Random Forest-Based Multistate Survival Analysis // Fits cause- specific random survival forests for flexible multistate survival analysis with covariate-adjusted transition probabilities computed via product-integral. State transitions are modeled by random forests. Subject-specific transition probability matrices are assembled from predicted cumulative hazards using the product-integral formula. Also provides a standalone Aalen-Johansen nonparametric estimator as a covariate-free baseline. Supports arbitrary state spaces with any number of states (three or more) and any set of allowed transitions, applicable to clinical trials, disease progression, reliability engineering, and other domains where subjects move among discrete states over time. Provides per- transition feature importance, bias-variance diagnostics, and comprehensive visualizations. Handles right censoring and competing transitions. Methods are described in Ishwaran et al. (2008) doi:10.1214/08-AOAS169 for random survival forests, Putter et al. (2007) doi:10.1002/sim.2712 for multistate competing risks decomposition, and Aalen and Johansen (1978) https://www.jstor.org/stable/4615704 for the nonparametric estimator.