1989
DOI: 10.1145/76909.76913
|View full text |Cite
|
Sign up to set email alerts
|

Algorithm 676: ODRPACK: software for weighted orthogonal distance regression

Abstract: In this paper, we describe ODRPACK, a software package for the weighted orthogonal distance regression problem. This software is an implementation of the algorithm described in [2] for finding the parameters that minimize the sum of the squared weighted orthogonal distances from a set of observations to a curve or surface determined by the parameters. It can also be used to solve the ordinary nonlinear least squares problem. The weighted orthogonal distance regression procedure application to curve and surface… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

3
243
0
1

Year Published

1997
1997
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 281 publications
(252 citation statements)
references
References 9 publications
3
243
0
1
Order By: Relevance
“…The uncertainties were calculated by weight orthogonal distance regression (ODR) using the ODRPAC suite of program [86]. The uncertainties were calculated by weight orthogonal distance regression (ODR) using the ODRPAC suite of program [86].…”
Section: Discussionmentioning
confidence: 99%
“…The uncertainties were calculated by weight orthogonal distance regression (ODR) using the ODRPAC suite of program [86]. The uncertainties were calculated by weight orthogonal distance regression (ODR) using the ODRPAC suite of program [86].…”
Section: Discussionmentioning
confidence: 99%
“…Internal calibration was performed using eq 2 after all calibration coefficients were fit using a LevenbergMarquardt algorithm [60] for each mass spectrum acquired during the LC separation. The frequencies of calibrant ions from adjacent scans were used with weight coefficients to provide higher statistical confidence.…”
Section: Capillary Lc-fticr Analysesmentioning
confidence: 99%
“…Eqs. (9)- (18) were solved using the ODRPACK package [22] with the ordinary least squares criterion. Re-parameterization was performed on k D and E a being the estimated parameters Ī² 1 = ln (k D ) āˆ’ E a /300R and Ī² 2 = E a /300R.…”
Section: Resultsmentioning
confidence: 99%