Packaged: 2019-05-06 16:41:51 UTC; kearneymw

Title: Data Quality Assessment for Process-Oriented Data
person("Niels", "Martin", email = "niels.martin@uhasselt.be", role = c("aut", "cre")),
person("Greg", "Van Houdt", email = "greg.vanhoudt@uhasselt.be", role = "ctb"),
person("Gert", "Janssenswillen", email = "gert.janssenswillen@uhasselt.be", role = "ctb"))
Description: Provides a variety of methods to identify data quality issues in process-oriented data, which are useful to verify data quality in a process mining context. Builds on the class for activity logs implemented in the package 'bupaR'. Methods to identify data quality issues either consider each activity log entry independently (e.g. missing values, activity duration outliers,...), or focus on the relation amongst several activity log entries (e.g. batch registrations, violations of the expected activity order,...).
Imports: dplyr, lubridate, stringdist, stringr, tidyr, xesreadR, rlang,
bupaR, readr, edeaR, magrittr, purrr, glue, miniUI, shiny

bupaR (>= 0.5.0), readr, edeaR, magrittr, purrr, glue, miniUI,

Title: Package for Deep Architectures and Restricted Boltzmann Machines
Description: The darch package is built on the basis of the code from G. E.
Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief
nets). This package is for generating neural networks with many layers (deep
architectures) and train them with the method introduced by the publications
"A fast learning algorithm for deep belief nets" (G. E. Hinton, S. Osindero,
dimensionality of data with neural networks" (G. E. Hinton, R. R.
Salakhutdinov; doi: 10.1126/science.1127647). This method includes a pre
training with the contrastive divergence method published by G.E Hinton
training algorithms like backpropagation or conjugate gradients.
Additionally, supervised fine-tuning can be enhanced with maxout and
dropout, two recently developed techniques to improve fine-tuning for deep
Imports: stats, methods, futile.logger (>= 1.4.1), ff (>= 2.2-13)
Collate: 'net.R' 'darch.R' 'backpropagation.R' 'darch.Add.R'