Multi-state models are essential tools in longitudinal data analysis. 
             One primary goal of these models is the estimation of transition 
             probabilities, a critical metric for predicting clinical prognosis 
             across various stages of diseases or medical conditions. Traditionally, 
             inference in multi-state models relies on the Aalen-Johansen (AJ) 
             estimator which is consistent under the Markov assumption. However, 
             in many practical applications, the Markovian nature of the process
             is often not guaranteed, limiting the applicability of the AJ
             estimator in more complex scenarios. This package extends the landmark Aalen-Johansen
             estimator (Putter, H, Spitoni, C (2018) <doi:10.1177/0962280216674497>)
             incorporating presmoothing techniques described by Soutinho, 
             Meira-Machado and Oliveira (2020) <doi:10.1080/03610918.2020.1762895>, 
             offering a robust alternative for estimating transition probabilities
             in non-Markovian multi-state models with multiple states and potential
             reversible transitions.

Provides a function for estimating the transition probabilities in an illness-death model. 
            The transition probabilities can be estimated from the unsmoothed landmark estimators developed 
            by de Una-Alvarez and Meira-Machado (2015) <doi:10.1111/biom.12288>.
            Presmoothed estimates can also be obtained through the use of a parametric family of binary
            regression curves, such as logit, probit or cauchit. The additive logistic regression model 
            and nonparametric regression are also alternatives which have been implemented.
            The idea behind the presmoothed landmark estimators is to use the presmoothing techniques 
            developed by  Cao et al. (2005) <doi:10.1007/s00180-007-0076-6> in the 
            landmark estimation of the transition probabilities.