The compatibility of theoretical frameworks with machine learning analyses in psychological research.

[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 .