stats, mgcv, ranger, glmnet, magrittr, dplyr, forcats,
ggplot2, tidyr, foreach, tidyselect, parallel, doParallel

Tools to fill missing values in satellite data and to develop new
gap-fill algorithms. The methods are tailored to data (images) observed
at equally-spaced points in time. The package is illustrated with MODIS
NDVI data.

c(person("Florian", "Gerber", role = c("aut", "cre"),
email = "flora.fauna.gerber@gmail.com",
comment = c(ORCID = "0000-0001-8545-5263")))
Florian Gerber [aut, cre] (<https://orcid.org/0000-0001-8545-5263>)

Functions for Drawing Gapped Cluster Heatmap with ggplot2
The gap encodes the distance between clusters and improves
interpretation of cluster heatmaps. The gaps can be of the same
distance based on a height threshold to cut the dendrogram. Another
option is to vary the size of gaps based on the distance between
clusters.

Drawing Gapped Cluster Heatmap with 'ggplot2'

person("Jennifer", "Bryan", email = "jenny@stat.ubc.ca",
role = c('aut', 'cre'))
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.

person("Jennifer", "Bryan", email = "jenny@rstudio.com",
role = c('aut', 'cre'),
comment = c(ORCID = "0000-0002-6983-2759"))

The Tidymodels Extension for GARCH Models
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
and Go-GARCH among others.
rlang (>= 0.1.2), magrittr, purrr, dplyr, stringr, tibble,
tidyr, dials
tidymodels, tidyverse, modeltime, timetk, lubridate, knitr,
rmarkdown, roxygen2, testthat (>= 3.0.0), covr

The 'Tidymodels' Extension for GARCH Models

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.