Misc Functions of the Department of Statistics, Probability
Theory Group (Formerly: E1071), TU Wien

cluster, mlbench, nnet, randomForest, rpart, SparseM, xtable,
Matrix, MASS, slam

graphics, grDevices, class, stats, methods, utils, proxy
Functions for latent class analysis, short time Fourier
transform, fuzzy clustering, support vector machines,
shortest path computation, bagged clustering, naive Bayes
classifier, generalized k-nearest neighbour ...

Dynamic Web Analytics for the Energy Industry
A 'Shiny' web application for energy industry analytics.
Take an overview of the industry, measure Key Performance Indicators,
identify changes in the industry over time, and discover new relationships in the data.
d3heatmap, dplyr, DT, energyr, googleVis, leaflet, shiny,
shinydashboard

Dynamic Web-Based Analytics for the Energy Industry

dplyr, DT, energyr, googleVis, leaflet, plotly (>= 4.5.2),
shiny, shinydashboard

dplyr, DT, energyr, googleVis, leaflet, plotly (>= 4.5.2),
shiny, shinydashboard, shinytest, shinyWidgets

Empirical Bayes Smoothing Splines with Correlated Errors
c(person(given = "Francisco",
family = "Rosales",
role = c("aut", "cre"),
email = "francisco@brein.pe"),
person(given = "Tatyana",
family = "Krivobokova",
role = "aut"),
person(given = "Paulo",
family = "Serra",
role = "aut"))
Presents a statistical method that uses a recursive algorithm for signal extraction. The method handles a non-parametric estimation for the correlation of the errors. For details on the methods see: Serra, Krivobokova and Rosales (2018) <arXiv:1812.06948>.
Brobdingnag, parallel, nlme, Matrix, MASS, splines, Rcpp
Francisco Rosales [aut, cre],
Tatyana Krivobokova [aut],
Paulo Serra [aut]

"Empirical Bayes Smoothing Splines with Correlated Errors"
Francisco Rosales, Tatyana Krivobokova, Paulo Serra.
Presents a statistical method that uses a recursive algorithm for signal extraction. The method handles a non-parametric estimation for the correlation of the errors. See "Serra", "Krivobokova" and "Rosales" (2018) <arXiv:1812.06948> for details.