The compatibility of theoretical frameworks with machine learning analyses in psychological research.
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[1] J. Elhai,et al. Editorial overview: Cyberpsychology: reviews of research on the intersection between computer technology use and human behavior. , 2020, Current opinion in psychology.
[2] S. D’Alfonso,et al. AI in mental health. , 2020, Current opinion in psychology.
[3] Michael A Proschan,et al. A primer on strong vs weak control of familywise error rate , 2020, Statistics in medicine.
[4] Jaime Delgadillo,et al. Targeted prescription of cognitive-behavioral therapy versus person-centered counseling for depression using a machine learning approach. , 2020, Journal of consulting and clinical psychology.
[5] C. Montag,et al. Using machine learning to model problematic smartphone use severity: The significant role of fear of missing out. , 2019, Addictive behaviors.
[6] Mu-Yen Chen,et al. Modeling public mood and emotion: Stock market trend prediction with anticipatory computing approach , 2019, Comput. Hum. Behav..
[7] B. M. Kibria,et al. Comparative Study of LASSO, Ridge Regression, Preliminary Test and Stein-type Estimators for the Sparse Gaussian Regression Model , 2019 .
[8] Lirong Wang,et al. Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder. , 2019, Drug and alcohol dependence.
[9] Stephan Lewandowsky,et al. Addressing the theory crisis in psychology , 2019, Psychonomic Bulletin & Review.
[10] Souleiman Hasan,et al. Feeling anxious? Perceiving anxiety in tweets using machine learning , 2019, Comput. Hum. Behav..
[11] T. Robbins,et al. The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors , 2019, Neuroscience & Biobehavioral Reviews.
[12] Jennifer L Tackett,et al. Psychology's Replication Crisis and Clinical Psychological Science. , 2018, Annual review of clinical psychology.
[13] T. Wager,et al. Improving Practices for Selecting a Subset of Important Predictors in Psychology: An Application to Predicting Pain , 2019, Advances in methods and practices in psychological science.
[14] Adrian B. R. Shatte,et al. Machine learning in mental health: a scoping review of methods and applications , 2019, Psychological Medicine.
[15] J. Henrich,et al. A problem in theory , 2019, Nature Human Behaviour.
[16] R. Alarcón,et al. Current Practices in Data Analysis Procedures in Psychology: What Has Changed? , 2018, Front. Psychol..
[17] Colin G. Walsh,et al. Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning , 2018, Journal of child psychology and psychiatry, and allied disciplines.
[18] D. Leightley,et al. Identifying probable post-traumatic stress disorder: applying supervised machine learning to data from a UK military cohort , 2018, Journal of Mental Health.
[19] G. Gong,et al. The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features , 2018, NeuroImage.
[20] Shulin Wang,et al. Feature selection in machine learning: A new perspective , 2018, Neurocomputing.
[21] Sigal Zilcha-Mano,et al. A Machine Learning Approach to Identifying Placebo Responders in Late-Life Depression Trials. , 2018, The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry.
[22] P. Falkai,et al. Machine Learning Approaches for Clinical Psychology and Psychiatry. , 2018, Annual review of clinical psychology.
[23] I. Kohane,et al. Big Data and Machine Learning in Health Care. , 2018, JAMA.
[24] Franco Turini,et al. A Survey of Methods for Explaining Black Box Models , 2018, ACM Comput. Surv..
[25] A. Serretti,et al. Pleiotropic genes in psychiatry: Calcium channels and the stress-related FKBP5 gene in antidepressant resistance , 2018, Progress in Neuro-Psychopharmacology and Biological Psychiatry.
[26] M. Kosinski,et al. Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation From Facial Images , 2018, Journal of personality and social psychology.
[27] J. Rodgers,et al. Psychology, Science, and Knowledge Construction: Broadening Perspectives from the Replication Crisis , 2018, Annual review of psychology.
[28] Joyce Lok Yin Kwan,et al. Variable system: An alternative approach for the analysis of mediated moderation. , 2017, Psychological methods.
[29] Ken Kelley,et al. A Novel Measure of Effect Size for Mediation Analysis , 2017, Psychological methods.
[30] Giorgos Borboudakis,et al. Bootstrapping the out-of-sample predictions for efficient and accurate cross-validation , 2017, Machine Learning.
[31] Sarfaraz Serang,et al. Exploratory Mediation Analysis via Regularization , 2017, Structural equation modeling : a multidisciplinary journal.
[32] M. Brand,et al. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: An Interaction of Person-Affect-Cognition-Execution (I-PACE) model , 2016, Neuroscience & Biobehavioral Reviews.
[33] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[34] Max Kuhn,et al. caret: Classification and Regression Training , 2015 .
[35] Cesar H. Comin,et al. A Systematic Comparison of Supervised Classifiers , 2013, PloS one.
[36] Max Kuhn,et al. Applied Predictive Modeling , 2013 .
[37] Han L. J. van der Maas,et al. Science Perspectives on Psychological an Agenda for Purely Confirmatory Research on Behalf Of: Association for Psychological Science , 2022 .
[38] N. Nathani,et al. Foundations of Machine Learning , 2021, Introduction to AI Techniques for Renewable Energy Systems.
[39] H. Zou,et al. Regularization and variable selection via the elastic net , 2005 .
[40] Ethem Alpaydin,et al. Introduction to machine learning , 2004, Adaptive computation and machine learning.
[41] J. Elhai,et al. Studying Psychopathology in Relation to Smartphone Use , 2019, Studies in Neuroscience, Psychology and Behavioral Economics.
[42] E. Horváth-Puhó,et al. Prediction of Sex-Specific Suicide Risk Using Machine Learning and Single-Payer Health Care Registry Data From Denmark. , 2019, JAMA psychiatry.
[43] Ranjan Duara,et al. A Clinically-Translatable Machine Learning Algorithm for the Prediction of Alzheimer's Disease Conversion in Individuals with Mild and Premild Cognitive Impairment. , 2018, Journal of Alzheimer's disease : JAD.
[44] Flavio Sanson Fogliatto,et al. Variable selection methods in multivariate statistical process control: A systematic literature review , 2018, Comput. Ind. Eng..
[45] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[46] M. Kuhn. The caret Package , 2007 .