Provide addins for 'RStudio'.
  It currently contains 3 addins. The first to add a shortcut for the double pipe. The second is to add a shortcut for the same operator. And the third to simplify the creation of vectors from texts pasted from the computer transfer area.

Browse through a continuously updated list of existing RStudio 
    addins and install/uninstall their corresponding packages.

'RStudio' allows to show and navigate for the outline of a 
    R Markdown file, but not for R Markdown projects with multiple 
    files. For this reason, I have developed several 'RStudio' addins capable 
    of show project outline. Each addin is specialized in showing projects 
    of different types: R Markdown project, 'bookdown' package project 
    and 'LaTeX' project. There is a configuration file that allows you 
    to customize additional searches.

Fit Generalized Additive Models (GAM) using 'mgcv' with 'parsnip'/'tidymodels'
    via 'additive' <doi:10.5281/zenodo.4784245>. '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>.

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.