Data from the United States Center for Medicare and Medicaid
    Services (CMS) is included in this package. There are ICD-9 and ICD-10
    diagnostic and procedure codes, and lists of the chapter and sub-chapter 
    headings and the ranges of ICD codes they encompass. There are also two 
    sample datasets. These data are used by the 'icd' package for finding
    comorbidities.

Provides an estimator for generalized linear models with incomplete
    data for discrete covariates. The estimation is based on the EM algorithm by the
    method of weights by Ibrahim (1990) <DOI:10.2307/2290013>.

Categorization and scoring of injury severity typically involves
    trained personnel with access to injured persons or their medical records. 'icdpicr' contains a function
    that provides automated calculation of Abbreviated Injury Scale ('AIS') and Injury Severity Score ('ISS')
    from International Classification of Diseases ('ICD') codes and may be a useful substitute to manual injury 
    severity scoring. 'ICDPIC' was originally developed in 'Stata', and 'icdpicr' is an open-access update 
    that accepts both 'ICD-9' and 'ICD-10' codes.

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.