ggplot2, sampling, scales, rmarkdown, ISLR(>= 1.0),
data.table, gridExtra, GGally

ggplot2, sampling, scales, rmarkdown, ISLR(>= 1.0),
data.table, gridExtra, GGally, qpdf

c(
person(given = "Konstantin",
family = "Hopf",
role = c("aut", "cre"),
email = "konstantin.hopf@uni-bamberg.de",
comment = c(ORCID = "0000-0002-5452-0672")),
person(given   = "Andreas",
family = "Weigert",
role = "ctb",
email = "andreas.weigert@uni-bamberg.de"),
person(given = "Ilya",
family = "Kozlovskiy",
role = "ctb",
email = "ilya.kozlovskiy@uni-bamberg.de"),
person(given = "Thorsten",
family = "Staake",
role = "ctb",
email = "thorsten.staake@uni-bamberg.de",
comment = c(ORCID = "0000-0003-1399-4676")))
Methods for analysis of energy consumption data (electricity, gas,
water) at different data measurement intervals. The package provides feature extraction
methods and algorithms to prepare data for data mining and machine learning
applications. Deatiled descriptions of the methods and their application can be found
in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and
Energy Retailing" <doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9>
"Enhancing energy efficiency in the residential sector with smart meter data analytics".
plyr, futile.logger, FNN, stinepack, zoo
stringr, knitr, rmarkdown, ROCR, randomForest, caret, dplyr
Konstantin Hopf [aut, cre] (<https://orcid.org/0000-0002-5452-0672>),
Andreas Weigert [ctb],
Ilya Kozlovskiy [ctb],
Thorsten Staake [ctb] (<https://orcid.org/0000-0003-1399-4676>)

Jun Chen <Chen.Jun2@mayo.edu>, Ehsan Behnam <behnamgh@usc.edu>
Introduces an improved Surrogate Variable Analysis
algorithm that automatically captures salient features from data in the presence
of confounding factors. The algorithm is based on the popular 'SVA' package and
proposes a revision on it that achieves 10 times faster running time while
trading no accuracy loss in return.

Introduces a fast and efficient Surrogate Variable Analysis algorithm that captures variation of unknown sources (batch effects) for high-dimensional genomic data. The algorithm is based on the popular 'SVA' package and proposes a revision on it that achieves more than 10 times faster running time while trading no accuracy loss in return.