An interactive document on  the topic of multidimensional scaling and principal component analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/MDS_PCAShiny/>.

A complement to all editions of *Modern Data
    Science with R* 
     (ISBN: 978-0367191498, publisher URL: 
    <https://www.routledge.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/p/book/9780367191498>).
    This package contains data and code to complete exercises and 
    reproduce examples from the text. It also facilitates connections 
    to the SQL database server used in the book. All editions of the book are 
    supported by this package.

Modeling microstructures of
	human tooth dentin and
	horizontal serial-sectioning of the dentin.
	Corresponding age range of dentin serial sections,
	that is used in stable isotope analyses,
	can be calculated by using this package.

Dimension-reduction methods aim at defining a score that maximizes signal diversity. Three approaches, tree weight, maximum entropy weights, and maximum variance weights are provided. These methods are described in He and Fong (2019) <DOI:10.1002/sim.8212>.

Analyse and visualise multi electrode array data at the single electrode and whole well level, downstream of 'AxIS Navigator 3.6.2 Software' processing. 
             Compare bursting parameters between time intervals and recordings using the bar chart visualisation functions. Compatible with 12- and 24- well plates.

Sentiment analysis is a popular technique in text mining that
    attempts to determine the emotional state of some text. We provide a new
    implementation of a common method for computing sentiment, whereby words are
    scored as positive or negative according to a dictionary lookup. Then the
    sum of those scores is returned for the document. We use the 'Hu' and 'Liu'
    sentiment dictionary ('Hu' and 'Liu', 2004) <doi:10.1145/1014052.1014073>
    for determining sentiment. The scoring function is 'vectorized' by document,
    and scores for multiple documents are computed in parallel via 'OpenMP'.