This report describes an R package, called the Individualized Coherent Absolute Risk Estimation (iCARE) tool, which allows researchers to quickly build models for absolute risk, and apply them to estimate an individual's risk of developing disease during a specified time interval, based on a set of user defined input parameters. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge of risk factors and tailor models to different populations. The tool requires three input arguments be specified: (1) a model for relative risk (2) an age-specific disease incidence rate and (3) the distribution of risk factors for the population of interest. The tool handles missing risk factor information for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. We discuss the statistical framework, handling of missing data and genetic factors, and provide real data examples that demonstrate the utility of iCARE for building and applying absolute risk models, using breast cancer as an example.