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Building Predictive Models in R Using the caret Package
Abstract
The caret package, short for classification and regression training, contains numerous tools for developing predictive models using the rich set of models available in R. The package focuses on simplifying model training and tuning across a wide variety of modeling techniques. It also includes methods for pre-processing training data, calculating variable importance, and model visualizations. An example from computational chemistry is used to illustrate the functionality on a real data set and to benchmark the benefits of parallel processing with several types of models.
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The random forest-based transcription clock was trained using random forest regression with tenfold cross validation, three repeats, and a tuneLength of 5.
“The random forest-based transcription clock was trained on the batch corrected aging reference data set using the caret R package (Kuhn, 2008) and random forest regression with tenfold cross validation, three repeats and a ‘tuneLength’ of 5.”
From Multi-omic rejuvenation of human cells by maturation phase transient reprogramming (2022) · cites this paper as Kuhn, 2008
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Unbiased Recursive Partitioning: A Conditional Inference Framework
Hothorn, T., Hornik, K., & Zeileis, A. (2006) · Journal of Computational and Graphical Statistics · doi:10.1198/106186006x133933 · Cited by 4,021
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Feature Selection with the Boruta Package
Kursa, M. B., & Rudnicki, W. R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i11 · Cited by 5,659
SoilGrids250m: Global gridded soil information based on machine learning
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B. M., Ruiperez Gonzalez, M., Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., Bauer-Marschallinger, B., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0169748 · Cited by 4,733
McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521
A communal catalogue reveals Earth's multiscale microbial diversity
Thompson, L. R., Sanders, J. G., McDonald, D., Amir, A., Ladau, J., Locey, K. J., Prill, R. J., Tripathi, A., Gibbons, S. M., Ackermann, G., et al. (2017) · Nature · doi:10.1038/nature24621 · Cited by 2,684
Phylogenomics resolves the timing and pattern of insect evolution
Misof, B., Liu, S., Meusemann, K., Peters, R. S., Donath, A., Mayer, C., Frandsen, P. B., Ware, J., Flouri, T., Beutel, R. G., et al. (2014) · Science · doi:10.1126/science.1257570 · Cited by 2,694
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597
Microbial co-occurrence relationships in the human microbiome
Faust, K., Sathirapongsasuti, J. F., Izard, J., Segata, N., Gevers, D., Raes, J., & Huttenhower, C. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002606 · Cited by 1,527
Multiple early factors anticipate post-acute COVID-19 sequelae
Su, Y., Yuan, D., Chen, D. G., Ng, R. H., Wang, K., Choi, J., Li, S., Hong, S., Zhang, R., Xie, J., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.014 · Cited by 1,156
RETRACTED ARTICLE: Microbiome analyses of blood and tissues suggest cancer diagnostic approach
Poore, G. D., Kopylova, E., Zhu, Q., Carpenter, C., Fraraccio, S., Wandro, S., Kosciolek, T., Janssen, S., Metcalf, J., Song, S. J., et al. (2020) · Nature · doi:10.1038/s41586-020-2095-1 · Cited by 989
RetractedQuantitative Proteomics of the Cancer Cell Line Encyclopedia
Nusinow, D. P., Szpyt, J., Ghandi, M., Rose, C. M., McDonald, E. R., Kalocsay, M., Jané-Valbuena, J., Gelfand, E., Schweppe, D. K., Jedrychowski, M., et al. (2020) · Cell · doi:10.1016/j.cell.2019.12.023 · Cited by 957
Sensitive tumour detection and classification using plasma cell-free DNA methylomes
Shen, S. Y., Singhania, R., Fehringer, G., Chakravarthy, A., Roehrl, M. H. A., Chadwick, D., Zuzarte, P. C., Borgida, A., Wang, T. T., Li, T., et al. (2018) · Nature · doi:10.1038/s41586-018-0703-0 · Cited by 983
Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease
Kinchen, J., Chen, H. H., Parikh, K., Antanaviciute, A., Jagielowicz, M., Fawkner-Corbett, D., Ashley, N., Cubitt, L., Mellado-Gomez, E., Attar, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.08.067 · Cited by 745
Cross-trial prediction of treatment outcome in depression: a machine learning approach
Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00471-x · Cited by 676
Yin, J., Liao, S., He, Y., Wang, S., Xia, G., Liu, F., Zhu, J., You, C., Chen, Q., Zhou, L., et al. (2015) · Journal of the American Heart Association · doi:10.1161/jaha.115.002699 · Cited by 669
Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study
Hosny, A., Parmar, C., Coroller, T. P., Grossmann, P., Zeleznik, R., Kumar, A., Bussink, J., Gillies, R. J., Mak, R. H., & Aerts, H. J. W. L. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002711 · Cited by 579
An Empirical Comparison of Model Validation Techniques for Defect Prediction Models
Tantithamthavorn, C., McIntosh, S., Hassan, A. E., & Matsumoto, K. (2017) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2016.2584050 · Cited by 574
Pan-cancer analyses reveal cancer-type-specific fungal ecologies and bacteriome interactions
Narunsky-Haziza, L., Sepich-Poore, G. D., Livyatan, I., Asraf, O., Martino, C., Nejman, D., Gavert, N., Stajich, J. E., Amit, G., González, A., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.005 · Cited by 538
Applications of machine learning in animal behaviour studies
Valletta, J. J., Torney, C., Kings, M., Thornton, A., & Madden, J. (2017) · Animal Behaviour · doi:10.1016/j.anbehav.2016.12.005 · Cited by 505
“The caret package in R (Kuhn, 2008) and scikit-learn module in Python (Pedregosa et al., 2011) provide a common interface to a wide range of ML algorithms, together with numerous auxiliary functions to perform visualization, cross-validation, model evaluation and more.”
Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin
Whalen, S., Truty, R. M., & Pollard, K. S. (2016) · Nature Genetics · doi:10.1038/ng.3539 · Cited by 456
Predicting the conservation status of data-deficient species
Bland, L. M., Collen, B., Orme, C. D. L., & Bielby, J. (2015) · Conservation Biology · doi:10.1111/cobi.12372 · Cited by 364
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
Ahern, D. J., Ai, Z., Ainsworth, M., Allan, C., Allcock, A., Angus, B., Ansari, M. A., Arancibia-Cárcamo, C. V., Aschenbrenner, D., Attar, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.012 · Cited by 328
Impact of a deep learning assistant on the histopathologic classification of liver cancer
Kiani, A., Uyumazturk, B., Rajpurkar, P., Wang, A., Gao, R., Jones, E., Yu, Y., Langlotz, C. P., Ball, R. L., Montine, T. J., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0232-8 · Cited by 325
Why Cohen’s Kappa should be avoided as performance measure in classification
Delgado, R., & Tibau, X.-A. (2019) · PLOS ONE · doi:10.1371/journal.pone.0222916 · Cited by 324
Predicting hospital admission at emergency department triage using machine learning
Hong, W. S., Haimovich, A. D., & Taylor, R. A. (2018) · PLOS ONE · doi:10.1371/journal.pone.0201016 · Cited by 326
Effect of predicted protein-truncating genetic variants on the human transcriptome
Rivas, M. A., Pirinen, M., Conrad, D. F., Lek, M., Tsang, E. K., Karczewski, K. J., Maller, J. B., Kukurba, K. R., DeLuca, D. S., Fromer, M., et al. (2015) · Science · doi:10.1126/science.1261877 · Cited by 313
Climate change rapidly warms and acidifies Australian estuaries
Scanes, E., Scanes, P. R., & Ross, P. M. (2020) · Nature Communications · doi:10.1038/s41467-020-15550-z · Cited by 292
Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., Comer, J. S., Brewer, L. C., Frazier, S. L., & Chaspari, T. (2023) · Perspectives on Psychological Science · doi:10.1177/17456916221134490 · Cited by 263
Illusory generalizability of clinical prediction models
Chekroud, A. M., Hawrilenko, M., Loho, H., Bondar, J., Gueorguieva, R., Hasan, A., Kambeitz, J., Corlett, P. R., Koutsouleris, N., Krumholz, H. M., et al. (2024) · Science · doi:10.1126/science.adg8538 · Cited by 251
Shung, D. L., Au, B., Taylor, R. A., Tay, J. K., Laursen, S. B., Stanley, A. J., Dalton, H. R., Ngu, J., Schultz, M., & Laine, L. (2020) · Gastroenterology · doi:10.1053/j.gastro.2019.09.009 · Cited by 227
Walerych, D., Lisek, K., Sommaggio, R., Piazza, S., Ciani, Y., Dalla, E., Rajkowska, K., Gaweda-Walerych, K., Ingallina, E., Tonelli, C., et al. (2016) · Nature Cell Biology · doi:10.1038/ncb3380 · Cited by 239
Decoding molecular and cellular heterogeneity of mouse nucleus accumbens
Chen, R., Blosser, T. R., Djekidel, M. N., Hao, J., Bhattacherjee, A., Chen, W., Tuesta, L. M., Zhuang, X., & Zhang, Y. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00938-x · Cited by 191
Improving propensity score estimators’ robustness to model misspecification using super learner.
