In retrospective studies and prospective observational studies, the groups being compared may be imbalanced with regards to the prognostic factors that are significantly associated with the time-to-event outcomes, and thus the unadjusted Kaplan-Meier (KM) function and the cumulative incidence function (CIF) may be incompatible with the results from the multivariable model. To our knowledge, no R package is available for creating the stratified model-based, covariate-adjusted KM or CIF functions. To address these concerns, the covariate-adjusted KM functions and CIFs can be estimated by the multivariable Cox and Fine-Gray regression model, depending on the presence of the competing risks. The Direct Adjustment method is used to create the covariate-adjusted KM functions and CIFs. The Gail and Byar method and the Storer method are offered for the application of the stratified Cox model and the stratified Fine-Gray regression model. In addition, the bootstrap percentile method is used for computing the pointwise confidence intervals. The R package AdjKM.CIF and shiny application for all practitioners have been developed to create the covariate-adjusted KM functions and CIFs.