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partial least square algorithm smartpls 3  (SmartPLS GmbH)

 
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    SmartPLS GmbH partial least square algorithm smartpls 3
    Partial Least Square Algorithm Smartpls 3, supplied by SmartPLS GmbH, 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/partial+least-squares+algorithm/smartpls+software/pmc10828007-63-9-9
    Average 90 stars, based on 1 article reviews
    partial least square algorithm smartpls 3 - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: How perceived eWOM in visual form influences online purchase intention on social media: A research based on the SOR theory.
    Article Snippet: The research employed Partial Least Squares Structural Equation Modeling (PLS-SEM) for data analysis, using SmartPLS software.

    Article Title: Ethical dilemmas and the reconstruction of subjectivity in digital mourning in the age of AI: an empirical study on the acceptance intentions of bereaved family members of cancer patients
    Article Snippet: This study employed the Partial Least Squares Algorithm function in SmartPLS 3.27 to evaluate the reliability and validity of each latent construct.

    Article Title: Parenting, psychological insecurity, and mental health: Understanding the effects of adverse childhood experiences in adolescents.
    Article Snippet: Background: Adolescence is a critical developmental period during which individuals undergo significant cognitive, social, and emotional changes.. Although extensive research shows that parenting styles can either promote or hinder adolescent mental health, relatively few studies have examined how adverse childhood experiences (ACEs) and psychological insecurity jointly mediate these effects in non-Western contexts.. Objective: This study aimed to investigate how authoritative, authoritarian, and permissive parenting styles influence adolescent psychological well-being (PWB), with a specific focus on the mediating roles of ACEs and psychological insecurity among South African high school students.

    Article Title: Understanding the psychological drivers of online self-presentation: a survey study on social media exposure, social comparison, social network type and FOMO.
    Article Snippet: Structural equation modelling using SmartPLS v4 was employed to analyse the data.


    Article Title: Spatiotemporal evolution and driving factors of eco-environmental quality in the Beijing-Tianjin-Hebei urban agglomeration in China
    Article Snippet: The figure is generated using SmartPLS 3.2.9 ( https://www.smartpls.com/ ).

    Article Title: Spatiotemporal evolution and driving factors of eco-environmental quality in the Beijing-Tianjin-Hebei urban agglomeration in China.
    Article Snippet: The figure is generated using SmartPLS 3.2.9 (https://www.smartpls.com/).



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    Validation of predictive models for PDB severity scores. Orthogonal Partial Least Square (OPLS) classifier was trained on the discover set and tested on the cross-validation set. The features consisted of the list of significant sites from site and region level analysis based on the discovery samples (Panels A and C, n = 2,247 CpG sites) and separately, on a best explanatory subset from this pool of sites obtained with the Glmnet (Panels B and D, n = 33 CpG sites) (A,B) Scatter plots of predicted versus observed PDB_severity scores for each model. The observed PDB severity scores were also dichotomized into two groups based on the median PDB severity score of seven; low severity (PDB_severity_score <7; blue) and high severity (PDB_severity_score ≥7; red) and subsequently used for ROC curve analysis showing the area under curve AUC for pooled sites (C) and Glmnet selected sites (D) .

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    Figure Lengend Snippet: Validation of predictive models for PDB severity scores. Orthogonal Partial Least Square (OPLS) classifier was trained on the discover set and tested on the cross-validation set. The features consisted of the list of significant sites from site and region level analysis based on the discovery samples (Panels A and C, n = 2,247 CpG sites) and separately, on a best explanatory subset from this pool of sites obtained with the Glmnet (Panels B and D, n = 33 CpG sites) (A,B) Scatter plots of predicted versus observed PDB_severity scores for each model. The observed PDB severity scores were also dichotomized into two groups based on the median PDB severity score of seven; low severity (PDB_severity_score <7; blue) and high severity (PDB_severity_score ≥7; red) and subsequently used for ROC curve analysis showing the area under curve AUC for pooled sites (C) and Glmnet selected sites (D) .

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