The program implements the LPLS (linear programming through least squares) estimator with the help of the Moore-Penrose inverse (pseudoinverse), calculated using singular value decomposition (SVD), with emphasis on the estimation of OLS constrained in values (cOLS), Transaction Matrix (TM), and custom (user-defined) cases. The pseudoinverse offers a unique minimum-norm least-squares solution, which is the best linear unbiased estimator (BLUE); see Albert (1972, Chapter VI). (Over)determined problems are accompanied by regression analysis, which is feasible in their case. For such and especially all remaining cases, a Monte Carlo-based t-test of mean NRMSE (normalized by the standard deviation of the RHS) is performed, the sample being drawn from a uniform or user-provided distribution (via a Mata function).