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'.

Performs mean shift classification using linear and 
  k-d tree based nearest neighbor implementations for the Gaussian,
  Epanechnikov, and biweight product kernels.

Quickly make tables of descriptive statistics (i.e., counts, 
    means, confidence intervals) for continuous variables. This 
    package is designed to work in a Tidyverse pipeline, and consideration
    has been given to get results from R to 'Microsoft Word' ® with minimal pain.

Estimate diagnostic classification models (also called cognitive
    diagnostic models) with 'Stan'. Diagnostic classification models are 
    confirmatory latent class models, as described by Rupp et al.
    (2010, ISBN: 978-1-60623-527-0). Automatically generate 'Stan' code for the
    general loglinear cognitive diagnostic diagnostic model proposed by
    Henson et al. (2009) <doi:10.1007/s11336-008-9089-5> and other subtypes that
    introduce additional model constraints. Using the generated 'Stan' code,
    estimate the model evaluate the model's performance using model fit indices,
    information criteria, and reliability metrics.