (non-controllable) inputs and/or outputs; (iii) bounding the slacks so that unrealistically large slack values are avoided; and (iv) calculating the efficiency of specific Decision-Making Units (DMUs), rather than of the whole sample. Package additiveDEA also provides a function for reducing computation time when datasets are large.