Diethelm Wuertz provides a handy overview of the current state-of-the-art in portfolio optimization in R in a recent posting on the r-sig-finance mailing list.
Mean-Variance portfolio optimization with a quadratic objective and linear constraints using the "quadprog" solver from R for quadratic programming problems. Note, to this class of problems do not belong the problems of maximizing the return for a given risk, and also not problems with quadratic or nonlinear constraints like portfolios with covariance risk budgeting, or 130/30 portfolios. These portfolios are more complex and require solvers which can handle quadratic and/or nonlinear constraints.
Mean-CVaR portfolios which belong to the class of scenario optimization problems with a linear objective function and linear constraints. The default solver for this kind of portfolio is the solver from the package "Rglpk". Again you cannot solve more complex problems which add quadratic or non-linear constraints.