R: Integrating Multiple Modalities of High Throughput Assays Using Item Response Theory

# Integrating Multiple Modalities of High Throughput Assays Using Item Response Theory

## Documentation for package ‘integIRTy’ version 1.0.8

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

calculatePermutedScoreByGeneSampling | Calculate the permuted latent trait by gene sampling
CN_N | Ovarian Cancer Datasets
CN_T | Ovarian Cancer Datasets
computeAbility | Calculate latent traits for a given response matrix and item parameters using MLE
dichotomize | A wrapper that is able to dichotomize expression, methylation and CN data
dichotomizeCN | Dichotomizing copy number data based on segmented data (i.e. log2ratio).
dichotomizeExpr | Dichotomize the expression data given both tumor and normal samples.
dichotomizeMethy | Dichotomize the methylation data given both tumor and normal controls.
Expr_N | Ovarian Cancer Datasets
Expr_T | Ovarian Cancer Datasets
fitOnSinglePlat | Fit IRT model on a single platform
intIRTeasyRun | The easyrun function for integrating multiple modalities of high throughput assays using binary input matrix.
intIRTeasyRunFromRaw | The easyrun function for integrating multiple modalities of high throughput assays using raw data.
Methy_N | Ovarian Cancer Datasets
Methy_T | Ovarian Cancer Datasets
OV | Ovarian Cancer Datasets
simulateBinaryResponseMat | Simulate binary response matrix according to 2-parameter Item Characteristic Function for given latent traits and item parameters.