Title: Drawing Gapped Cluster Heatmap with 'ggplot2'
Suggests: knitr, dendsort, RColorBrewer, rmarkdown
Packaged: 2021-04-18 22:19:35 UTC; evanbiederstedt

Authors@R: person("Jennifer", "Bryan", email = "jenny@stat.ubc.ca",
Description: An excerpt of the data available at Gapminder.org. For each of 142
countries, the package provides values for life expectancy, GDP per capita,
and population, every five years, from 1952 to 2007.
Suggests: dplyr, gdata, ggplot2, tidyr, testthat

Authors@R: person("Jennifer", "Bryan", email = "jenny@rstudio.com",
Author: Jennifer Bryan [aut, cre] (0000-0002-6983-2759)

Title: The Tidymodels Extension for GARCH Models
Description: Garch framework for use with the 'tidymodels' ecosystem. It includes both univariate and
multivariate methods from the 'rugarch' and 'rmgarch' packages. These models include DCC-Garch, Copula Garch
Depends: R (>= 3.5.0), parsnip, rugarch, rmgarch
Imports: rlang (>= 0.1.2), magrittr, purrr, dplyr, stringr, tibble,
Suggests: tidymodels, tidyverse, modeltime, timetk, lubridate, knitr,
rmarkdown, roxygen2, testthat (>= 3.0.0), covr

Title: The 'Tidymodels' Extension for GARCH Models

Title: Flexible and Robust GARCH-X Modelling
Description: Flexible and robust estimation and inference of generalised autoregressive conditional heteroscedasticity (GARCH) models with covariates based on the results by Francq and Thieu (2018) <doi:10.1017/S0266466617000512>. Suitable subsets of the coefficients can be restricted to zero during estimation, and quasi maximum likelihood methods ensure estimates are generally consistent, even when the standardised innovations are non-normal and/or not independent and identically distributed.

Description: Flexible and robust estimation and inference of generalised autoregressive conditional heteroscedasticity (GARCH) models with covariates based on the results by Francq and Thieu (2018) <doi:10.1017/S0266466617000512>. Coefficients can straightforwardly be set to zero by omission, and quasi maximum likelihood methods ensure estimates are generally consistent and inference valid, even when the standardised innovations are non-normal and/or dependent over time.