pls_toolbox statistical multivariate analysis library Search Results


pls toolbox version 7 9 2  (MathWorks Inc)
96
MathWorks Inc pls toolbox version 7 9 2
Pls Toolbox Version 7 9 2, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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pls toolbox version 9 5  (Eigenvector Research Inc)
86
Eigenvector Research Inc pls toolbox version 9 5
Pls Toolbox Version 9 5, supplied by Eigenvector Research Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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pls toolbox version 9 5 - by Bioz Stars, 2026-09
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paket perangkat lunak nir memungkinkan data spektral untuk dipindahkan ke perangkat lunak analisis data kemometrik atau analisis multivariat  (MathWorks Inc)
96
MathWorks Inc paket perangkat lunak nir memungkinkan data spektral untuk dipindahkan ke perangkat lunak analisis data kemometrik atau analisis multivariat
Paket Perangkat Lunak Nir Memungkinkan Data Spektral Untuk Dipindahkan Ke Perangkat Lunak Analisis Data Kemometrik Atau Analisis Multivariat, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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paket perangkat lunak nir memungkinkan data spektral untuk dipindahkan ke perangkat lunak analisis data kemometrik atau analisis multivariat - by Bioz Stars, 2026-09
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multivariate calibration models  (MathWorks Inc)
94
MathWorks Inc multivariate calibration models
Multivariate Calibration Models, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 94/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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multivariate calibration models - by Bioz Stars, 2026-09
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pls toolbox solo  (MathWorks Inc)
96
MathWorks Inc pls toolbox solo
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pls toolbox solo - by Bioz Stars, 2026-09
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pls toolbox  (Baycrest Technology Pty Ltd)
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Baycrest Technology Pty Ltd pls toolbox
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Pls Toolbox, supplied by Baycrest Technology Pty Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/pls_toolbox+statistical+multivariate+analysis+library/pls+toolbox/pmc10570707-84-25-28
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pls toolbox - by Bioz Stars, 2026-09
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version 9 6  (MathWorks Inc)
96
MathWorks Inc version 9 6
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Version 9 6, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/pls_toolbox+statistical+multivariate+analysis+library/Statistics+and+Machine+Learning+Toolbox/pmc09349415-74-22-31
Average 96 stars, based on 1 article reviews
version 9 6 - by Bioz Stars, 2026-09
96/100 stars
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pirouettetm multivariate data analysis for windows  (Infometrix Inc)
90
Infometrix Inc pirouettetm multivariate data analysis for windows
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Pirouettetm Multivariate Data Analysis For Windows, supplied by Infometrix Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/pls_toolbox+statistical+multivariate+analysis+library/pirouette+multivariate+data+analysis+for+windows+95+98+and+nt+version+2+6/us07106437-199-17-20
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pirouettetm multivariate data analysis for windows - by Bioz Stars, 2026-09
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pirouettetm multivariate data analysis for windows software  (Infometrix Inc)
90
Infometrix Inc pirouettetm multivariate data analysis for windows software
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Pirouettetm Multivariate Data Analysis For Windows Software, supplied by Infometrix Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/pls_toolbox+statistical+multivariate+analysis+library/pirouette++multivariate+analysis+software/us09389161-182-17-20
Average 90 stars, based on 1 article reviews
pirouettetm multivariate data analysis for windows software - by Bioz Stars, 2026-09
90/100 stars
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deep learning toolbox 14 0  (MathWorks Inc)
96
MathWorks Inc deep learning toolbox 14 0
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Deep Learning Toolbox 14 0, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/pls_toolbox+statistical+multivariate+analysis+library/Deep+Learning+Toolbox/pm41839233-91-22-27
Average 96 stars, based on 1 article reviews
deep learning toolbox 14 0 - by Bioz Stars, 2026-09
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classification toolbox for matlab  (MathWorks Inc)
96
MathWorks Inc classification toolbox for matlab
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Classification Toolbox For Matlab, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/pls_toolbox+statistical+multivariate+analysis+library/Instrument+Control+Toolbox/pmc12878783-135-32-35
Average 96 stars, based on 1 article reviews
classification toolbox for matlab - by Bioz Stars, 2026-09
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unscrambler 10.2  (CAMO Software)
90
CAMO Software unscrambler 10.2
Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), <t>and</t> <t>PLSC</t> to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, <t>partial</t> <t>least</t> <t>squares</t> correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.
Unscrambler 10.2, supplied by CAMO Software, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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unscrambler 10.2 - by Bioz Stars, 2026-09
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Image Search Results


Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), and PLSC to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, partial least squares correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.

Journal: Neurobiology of Stress

Article Title: Pre-COVID brain network topology prospectively predicts social anxiety alterations during the COVID-19 pandemic

doi: 10.1016/j.ynstr.2023.100578

Figure Lengend Snippet: Workflow of the study. (A) Timeline of data acquisition. Before the COVID-19 pandemic (T1: October 2019 to January 2020), participants underwent brain MRI scanning and completed baseline behavioral measures. During the most severe pandemic period (T2: February 2020 to April 2020), participants were re-contacted for follow-up behavioral testing. 110 subjects were identified as eligible for the study. (B) Construction of 264 × 264 functional connectivity matrix for each subject. (C) Topological graph theory and statistical analyses. We computed both global and nodal metrics for each individual to describe the characteristics of each weighted network, and for each network metric we used the AUC over a range of network sparsity thresholds (0.02: 0.01: 0.33) in subsequent statistical analyses. We used partial correlation to investigate the association between each global metric and SA alterations (T2-T1), and PLSC to determine the degree centrality pattern of nodes linked to SA alterations. Abbreviations: AUC, area under the curve; COVID-19, coronavirus disease 2019; LSAS, Liebowitz Social Anxiety Scale; MRI, magnetic resonance imaging; PLSC, partial least squares correlation; SA, social anxiety; SRLEC, Self-Rating Life Events Checklist; SSS, Socioeconomic Status Scale; SVD, singular value decomposition; TAI, Trait Anxiety Inventory; C p , clustering coefficient; E glob , global efficiency; E loc , local efficiency; L p , shortest path length; γ , normalized clustering coefficient; λ , normalized shortest path length; σ , small-worldness.

Article Snippet: To evaluate multivariate patterns of correlation between the nodal-level topological property (degree centrality) and SA alterations across subjects, we used PLSC via the publicly available PLS toolbox ( https://www.rotman-baycrest.on.ca/index.php?section=84 ) in MATLAB R2018b (MathWorks, Natick, USA).

Techniques: Functional Assay, Magnetic Resonance Imaging