Computation of Multiscale Codependence Analysis and spatial eigenvector maps.

Fast categorization of items based on external code data identified by 
  regular expressions. A typical use case considers patient with medically coded 
  data, such as codes from the International Classification of Diseases ('ICD') or 
  the Anatomic Therapeutic Chemical ('ATC') classification system. 
  Functions of the package relies on a triad of objects: (1) case data with unit 
  id:s and possible dates of interest; (2) external code data for corresponding 
  units in (1) and with optional dates of interest and; (3) a classification 
  scheme ('classcodes' object) with regular expressions to identify and 
  categorize relevant codes from (2). 
  It is easy to introduce new classification schemes ('classcodes' objects) or  
  to use default schemes included in the package. Use cases includes patient 
  categorization based on 'comorbidity indices' such as 'Charlson', 'Elixhauser', 
  'RxRisk V', or the 'comorbidity-polypharmacy' score (CPS), as well as adverse 
  events after hip and knee replacement surgery.

Predicts 3 to 12 months prognosis in Chronic Obstructive Pulmonary Disease (COPD) patients hospitalized for severe exacerbations, as described in Almagro et al. (2014) <doi:10.1378/chest.13-1328>.

Proposes Seq2seq Time-Feature Analysis using an Encoder-Decoder to project into latent space and a Forward Network to predict the next sequence.

Augment clinical data with metadata to create
    output used in conventional publications and reports.

Categorize links and nodes from multiple networks in 3 categories: Common links (alpha) specific links (gamma), and different links (beta). Also categorizes the links into sub-categories and groups. The package includes a visualization tool for the networks. More information about the methodology can be found at: Gysi et. al., 2018 <arXiv:1802.00828>.

A collection of coding functions as alternatives to the standard
    functions in the stats package, which have names starting with 'contr.'.  Their
    main advantage is that they provide a consistent method for defining marginal
    effects in factorial models.  In a simple one-way ANOVA model the
    intercept term is always the simple average of the class means.