Capture code evaluations and script executions by expressions,
    outputs, and condition calls for logging.

Provides a function (echo_find()) designed to find rhythms 
    from data using extended harmonic oscillators. For more information,
    see H. De los Santos et al. (2020) <doi:10.1093/bioinformatics/btz617> .

Easily import multi-frequency acoustic data stored in 'HAC' files (see <http://biblio.uqar.ca/archives/30005500.pdf> for more information on the format), and produce echogram visualisations with predefined or customized color palettes. It is also possible to merge consecutive echograms; mask or delete unwanted echogram areas; model and subtract background noise; and more important, develop, test and interpret different combinations of frequencies in order to perform acoustic filtering of the echogram's data.

Implements choice models based on economic theory, including estimation using Markov chain Monte Carlo (MCMC), prediction, and more. Its usability is inspired by ideas from 'tidyverse'. Models include versions of the Hierarchical Multinomial Logit and Multiple Discrete-Continous (Volumetric) models with and without screening. The foundations of these models are described in Allenby, Hardt and Rossi (2019)  <doi:10.1016/bs.hem.2019.04.002>. Models with conjunctive screening are described in Kim, Hardt, Kim and Allenby (2022) <doi:10.1016/j.ijresmar.2022.04.001>. Models with set-size variation are described in Hardt and Kurz (2020) <doi:10.2139/ssrn.3418383>.

An R interface to United States Environmental 
    Protection Agency (EPA) Environmental Compliance 
    History Online ('ECHO') Application Program Interface
    (API). 'ECHO' provides information about EPA permitted 
    facilities, discharges, and other reporting info 
    associated with permitted entities. Data are obtained 
    from <https://echo.epa.gov/>.

Extends the Changes-in-Changes model a la Athey and Imbens
    (2006) <doi:10.1111/j.1468-0262.2006.00668.x> to multiple cohorts and
    time periods, which generalizes difference-in-differences estimation
    techniques to the entire distribution. Computes quantile treatment
    effects for every possible two-by-two combination in ecic(). Then,
    aggregating all bootstrap runs adds the standard errors in
    summary_ecic(). Results can be plotted with plot_ecic() aggregated
    over all cohort-group combinations or in an event-study style for
    either individual periods or individual quantiles.