Chemometric Software supporting NSF Project Variable Selection for Remedying the Effects of Uncontrolled Variation in Data Driven Predictions
| Author(s) | Poerio, Dominic V. | |
| Author(s) | Kneale, Casey | |
| Author(s) | Brown, Steven D. | |
| Date Accessioned | 2019-09-14T00:10:20Z | |
| Date Available | 2019-09-14T00:10:20Z | |
| Publication Date | 2019-09-15 | |
| Description | Software is provided as a deliverable product for NSF project Variable Selection for Remedying the Effects of Uncontrolled Variation in Data Driven Predictions (NSF Grant Number 1506853). There are 8 separate software packages, each provided in its own folder. Each folder includes a README file and example data to permit testing. The packages implement work published in the following papers: | en_US |
| Description | D. Poerio and S.D. Brown, “Stacked Interval Sparse Partial Least Squares Regression Analysis,” Chemom. Intell. Lab Syst. 166, 2017, 49-60. (DOI: 10.1016/j.chemolab.2017.03.006) | |
| Description | D. Poerio and S.D. Brown, “Dual-Domain Calibration Transfer by Orthogonal Projection” , Appl. Spectrosc., 2018. (DOI: 10.1177/0003702817724164).Erratum to Dual-domain calibration transfer using orthogonal projection. Appl. Spectrosc. 2018, (DOI: 10.1177/0003702818768732) | |
| Description | D. Poerio and S.D. Brown, A Frequency-Localized Recursive Partial Least Squares Ensemble for Soft Sensing, J. Chemom. e2999, 2018. (DOI: 10.1002/cem.2999) | |
| Description | D. Poerio and S.D. Brown, “Highly-Overlapped, Recursive Partial Least Squares Soft Sensor with State Partitioning via Local Variable Selection”,Chemom. Intell. Lab Syst. 175 (2018) 104–115. (DOI: 10.1016/j.chemolab.2018.02.006) | |
| Description | C. Kneale and S.D. Brown, “Small Moving-Window Calibration Models for Soft Sensing Processes with Limited History.” Chemom. Intell. Lab. Syst.183, 2018, 36-46. (DOI: 10.1016/j.chemolab.2018.10.007) | |
| Description | C. Kneale and S.D. Brown, Band Target Entropy Minimization and Target Partial Least Squares for Spectral Recovery and Calibration, Analyt. Chim. Acta, 1031 (2018) 38-46. (DOI:10.1016/j.aca.2018.07.054) | |
| Description | D. Poerio and S.D. Brown, Localized and Adaptive Soft Sensor Based on an Extreme Learning Machine with Automated Self-correction Strategies, J. Chemom., 2018;e3088. (DOI: 10.1002/cem.3088) | |
| Description | C. Kneale and S.D. Brown, Exploratory Data Analysis using an Uncharted Forest, Talanta 189 (2018) 71–78. (DOI: 10.1016/j.talanta.2018.06.061) | |
| Sponsor | NSF Grant Number 1506853 | en_US |
| dc.format | R language source code, with some MATLAB source code and some MATLAB and CSV (comma-separated values) or TXT files containing example data. | |
| URL | http://udspace.udel.edu/handle/19716/24440 | |
| Publisher | Steven D. Brown | en_US |
| dc.relation.requires | R supports MacOS, Linux and Windows. This software should run in R installed on any of these operating systems, but we only run R on MacOS and Linux, and we did not test the code on Windows. | |
| dc.rights | Copyright 2019, University of Delaware, released under a GPL-2 license. All Rights Reserved. | |
| Keywords | Chemometrics software | en_US |
| Title | Chemometric Software supporting NSF Project Variable Selection for Remedying the Effects of Uncontrolled Variation in Data Driven Predictions | en_US |
| Type | Software | en_US |