  Software development innovations and advances in computing have enabled more
complex and less costly computations in medical research (survival analysis),
engineering studies (reliability analysis), and social sciences event analysis
(historical analysis). As a result, many semi-parametric modeling efforts
emerged when it comes to time-to-event data analysis. In this context, this
work presents a flexible Bernstein polynomial (BP) based framework for survival
data modeling. This innovative approach is applied to existing families of
models such as proportional hazards (PH), proportional odds (PO), and
accelerated failure time (AFT) models to estimate unknown baseline functions.
Along with this contribution, this work also presents new automated routines in
R, taking advantage of algorithms available in Stan. The proposed computation
routines are tested and explored through simulation studies based on artificial
datasets. The tools implemented to fit the proposed statistical models are
combined and organized in an R package. Also, the BP based proportional hazards
(BPPH), proportional odds (BPPO), and accelerated failure time (BPAFT) models
are illustrated in real applications related to cancer trial data using maximum
likelihood (ML) estimation and Markov chain Monte Carlo (MCMC) methods.