Pirracchio, R., Petersen, M. L., & van der Laan, M. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwu253 · Cited by 186
Multi-omic rejuvenation of human cells by maturation phase transient reprogramming
Gill, D., Parry, A., Santos, F., Okkenhaug, H., Todd, C. D., Hernando-Herraez, I., Stubbs, T. M., Milagre, I., & Reik, W. (2022) · eLife · doi:10.7554/elife.71624 · Cited by 185
CSF biomarkers cutoffs: The importance of coincident neuropathological diseases
Toledo, J. B., Brettschneider, J., Grossman, M., Arnold, S. E., Hu, W. T., Xie, S. X., Lee, V. M.-Y., Shaw, L. M., & Trojanowski, J. Q. (2012) · Acta Neuropathologica · doi:10.1007/s00401-012-0983-7 · Cited by 182
Satellite observations document trends consistent with a boreal forest biome shift
Berner, L. T., & Goetz, S. J. (2022) · Global Change Biology · doi:10.1111/gcb.16121 · Cited by 182
Shah, M. S., DeSantis, T. Z., Weinmaier, T., McMurdie, P. J., Cope, J. L., Altrichter, A., Yamal, J.-M., & Hollister, E. B. (2018) · Gut · doi:10.1136/gutjnl-2016-313189 · Cited by 180
Genome Sequences Reveal Cryptic Speciation in the Human Pathogen Histoplasma capsulatum
Sepúlveda, V. E., Márquez, R., Turissini, D. A., Goldman, W. E., & Matute, D. R. (2017) · mBio · doi:10.1128/mbio.01339-17 · Cited by 175
Field evaluation of a random forest activity classifier for wrist-worn accelerometer data
Pavey, T. G., Gilson, N. D., Gomersall, S. R., Clark, B., & Trost, S. G. (2017) · Journal of Science and Medicine in Sport · doi:10.1016/j.jsams.2016.06.003 · Cited by 174
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Mahood, E. H., Kruse, L. H., & Moghe, G. D. (2020) · Applications in Plant Sciences · doi:10.1002/aps3.11376 · Cited by 153
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Leung, H., Long, X., Ni, Y., Qian, L., Nychas, E., Siliceo, S. L., Pohl, D., Hanhineva, K., Liu, Y., Xu, A., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abk0855 · Cited by 152
Dinov, I. D., Heavner, B., Tang, M., Glusman, G., Chard, K., Darcy, M., Madduri, R., Pa, J., Spino, C., Kesselman, C., et al. (2016) · PLOS ONE · doi:10.1371/journal.pone.0157077 · Cited by 145
Lynam, A. L., Dennis, J. M., Owen, K. R., Oram, R. A., Jones, A. G., Shields, B. M., & Ferrat, L. A. (2020) · Diagnostic and Prognostic Research · doi:10.1186/s41512-020-00075-2 · Cited by 142
Predicting functional effects of missense variants in voltage-gated sodium and calcium channels
Heyne, H. O., Baez-Nieto, D., Iqbal, S., Palmer, D. S., Brunklaus, A., May, P., Collaborative, E., Johannesen, K. M., Lauxmann, S., Lemke, J. R., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay6848 · Cited by 140
The best practice for microbiome analysis using R
Wen, T., Niu, G., Chen, T., Shen, Q., Yuan, J., & Liu, Y.-X. (2023) · Protein & Cell · doi:10.1093/procel/pwad024 · Cited by 139
Neurite orientation dispersion and density imaging (NODDI) and free‐water imaging in Parkinsonism
Mitchell, T., Archer, D. B., Chu, W. T., Coombes, S. A., Lai, S., Wilkes, B. J., McFarland, N. R., Okun, M. S., Black, M. L., Herschel, E., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24760 · Cited by 118
Schultebraucks, K., Yadav, V., Shalev, A. Y., Bonanno, G. A., & Galatzer-Levy, I. R. (2022) · Psychological Medicine · doi:10.1017/s0033291720002718 · Cited by 108
Faedda, G. L., Ohashi, K., Hernandez, M., McGreenery, C. E., Grant, M. C., Baroni, A., Polcari, A., & Teicher, M. H. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12520 · Cited by 111
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Vittorini, P., Menini, S., & Tonelli, S. (2021) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-020-00230-2 · Cited by 105
