Analysis of High-Dimensional Categorical Data Such as SNP Data
Approximate Bayes Factor
Summarize MCMC sample of Bayesian logic regression models
Construct Annotation for Affymetrix SNP Chips
Cordell's Test for Epistatic Interactions
Pairwise Contingency Tables
Rowwise Contigency Tables
Full Bayesian Logic Regression for SNP Data
Generalized k Nearest Neighbors
Identification of Constant Variables
Missing Value Imputation with kNN
Missing Value Imputation with kNN for High-Dimensional Data
Prediction Analysis of Categorical Data
Pearson's Contingency Coefficient
Predict Method for pamCat Objects
Predict Case Probabilities with Full Bayesian Logic Regression
Recoding of Affymetrix SNP Values
Recoding of SNP Values
Rowwise Cochran-Armitage Trend Test Based on Tables
Rowwise Pearson's ChiSquare Test Based on Tables
Rowwise Pearson's ChiSquare Statistic
Rowwise Correlation with a Vector
Rowwise Frequencies
Rowwise Test for Hardy-Weinberg Equilibrium
Rowwise Minor Allele Frequency
Rowwise Linear Trend Test Based on Tables
Rowwise Scaling
Rowwise Tables
Trend Test for Fuzzy Genotype Calls
Rowwise Linear Trend Tests
Internal scrime functions
Shorten the Gene Description
Displaying Changes
Simulation of SNP Data with Categorical Response
Simulation of SNP data
Simulation of SNP data
Simple Matching Coefficient and Cohen's Kappa
Transformation of SNPs to Binary Variables
Summarizing a simSNPglm object
Tools for the analysis of high-dimensional data developed/implemented at the group "Statistical Complexity Reduction In Molecular Epidemiology" (SCRIME). Main focus is on SNP data. But most of the functions can also be applied to other types of categorical data.