  The creation of optimal samplers can be a challenging task, especially in the
presence of constraints on the support of parameters. One way of mitigating the
severity of this challenge is to work with transformed variables, where the
support is more conducive to sampling. In this work, a particular
transformation called inversion in a sphere is embedded within the popular
Metropolis-Hastings paradigm to effectively sample in such scenarios. The
method is illustrated on three domains: the standard simplex (sum-to-one
constraint), a sector of an $n$-sphere, and hypercubes. The method's
performance is assessed using simulation studies with comparisons to strategies
from existing literature.
