Bandwidth Selectors for Local Linear Quantile Regression
c(
person("Mercedes", "Conde-Amboage", role = c("aut", "cre"),
email = "mercedes.amboage@usc.es", comment = c(ORCID = "0000-0003-0306-8142")),
person("Cesar", "Sanchez-Sellero", role = "aut",
email = "cesar.sanchez@usc.es",comment = c(ORCID = "0000-0003-0046-0819"))
)
R (>= 2.6), quantreg, KernSmooth, nleqslv
Bandwidth selectors for local linear quantile regression, including cross-validation and plug-in methods. The local linear quantile regression estimate is also implemented.
Mercedes Conde-Amboage [aut, cre]
(<https://orcid.org/0000-0003-0306-8142>),
Cesar Sanchez-Sellero [aut] (<https://orcid.org/0000-0003-0046-0819>)

Dataset of Regional COVID-19 Deaths per 100,000 Pop (NUTS)
Dataset containing COVID-19 deaths (absolute and per 100,000 pop) at the regional level (mostly NUTS 3) for 31 EU/EFTA countries.

Dataset containing cumulative COVID-19 deaths (absolute and per 100,000 pop) at the regional level (mostly NUTS 3) for 31 EU/EFTA countries.

MASS, partykit, rpart, RColorBrewer, grDevices, gridExtra,
ggplot2, cluster, parallel, igraph, reshape2, qgraph, stats,
graphics
Getting insight into a forest of classification trees, by calculating similarities
between the trees, and subsequently clustering them. Each cluster is represented by it's most
central cluster member. Sies, A & Van Mechelen, I. (paper submitted for publication).
Aniek Sies [aut, cre],
Iven Van Mechelen [ths]

MASS, partykit, rpart, RColorBrewer, grDevices, gridExtra,
ggplot2, cluster, parallel, igraph, reshape2, qgraph, stats,
graphics,plyr, plot.matrix
Getting insight into a forest of classification trees, by calculating similarities between the trees, and subsequently clustering them. Each cluster is represented by it's most central cluster member. The package implements the methodology described in Sies & Van Mechelen (2020). Sies, A., & Van Mechelen, I. (2020). C443: A methodology to see a forest for the trees. Journal of Classification. <doi:10.1007/s00357-019-09350-4>.