Title: Learning Data Cleaning, Visual Analysis and Model Performance
Description: Learn data cleaning, visual data analysis and model performance assessment (KS, AUC and ROC), package core is in the vignette documentation explaining last topics as a tutorial.
Imports: ROCR, ggplot2, gridExtra, pander, plyr, reshape2, scales

Title: Title: Learn Data Science Through the "Data Science Live Book"
Description: Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book <http://livebook.datascienceheroes.com/> are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
Imports: ROCR, ggplot2, gridExtra, pander, reshape2, scales, dplyr,

Description: Around 10% of almost any predictive modeling project
is spent in predictive modeling, 'funModeling' and the book <http://
livebook.datascienceheroes.com/> are intended to cover remaining 90%: data
preparation, profiling, selecting best variables 'dataViz', assessing model

Title: Learn Data Science Through the "Data Science Live Book"

Title: Exploratory Data Analysis and Data Preparation Tool-Box Book
Description: Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book (<http://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
Packaged: 2017-06-03 23:06:58 UTC; oblaphouses

Packaged: 2017-06-17 15:47:01 UTC; oblaphouses

Packaged: 2017-07-09 16:08:58 UTC; oblaphouses

lazyeval, utils, RColorBrewer, moments, entropy
Packaged: 2017-12-18 20:23:12 UTC; oblaphouses

Packaged: 2018-01-21 18:39:11 UTC; oblaphouses

Authors@R: person("Pablo", "Casas", role = c("aut", "cre"), email = "pcasas.biz@gmail.com")
Packaged: 2018-07-31 21:54:10 UTC; oblaphouses

Packaged: 2019-02-13 01:32:59 UTC; oblaphouses

Description: Around 10% of almost any predictive modeling project is spent in predictive modeling, 'funModeling' and the book Data Science Live Book (<https://livebook.datascienceheroes.com/>) are intended to cover remaining 90%: data preparation, profiling, selecting best variables 'dataViz', assessing model performance and other functions.
lazyeval, utils, RColorBrewer, moments, entropy, pROC
Packaged: 2019-08-01 17:46:25 UTC; oblaphouses