Various diffusion models to forecast new product growth. Currently
    the package contains Bass, Gompertz, Gamma/Shifted Gompertz and Weibull curves. See
    Meade and Islam (2006) <doi:10.1016/j.ijforecast.2006.01.005>.

Implements diffusion map method of data
    parametrization, including creation and visualization of
    diffusion map, clustering with diffusion K-means and
	  regression using adaptive regression model.
	  Richards (2009) <doi:10.1088/0004-637X/691/1/32>.

Find, visualize and explore patterns of differential taxa in
    vegetation data (namely in a phytosociological table), using the
    Differential Value (DiffVal). Patterns are searched through
    mathematical optimization algorithms. Ultimately, Total Differential
    Value (TDV) optimization aims at obtaining classifications of
    vegetation data based on differential taxa, as in the traditional
    geobotanical approach.  The Gurobi optimizer, as well as the R package
    'gurobi', can be installed from
    <https://www.gurobi.com/products/gurobi-optimizer/>.  The useful
    vignette Gurobi Installation Guide, from package 'prioritizr', can be
    found here:
    <https://prioritizr.net/articles/gurobi_installation_guide.html>.

A HTML widget that shows differences between files
    (text, images, and data frames).

Statistical hypothesis testing of pattern heterogeneity
    via differences in underlying distributions across multiple
    contingency tables. Five tests are included: the comparative
    chi-squared test (Song et al. 2014) <doi:10.1093/nar/gku086>
    (Zhang et al. 2015) <doi:10.1093/nar/gkv358>, the Sharma-Song
    test (Sharma et al. 2021) <doi:10.1093/bioinformatics/btab240>, 
    the heterogeneity test, the marginal-change test (Sharma et al.
    2020) <doi:10.1145/3388440.3412485>, and the strength test
    (Sharma et al. 2020) <doi:10.1145/3388440.3412485>. Under the
    null hypothesis that row and column variables are statistically
    independent and joint distributions are equal, their test
    statistics all follow an asymptotically chi-squared distribution.
    A comprehensive type analysis categorizes the relation among the
    contingency tables into type null, 0, 1, and 2 (Sharma et al.
    2020) <doi:10.1145/3388440.3412485>. They can identify
    heterogeneous patterns that differ in either the first order
    (marginal) or the second order (differential departure from
    independence). Second-order differences reveal more
    fundamental changes than first-order differences across
    heterogeneous patterns.