Create custom keyboard shortcuts to examine code selected in the 'Rstudio' editor.
             F3 can for example yield 'str(selection)' and F7 open the source
             code of CRAN and base package functions on 'github'.

Implements the "Stemming Algorithm for the Portuguese Language" <DOI:10.1109/SPIRE.2001.10024>.

Functions that simplify submitting R scripts to a 'Slurm' 
    workload manager, in part by automating the division of embarrassingly
    parallel calculations across cluster nodes.

Provides functions to generate response-surface designs, 
    fit first- and second-order response-surface models, 
    make surface plots, obtain the path of steepest ascent, 
    and do canonical analysis. A good reference on these methods 
    is Chapter 10 of Wu, C-F J and Hamada, M (2009) 
    "Experiments: Planning, Analysis, and Parameter Design Optimization"
    ISBN 978-0-471-69946-0. An early version of the package is
    documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.

We provide functions to perform an empirical small telescopes
    analysis. This package contains 2 functions, SmallTelescopes() and 
    EstimatePower(). Users only need to call SmallTelescopes() to conduct 
    the analysis. For more information on small telescopes analysis see
    Uri Simonsohn (2015) <doi:10.1177/0956797614567341>.

Aims at loading Facebook and Instagram advertising data from
    'Smartly.io' into R. 'Smartly.io' is an online advertising service that enables
    advertisers to display commercial ads on social media networks (see <http://www.smartly.io/> for more information).
    The package offers an interface to query the 'Smartly.io' API and loads data directly into R for further data processing and data analysis.

Implements popular methods for matching in time-varying
    observational studies. Matching is difficult in this scenario because
    participants can be treated at different times which may have an
    influence on the outcomes. The core methods include: "Balanced Risk
    Set Matching" from Li, Propert, and Rosenbaum (2011)
    <doi:10.1198/016214501753208573> and "Propensity Score Matching with
    Time-Dependent Covariates" from Lu (2005)
    <doi:10.1111/j.1541-0420.2005.00356.x>. Some functions use the
    'Gurobi' optimization back-end to improve the optimization problem
    speed; the 'gurobi' R package and associated software can be
    downloaded from <https://www.gurobi.com> after obtaining a license.