Functions read a dataframe containing one or more International Classification of Diseases Tenth Revision codes per subject.
    They return original data with injury categorizations and severity scores added.

Identify Cancer Dysfunctional Sub-pathway by integrating gene expression, DNA methylation and copy number variation, and pathway topological information. 1)We firstly calculate the gene risk scores by integrating three kinds of data: DNA methylation, copy number variation, and gene expression.  2)Secondly, we perform a greedy search algorithm to identify the key dysfunctional sub-pathways within the pathways for which the discriminative scores were locally maximal. 3)Finally, the permutation test was used to calculate statistical significance level for these key dysfunctional sub-pathways.

Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.

Provides user-friendly and configurable print debugging via a
    single function, ic(). Wrap an expression in ic() to print the
    expression, its value and (where available) its source location.
    Debugging output can be toggled globally without modifying code.

Estimate test-retest reliability for complex sampling strategies 
    and extract variances using IntraClass Effect Decomposition. Developed by
    Brandmaier et al. (2018) "Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)" <doi:10.7554/eLife.35718> 
    Also includes functions to simulate data based on sampling strategy. 
    Unofficial version release name: "Good work squirrels".

Given two unbiased samples of patient level data on cost and effectiveness
  for a pair of treatments, make head-to-head treatment comparisons by (i) generating the
  bivariate bootstrap resampling distribution of ICE uncertainty for a specified value of
  the shadow price of health, lambda, (ii) form the wedge-shaped ICE confidence region with
  specified confidence fraction within [0.50, 0.99] that is equivariant with respect to
  changes in lambda, (iii) color the bootstrap outcomes within the above confidence wedge
  with economic preferences from an ICE map with specified values of lambda, beta and gamma
  parameters, (iv) display VAGR and ALICE acceptability curves, and (v) illustrate variation
  in ICE preferences by displaying potentially non-linear indifference(iso-preference) curves
  from an ICE map with specified values of lambda, beta and either gamma or eta parameters.