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.

A specialized collection of measles epidemiological models built on the 'epiworldR' framework. This package is a spinoff from 'epiworldR' focusing specifically on measles transmission dynamics. It includes models for school settings with quarantine and isolation policies, mixing models with population groups, and risk-based quarantine strategies. The models use Agent-Based Models (ABM) with a fast 'C++' backend from the 'epiworld' library. Ideal for studying measles outbreaks, vaccination strategies, and intervention policies.

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.