Provides access and analytic functions for Sensus data.
The Rector and Visitors of the University of Virginia

c(person("Stefan", "Feuerriegel", email="stefan.feuerriegel@is.uni-freiburg.de",
role=c("aut", "cre")),
person("Nicolas", "Proellochs", email="nicolas.proellochs@is.uni-freiburg.de",
role=c("aut")))
Stefan Feuerriegel [aut, cre],
Nicolas Proellochs [aut]
Performs a sentiment analysis of textual contents in R. This implementation
utilizes various existing dictionaries, such as Harvard IV, or finance-specific
dictionaries. Furthermore, it can also create customized dictionaries. The latter
uses LASSO regularization as a statistical approach to select relevant terms based on
an exogenous response variable.
tm (>= 0.6), qdapDictionaries, ngramrr (>= 0.1), moments,
stringdist, SnowballC, XML, glmnet, spikeslab (>= 1.1),
ggplot2, mgcv

c(person("Stefan", "Feuerriegel", email="sentiment@sfeuerriegel.com",
role=c("aut", "cre")),
person("Nicolas", "Proellochs", email="nicolas.proellochs@is.uni-freiburg.de",
role=c("aut")))

tm (>= 0.6), qdapDictionaries, ngramrr (>= 0.1), moments,
stringdist, glmnet, spikeslab (>= 1.1), ggplot2
testthat, knitr, rmarkdown, SnowballC, XML, mgcv

c(person("Nicolas", "Proellochs", email="nicolas@nproellochs.com",
role=c("aut", "cre")),
person("Stefan", "Feuerriegel", email="sentiment@sfeuerriegel.com",
role=c("aut")))

Sequential Allocation for Prospective Experiments
Potential randomization schemes are prospectively evaluated when
units are assigned to treatment arms upon entry into the experiment. The schemes
are evaluated for balance on covariates and on predictability (i.e., how well
could a site worker guess the treatment of the next unit enrolled).

Sequential Bayesian Design for Accelerated Life Tests
The planning of accelerated life tests by using sequential Bayesian design, proposed by Lee et al. (2018) <doi:10.1080/00401706.2018.1437475>.

Sequence detector in this package contains a specific automaton model that can be used to learn and detect data and process sequences.
Automaton model in this package is capable of learning and tracing sequences. Automaton model can be found in Krleža, Vrdoljak, Brčić (2019) <doi:10.1109/ACCESS.2019.2955245>.
This research has been partly supported under Competitiveness and Cohesion Operational Programme from the European Regional and Development Fund, as part of the Integrated Anti-Fraud System project no. KK.01.2.1.01.0041. This research has also been partly supported by the European Regional Development Fund under the grant KK.01.1.1.01.0009.