Pärnänen, K. M., Hultman, J., Markkanen, M., Satokari, R., Rautava, S., Lamendella, R., Wright, J., McLimans, C. J., Kelleher, S. L., & Virta, M. P. (2022) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqab353 · Cited by 99
Rice, J. S., Emanuel, R. E., Vose, J. M., & Nelson, S. A. C. (2015) · Water Resources Research · doi:10.1002/2014wr016367 · Cited by 95
SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots
Moreira, I. S., Koukos, P. I., Melo, R., Almeida, J. G., Preto, A. J., Schaarschmidt, J., Trellet, M., Gümüş, Z. H., Costa, J., & Bonvin, A. M. J. J. (2017) · Scientific Reports · doi:10.1038/s41598-017-08321-2 · Cited by 98
Taliaz, D., Spinrad, A., Barzilay, R., Barnett-Itzhaki, Z., Averbuch, D., Teltsh, O., Schurr, R., Darki-Morag, S., & Lerer, B. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01488-3 · Cited by 93
Corey, K. E., Pitts, R., Lai, M., Loureiro, J., Masia, R., Osganian, S. A., Gustafson, J. L., Hutter, M. M., Gee, D. W., Meireles, O. R., et al. (2022) · Journal of Hepatology · doi:10.1016/j.jhep.2021.09.026 · Cited by 88
de Wet, T. J., Winkler, K. R., Mhlanga, M., Mizrahi, V., & Warner, D. F. (2020) · eLife · doi:10.7554/elife.60083 · Cited by 79
SOLart: a structure-based method to predict protein solubility and aggregation
Hou, Q., Kwasigroch, J. M., Rooman, M., & Pucci, F. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz773 · Cited by 86
Wang, Y., Zhao, Z.-J., Kang, X.-R., Bian, T., Shen, Z.-M., Jiang, Y., Sun, B., Hu, H.-B., & Chen, Y.-S. (2020) · Aging · doi:10.18632/aging.104095 · Cited by 85
Geng, C., Vangone, A., Folkers, G. E., Xue, L. C., & Bonvin, A. M. J. J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25630 · Cited by 82
Machine learning enhances prediction of illness course: a longitudinal study in eating disorders
Haynos, A. F., Wang, S. B., Lipson, S., Peterson, C. B., Mitchell, J. E., Halmi, K. A., Agras, W. S., & Crow, S. J. (2021) · Psychological Medicine · doi:10.1017/s0033291720000227 · Cited by 73
Mathioudakis, N. N., Abusamaan, M. S., Shakarchi, A. F., Sokolinsky, S., Fayzullin, S., McGready, J., Zilbermint, M., Saria, S., & Golden, S. H. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.30913 · Cited by 67
Smoliński, S., & Radtke, K. (2017) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsw136 · Cited by 69
Distinct EEG signatures differentiate unconsciousness and disconnection during anaesthesia and sleep
Casey, C. P., Tanabe, S., Farahbakhsh, Z., Parker, M., Bo, A., White, M., Ballweg, T., Mcintosh, A., Filbey, W., Saalmann, Y., et al. (2022) · British Journal of Anaesthesia · doi:10.1016/j.bja.2022.01.010 · Cited by 66
Text mining to support abstract screening for knowledge syntheses: a semi-automated workflow
Pham, B., Jovanovic, J., Bagheri, E., Antony, J., Ashoor, H., Nguyen, T. T., Rios, P., Robson, R., Thomas, S. M., Watt, J., et al. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01700-x · Cited by 63
Hulman, A., Witte, D. R., Vistisen, D., Balkau, B., Dekker, J. M., Herder, C., Hatunic, M., Konrad, T., Færch, K., & Manco, M. (2018) · Diabetes Care · doi:10.2337/dc18-0279 · Cited by 66
Wallert, J., Gustafson, E., Held, C., Madison, G., Norlund, F., von Essen, L., & Olsson, E. M. G. (2018) · doi:10.2196/preprints.10754 · Cited by 63
Missing the missing values: The ugly duckling of fairness in machine learning
Fernando, M., Cèsar, F., David, N., & José, H. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22415 · Cited by 62
Schiweck, C., Claes, S., Van Oudenhove, L., Lafit, G., Vaessen, T., de Beeck, G. O., Berghmans, R., Wijkhuijs, A., Müller, N., Arolt, V., et al. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-00979-z · Cited by 59
Khan, N., Shahid, S., Ismail, T. B., & Behlil, F. (2021) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-020-01963-1 · Cited by 59
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Pietropaoli, D., