Provides a systematic framework for
 integrating multiple modalities of assays profiled on the same set of
 samples. The goal is to identify genes that are altered in cancer
 either marginally or consistently across different assays. The
 heterogeneity among different platforms and different samples are
 automatically adjusted so that the overall alteration magnitude can
 be accurately inferred. See Tong and Coombes (2012) 
 <doi:10.1093/bioinformatics/bts561>.

Generates a 'Graphviz' graph of the most significant 3-way 
    interaction gains (i.e. conditional information gains) based on a provided 
    discrete data frame. Various output formats are supported ('Graphviz', SVG, 
    PNG, PDF, PS). For references, see the webpage of Aleks Jakulin 
    <http://stat.columbia.edu/~jakulin/Int/>.

Offers modeling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.

An implementation of a framework for drug sensitivity prediction from various genetic characterizations using ensemble approaches. Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach.

Four datasets are provided here from the 'Intendo' game
    'Super Jetroid'. It is data from the 2015 year of operation and it comprises
    a revenue table ('all_revenue'), a daily users table ('users_daily'), a user
    summary table ('user_summary'), and a table with data on all user sessions
    ('all_sessions'). These core datasets come in different sizes, and, each of
    them has a variant that was intentionally made faulty (totally riddled with
    errors and inconsistencies). This suite of tables is useful for testing with
    packages that focus on data validation and data documentation.