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circular statistics tool box  (MathWorks Inc)


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    MathWorks Inc circular statistics tool box
    Circular Statistics Tool Box, supplied by MathWorks 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/statistical+tool+box/bio_rxiv__2025__03__25__645291-566-23-21
    Average 90 stars, based on 1 article reviews
    circular statistics tool box - by Bioz Stars, 2026-11
    90/100 stars

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    other:

    Article Title: Chronic Electrical Stimulation Promotes the Excitability and Plasticity of ESC-derived Neurons following Glutamate-induced Inhibition In vitro
    Article Snippet: All statistical tests were performed using either SigmaPlot (Systat Software inc., CA) or MATLAB ® statistical tool box (MathWorks Inc., MA).

    Article Title: Bypassing stroke-damaged neural pathways via a neural interface induces targeted cortical adaptation
    Article Snippet: Statistical analysis was performed with MATLAB (The MathWorks Inc., MA, USA) statistical tool box.

    Article Title: Dasatinib response in acute myeloid leukemia is correlated with FLT3 /ITD, PTPN11 mutations and a unique gene expression signature
    Article Snippet: This model was validated on the Israel National Center for Personalized Medicine (INCPM) gene expression data (Matlab statistical tool box).

    Article Title: Chronic Electrical Stimulation Promotes the Excitability and Plasticity of ESC-derived Neurons following Glutamate-induced Inhibition In vitro
    Article Snippet: All statistical tests were performed using either SigmaPlot (Systat Software inc., CA) or MATLAB ® statistical tool box (MathWorks Inc., MA).

    Article Title: Primary study of identification of parathyroid gland based on laser-induced breakdown spectroscopy
    Article Snippet: In this work, LDA was performed using the statistical tool box in Matlab R2019b (Mathworks, USA).

    Article Title: Pre-Clinical Testing of Microwave Radiometer and a Pilot Study on the Screening Inflammation of Knee Joints.
    Article Snippet: This article presents the pre‐clinical evaluation of our custom‐built, single‐band microwave radiometer centered at 1.3 GHz for deep tissue thermometry, and a pilot study on volunteers for passive detection of inflammation in knee joints.. The electromagnetic (EM) compatibility of the battery‐operated radiometer for clinical use was assessed as per International Special Committee on Radio Interference (CISPR) 22 standard.. The ability to detect inflammation in knee joints was assessed using a substrate integrated waveguide antenna connected to the radiometer.



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    Voltage U—RMSE: D-PNN = 1.153, SMLT (Tree) = 1.118 [V], 12 inputs + THDU (both D-PNN and SMLT) in the 2nd stage.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Voltage U—RMSE: D-PNN = 1.153, SMLT (Tree) = 1.118 [V], 12 inputs + THDU (both D-PNN and SMLT) in the 2nd stage.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Power P—RMSE: D-PNN = 0.177, SMLT (GPR) = 0.182 [kW], 12 inputs + U + PF (D-PNN), 12 inputs + PF + THDU (SMLT) in the 3rd stage.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Power P—RMSE: D-PNN = 0.177, SMLT (GPR) = 0.182 [kW], 12 inputs + U + PF (D-PNN), 12 inputs + PF + THDU (SMLT) in the 3rd stage.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    THDU—RMSE: D-PNN = 0.429, SMLT (Tree) = 0.443 [%], 12 inputs + U (both D-PNN and SMLT) in the 2nd stage.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: THDU—RMSE: D-PNN = 0.429, SMLT (Tree) = 0.443 [%], 12 inputs + U (both D-PNN and SMLT) in the 2nd stage.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Power Factor PF—RMSE: D-PNN = 0.0516, SMLT (GPR) = 0.0496 [], 12 inputs + Pst + U (D-PNN, 12 inputs + Pst + THDU (SMLT) in the 3rd stage.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Power Factor PF—RMSE: D-PNN = 0.0516, SMLT (GPR) = 0.0496 [], 12 inputs + Pst + U (D-PNN, 12 inputs + Pst + THDU (SMLT) in the 3rd stage.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Pst—RMSE: D-PNN = 0.811, SMLT (EBoosT) = 0.544 [], 12 inputs + Pst (both D-PNN and SMLT) in the 2nd stage.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Pst—RMSE: D-PNN = 0.811, SMLT (EBoosT) = 0.544 [], 12 inputs + Pst (both D-PNN and SMLT) in the 2nd stage.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Voltage U 3-step model estimations.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Voltage U 3-step model estimations.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Power P 3-step model estimations.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Power P 3-step model estimations.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    THDU 3-step model estimations.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: THDU 3-step model estimations.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Power Factor PF 3-step model estimations.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Power Factor PF 3-step model estimations.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques:

    Pst 3-step model estimations.

    Journal: Scientific Reports

    Article Title: Power quality approximation for household equipment load combinations using a stepwise growth in input parameters of AI models

    doi: 10.1038/s41598-022-21812-1

    Figure Lengend Snippet: Pst 3-step model estimations.

    Article Snippet: [A] Matlab—Statistics and Machine Learning tool-box for regression (SMLT) www.mathworks.com/help/stats/choose-regression-model-options.html . https://mathworks.com/help/stats/select-data-and-validation-for-regression-problem.html .

    Techniques: