Regression modeling of percentages or normalized rates occurs in a variety of fields. Most long-standing techniques, however, are hampered by the presence of boundary values of zero or one. Boundary-inflated likelihood functions are one way of addressing this issue. However these approaches treat boundary values as arising independently from non-boundary values. We instead examine continuous regression modeling of proportions with boundary values using functionals of beta and triangular distributions.