Suggests: knitr, kableExtra, sdcHierarchies, testthat
Description: A statistical disclosure control tool to protect frequency tables in cases where small values are sensitive. The function PLSrounding() performs small count rounding of necessary inner cells so that all small frequencies of cross-classifications to be published (publishable cells) are rounded. This is equivalent to changing micro data since frequencies of unique combinations are changed. Thus, additivity and consistency are guaranteed. The methodology is described in Langsrud and Heldal (2018) <https://www.researchgate.net/publication/327768398_An_Algorithm_for_Small_Count_Rounding_of_Tabular_Data>.

Suggests: knitr, rmarkdown, kableExtra, sdcHierarchies, testthat

Author: Dayanand Ubrangala; Kiran R; Ravi Prasad Kondapalli
Description: Exploratory analysis on any input data describing the structure and the relationships present in the data. The package automatically select the variable and does related descriptive statistics. Analyzing information value, weight of evidence, custom tables, summary statistics, graphical techniques will be performed for both numeric and categorical predictors.
Packaged: 2018-04-06 07:06:09 UTC; dubrangala

Suggests: psych, Hmisc, smbinning,testthat,knitr, covr
Packaged: 2018-05-21 06:00:31 UTC; dubrangala

Packaged: 2018-07-25 07:39:41 UTC; dubrangala

Packaged: 2019-05-04 12:10:55 UTC; dubrangala

person("Ravi", "Prasad Kondapalli", role = c("aut","ctb")),
person("Sayan", "Putatunda", role = c("aut","ctb")))
Packaged: 2019-07-03 05:50:11 UTC; dubrangala

Packaged: 2020-01-20 12:34:35 UTC; dubrangala

Packaged: 2020-04-01 07:30:29 UTC; dubrangala

Packaged: 2020-04-25 05:36:35 UTC; dubrangala

Imports: ggplot2, sampling, scales, rmarkdown, ISLR(>= 1.0),
Packaged: 2020-07-10 09:37:35 UTC; dubrangala

Packaged: 2021-04-03 18:25:13 UTC; dubrangala

Packaged: 2021-06-05 09:51:45 UTC; dubrangala

Title: Methods for Smart Meter Data Analysis
Description: 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
"Enhancing energy efficiency in the residential sector with smart meter data analytics".
Imports: plyr, futile.logger, FNN, stinepack, zoo
Suggests: stringr, knitr, rmarkdown, ROCR, randomForest, caret, dplyr