Tools for creating data validation pipelines and tidy reports. This
package offers a framework for exploring and validating data frame like
objects using 'dplyr' grammar of data manipulation.
keyholder, dplyr (>= 0.7.0), rlang, tidyr (>= 0.7.0)

keyholder, dplyr (>= 0.7.0), rlang, tibble, tidyr (>= 0.7.0)

Tools for creating data validation pipelines and
tidy reports. This package offers a framework for exploring and
validating data frame like objects using 'dplyr' grammar of data
manipulation.
dplyr (>= 0.7.0), keyholder, rlang, tibble, tidyr (>= 0.7.0)

dplyr (>= 0.7.0), keyholder, rlang, tibble, tidyr (>= 0.7.0),
magrittr

Bindings for additional models for use with the 'parsnip' package.
Models include prediction rule ensembles (Friedman and Popescu, 2008)
<doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992 ISBN: 1558602380), and
Cubist (Kuhn and Johnson, 2013) <doi:10.1007/978-1-4614-6849-3>.
testthat, C50, Cubist, xrf (>= 0.2.0), covr, modeldata,
spelling
purrr, rlang, tibble, dials, tidyr, dplyr

testthat, C50, Cubist, xrf (>= 0.2.0), covr, modeldata,
spelling, recipes, knitr, rmarkdown
purrr, rlang, tibble, dials, tidyr, dplyr, stringr, generics
(>= 0.1.0)

Bindings for additional models for use with the 'parsnip'
package.  Models include prediction rule ensembles (Friedman and
Popescu, 2008) <doi:10.1214/07-AOAS148>, C5.0 rules (Quinlan, 1992
ISBN: 1558602380), and Cubist (Kuhn and Johnson, 2013)
<doi:10.1007/978-1-4614-6849-3>.
dials, dplyr, generics (>= 0.1.0), purrr, rlang, stringr,
tibble, tidyr
C50, covr, Cubist, knitr, recipes, rmarkdown, spelling,
testthat, xrf (>= 0.2.0)

dials (>= 0.1.1.9001), dplyr, generics (>= 0.1.0), purrr,
rlang, stringr, tibble, tidyr
C50, covr, Cubist, knitr, modeldata, recipes, rmarkdown,
spelling, testthat (>= 3.0.0), xrf (>= 0.2.0)

Univariate GARCH-MIDAS, Double-Asymmetric GARCH-MIDAS and
MEM-MIDAS
Adds the MIxing Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components within the GARCH (Engle et al. (2013) <doi:10.1162/REST_a_00300>) and MEM (Engle (2002) <doi:10.1002/jae.683>) frameworks, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. 'rumidas' also offers a summary tool, which synthesizes the main information of the estimated model. Finally, an option to generate one-step-ahead volatility forecasts automatically divides the whole period into a training and testing samples.