c(person("Chau", "Tran", email = "chautran@ucsb.edu", role = c("aut", "cre")),
person("Sang-Yun", "Oh", role = c("aut")),
person("Pedro", "Cisneros-Velarde", role = c("aut")),
person("Alexander", "Petersen", role = c("aut")))
An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) <http://proceedings.mlr.press/v108/cisneros20a.html>.
Chau Tran [aut, cre],
Sang-Yun Oh [aut],
Pedro Cisneros-Velarde [aut],
Alexander Petersen [aut]

Import an Observable Notebook as HTML Widget
c(
person(
"Julien", "Barnier",
email = "julien.barnier@cnrs.fr",
role = c("aut", "cre")
),
person(
"Kenton", "Russell",
email = "kent.russell@timelyportfolio.com",
role = "aut"
))
Allows loading and displaying an Observable notebook (online JavaScript
notebooks powered by <https://observablehq.com>) as an HTML Widget in an R
session, 'shiny' application or 'rmarkdown' document.
gapminder, knitr, rmarkdown, readr, dplyr, tidyr, lubridate,
stringr
Julien Barnier [aut, cre],
Kenton Russell [aut]

gapminder, palmerpenguins, knitr, rmarkdown, readr, dplyr,
tidyr, lubridate, stringr

Fast algorithms for the Theil-Sen estimator
and Siegel's repeated median slope estimator. The implementation is based on algorithms by
Dillencourt et. al (1992) <doi:10.1142/S0218195992000020> and Matousek et. al (1998)  <doi:10.10.1007/PL00009190. Both algorithms run in quasilinear time.

Fast algorithms for the Theil-Sen estimator
and Siegel's repeated median slope estimator. The implementation is based on algorithms by
Dillencourt et. al (1992) <doi:10.1142/S0218195992000020> and Matousek et. al (1998)  <doi:10.1007/PL00009190>. Both algorithms run in quasilinear time.

Fast algorithms for the Theil-Sen estimator,
Siegel's repeated median slope estimator, and Passing-Bablok regression.
The implementation is based on algorithms by
Dillencourt et. al (1992) <doi:10.1142/S0218195992000020> and Matousek et. al (1998)  <doi:10.1007/PL00009190>.
The implementation of Passing-Bablok regression is explained in detail in
Raymaekers J., Dufey F. (2022). Equivariant Passing-Bablok regression in quasilinear time. <arXiv:2202.08060>.
All algorithms run in quasilinear time.