Description: Performs angle-based outlier detection on a given dataframe. Three methods are available, a full but slow implementation using all the data that has cubic complexity, a fully randomized one which is way more efficient and another using k-nearest neighbours. These algorithms are specially well suited for high dimensional data outlier detection.

Title: R Interface to the A/B Street Transport System Simulation
Description: Provides functions to convert origin-destination data,
represented as straight 'desire lines' in the 'sf' Simple Features
class system, into JSON files that can be directly imported into
A/B Street <https://www.abstreet.org>, a free and open source tool
for simulating urban transport systems and scenarios of change
Suggests: knitr, dplyr (>= 1.0.0), rmarkdown
Author: Nathanael Sheehan [aut, cre] (<https://orcid.org/0000-0002-2779-0976>),

Suggests: knitr, dplyr (>= 1.0.0), rmarkdown, pct, tmap, foreign

Suggests: knitr, dplyr (>= 1.0.0), rmarkdown, tmap, pct, foreign

Title: An R-Shiny Application for Creating Visual Abstracts
Description: An R-Shiny application to create visual abstracts for original research. A variety of user defined options and formatting are included.

Authors@R: c(person(given="Quentin F.", family="Gronau", role=c("aut", "cre"),
Description: Provides functions for Bayesian A/B testing including prior elicitation
options based on Kass and Vaidyanathan (1992) <doi:10.1111/j.2517-6161.1992.tb01868.x>.
Imports: Rcpp (>= 0.12.14), mvtnorm, sn, VGAM, truncnorm, plotrix,
Maintainer: Quentin F. Gronau <Quentin.F.Gronau@gmail.com>

Imports: Rcpp (>= 0.12.14), mvtnorm, sn, qgam, truncnorm, plotrix,

Title: Abundant regression and high-dimensional principal fitted
Description: Fit and predict with the high-dimensional principal fitted

Title: High-Dimensional Principal Fitted Components and Abundant
components model.  This model is described by Cook, Forzani, and Rothman (2012)

Packaged: 2022-01-04 12:14:25 UTC; adamrothman

Title: Access the Twitter Academic Research Product Track V2 API
Description: Package to query the Twitter Academic Research Product Track (<https://developer.twitter.com/en/solutions/academic-research>),
providing access to full-archive search and other v2 API endpoints. Functions
are written with academic research in mind. They provide flexibility in how
the user wishes to store collected data, and encourage regular storage of data
to mitigate loss when collecting large volumes of tweets. They also provide
workarounds to manage and reshape the format in which data is provided on
Author: Christopher Barrie [aut, cre] (<https://orcid.org/0000-0002-9156-990X>),
Justin Chun-ting Ho [aut] (<https://orcid.org/0000-0002-7884-1059>)