  This paper introduces the R package INLAjoint, designed as a toolbox for
fitting a diverse range of regression models addressing both longitudinal and
survival outcomes. INLAjoint relies on the computational efficiency of the
integrated nested Laplace approximations methodology, an efficient alternative
to Markov chain Monte Carlo for Bayesian inference, ensuring both speed and
accuracy in parameter estimation and uncertainty quantification. The package
facilitates the construction of complex joint models by treating individual
regression models as building blocks, which can be assembled to address
specific research questions. Joint models are relevant in biomedical studies
where the collection of longitudinal markers alongside censored survival times
is common. They have gained significant interest in recent literature,
demonstrating the ability to rectify biases present in separate modeling
approaches such as informative censoring by a survival event or confusion bias
due to population heterogeneity. We provide a comprehensive overview of the
joint modeling framework embedded in INLAjoint with illustrative examples.
Through these examples, we demonstrate the practical utility of INLAjoint in
handling complex data scenarios encountered in biomedical research.
