One major goal in clinical applications of multi-state models is the estimation of transitionprobabilities. The usual nonparametric estimator of the transition matrix for nonhomogeneous Markov processes is the Aalen-Johansen estimator (Aalen and Johansen 1978). However, two problems may arise from using this estimator: first, its standard error may be large in heavy censored scenarios; second, the estimator may be inconsistent if the process is non-Markovian. The development of the R package TPmsm has been motivated by several recent contributions that account for these estimation problems. Estimation and statistical inference for transition probabilities can be performed using TPmsm. The TPmsm package provides seven di fferent approaches to three-state illness-death modeling. In two of these approaches the transition probabilities are estimated conditionally on current or past covariate measures. Two real data examples are included for illustration of software usage.