User's reference guide for ODRPACK version 2.01:: software for weighted orthogonal distance regression
By P. Boggs, Richard H. Byrd, Janet E. Rogers, R. Schnabel. 1992
ODRPACK is a software package for weighted orthogonal distance regression, i.e., for finding the parameters that minimize the sum of the squared weighted orthogonal distances from a set of observations to the curve or surface determined by the parameters. It can also be used to solve the nonlinear ordinary least squares problem. The procedure has application to curve and surface fitting, and to measurement error models in statistics. ODRPACK can handle both explicit and implicit models, and will easily accommodate complex and other types of multiresponse data. The algorithm implemented is an efficient and stable trust region Levenberg-Marquardt procedure that exploits the structure of the problem so that the computational cost per iteration is equal to that for the same type of algorithm applied to the nonlinear ordinary least squares problem. The package allows a general weighting scheme, provides for finite difference derivatives, and contains extensive error checking and report generating facilities.
Subfields: Statistics and Probability, Numerical Methods & Scientific Computing
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Citations in the corpus
- SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python (2019, background): “The project’s benevolent dictator for life, Pauli Virtanen, has overruling authority on any matter, but is expected to act in good faith and only exercise this authority when the steering council cannot reach agreement.”
- The Birmingham-CfA cluster scaling Project – I. Gas fraction and the M-Tx relation (2003, methodology): “We fitted a straight line, in log space, to the points (both including and excluding the two galaxies) using the odrpack software package (Boggs et al. 1989, 1992), to take account of parameter errors in both the X and Y directions.”