Functions for a classification method based on receiver operating characteristics (ROC). Briefly, features are selected according to their ranked AUC value in the training set. The selected features are merged by the mean value to form a meta-gene. The samples are ranked by their meta-gene value and the meta-gene threshold that has the highest accuracy in splitting the training samples is determined. A new sample is classified by its meta-gene value relative to the threshold. In the first place, the package is aimed at two class problems in gene expression data, but might also apply to other problems.

Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models.

Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data.

The Reproducible Open Coding Kit ('ROCK', and this package, 'rock')
was developed to facilitate reproducible and open coding, specifically
geared towards qualitative research methods. Although it is a
general-purpose toolkit, three specific applications have been
implemented, specifically an interface to the 'rENA' package that
implements Epistemic Network Analysis ('ENA'), means to process notes
from Cognitive Interviews ('CIs'), and means to work with a decentralized
construct taxonomy ('DCT').
covr, knitr, rENA (>= 0.1.6), rmarkdown, testthat

c(person(given = "Gjalt-Jorn Ygram",
family = "Peters",
role = c("aut", "cre"),
email = "gjalt-jorn@behaviorchange.eu",
comment = c(ORCID = "0000-0002-0336-9589")),
person(given = "Szilvia",
family = "Zorgo",
role = c("ctb"),
comment = c(ORCID = "0000-0002-6916-2097")))
The Reproducible Open Coding Kit ('ROCK', and this package, 'rock')
was developed to facilitate reproducible and open coding, specifically
geared towards qualitative research methods. Although it is a
general-purpose toolkit, three specific applications have been
implemented, specifically an interface to the 'rENA' package that
implements Epistemic Network Analysis ('ENA'), means to process notes
from Cognitive Interviews ('CIs'), and means to work with decentralized
construct taxonomies ('DCTs').