Includes marginal maximum likelihood estimation and joint maximum
    likelihood estimation for unidimensional and multidimensional 
    item response models. The package functionality covers the 
    Rasch model, 2PL model, 3PL model, generalized partial credit model, 
    multi-faceted Rasch model, nominal item response model, 
    structured latent class model, mixture distribution IRT models, 
    and located latent class models. Latent regression models and 
    plausible value imputation are also supported. For details see
    Adams, Wilson and Wang, 1997 <doi:10.1177/0146621697211001>,
    Adams, Wilson and Wu, 1997 <doi:10.3102/10769986022001047>,
    Formann, 1982 <doi:10.1002/bimj.4710240209>,
    Formann, 1992 <doi:10.1080/01621459.1992.10475229>.

Agglomerative hierarchical clustering with a bespoke distance 
  measure based on medication similarities in the Anatomical Therapeutic 
  Chemical Classification System, medication timing and 
  medication amount or dosage. Tools for summarizing, illustrating and 
  manipulating the cluster objects are also available.

The Tanaka method enhances the representation of topography on
    a map using shaded contour lines. In this simplified implementation of
    the method, north-west white contours represent illuminated topography
    and south-east black contours represent shaded topography. See Tanaka
    (1950) <doi:10.2307/211219>.

A two-stage regression method that can be used when various input data types are correlated, for example gene expression and methylation in drug response prediction. In the first stage it uses the upstream features (such as methylation) to predict the response variable (such as drug response), and in the second stage it uses the downstream features (such as gene expression) to predict the residuals of the first stage. In our manuscript (Aben et al., 2016, <doi:10.1093/bioinformatics/btw449>), we show that using TANDEM prevents the model from being dominated by gene expression and that the features selected by TANDEM are more interpretable.