Analysis of preprocessed dramatic texts, with respect to literary research.
The package provides functions to analyze and visualize information about characters,
stage directions, the dramatic structure and the text itself.
The dramatic texts are expected to be in CSV format, which can be installed from within
the package, sample texts are provided. The package and the reasoning behind it are described in
Reiter et al. (2017) <doi:10.18420/in2017_119>.

c(
person("Nils", "Reiter", email="nils.reiter@ims.uni-stuttgart.de", role=c("aut", "cre"), comment=(ORCID="0000-0003-3193-6170")),
person("Tim", "Strohmayer", email="st151528@stud.uni-stuttgart.de", role=c("ctb")),
person("Janis", "Pagel", email="janis.pagel@ims.uni-stuttgart.de", role=c("ctb")))
Nils Reiter [aut, cre] (0000-0003-3193-6170),
Tim Strohmayer [ctb],
Janis Pagel [ctb]

Calculates the Test-Statistic for the Drift Burst Hypothesis
Calculates the T-Statistic for the drift burst hypothesis from the working paper Christensen, Oomen and Reno (2018) <DOI:10.2139/ssrn.2842535>. The authors' MATLAB code is available upon request, see: <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2842535>.

Provides a R driver for Apache Drill<https://drill.apache.org>, which could connect to the Apache Drill cluster<https://drill.apache.org/docs/installing-drill-on-the-cluster> or drillbit<https://drill.apache.org/docs/embedded-mode-prerequisites> and get result(in data frame) from the SQL query and check the current configuration status. This link <https://drill.apache.org/docs> contains more information about Apache Drill.

c(person("Dayanand Ubrangala", role = c("aut", "cre"), email = "daya6489@gmail.com"),
person("Sayan", "Putatunda", role = c("aut","ctb")),
person("Kiran", "R", role = c("aut","ctb")),
person("Ravi", "Prasad Kondapalli", role = c("aut","ctb")))
Implementing some of the pillars of an automated machine learning pipeline such as (i) Automated data preparation, (ii) Feature engineering, (iii) Model building in classification context that includes techniques such as (a) Regularised regression [1], (b) Logistic regression [2], (c) Random Forest [3], (d) Decision tree [4] and (e) Extreme Gradient Boosting (xgboost) [5], and finally, (iv) Model explanation (using lift chart and partial dependency plots). Accomplishes the above tasks  by running the function instead of writing lengthy R codes. Also provides some additional features such as generating missing at random (MAR) variables and automated exploratory data analysis. Moreover, function exports the model results with the required plots in an HTML vignette report format that follows the best practices of the industry and the academia. [1] Gonzales G B and De Saeger (2018) <doi:10.1038/s41598-018-21851-7>, [2] Sperandei S (2014) <doi:10.11613/BM.2014.003>, [3] Breiman L (2001) <doi:10.1023/A:1010933404324>, [4] Kingsford C and Salzberg S (2008) <doi:10.1038/nbt0908-1011>, [5] Chen Tianqi and Guestrin Carlos (2016) <doi:10.1145/2939672.2939785>.
testthat, knitr, ranger, glmnet, randomForest, rpart,
xgboost, stats, graphics, tidyr, MASS
Dayanand Ubrangala [aut, cre],
Sayan Putatunda [aut, ctb],
Kiran R [aut, ctb],
Ravi Prasad Kondapalli [aut, ctb]