MOTIVATION: Various computational biology calculations require a probabilistic optimisation protocol to determine the parameters that capture the system at a desired state in the configurational space. Many existing methods excel at certain scenarios, but fail in others due, in part, to an inefficient exploration of the parameter space and easy trapping into local minima. Here, we developed a general-purpose optimisation engine in R that can be plugged to any, simple or complex, modelling initiative through a few lucid interfacing functions, to perform a seamless optimisation with rigorous parameter sampling. RESULTS: ROptimus features simulated annealing and replica exchange implementations equipped with adaptive thermoregulation to drive Monte Carlo optimisation process in a flexible manner, through constrained acceptance frequency but unconstrained adaptive pseudo temperature regimens. We exemplify the applicability of our R optimiser to a diverse set of problems spanning data analyses and computational biology tasks. AVAILABILITY AND IMPLEMENTATION: ROptimus is written and implemented in R, and is freely available from CRAN (http://cran.r-project.org/web/packages/ROptimus/index.html), and GitHub (http://github.com/SahakyanLab/ROptimus). SUPPLEMENTARY INFORMATION: Supplementary information with more details, tutorials, and developer instructions is available at Bioinformatics online.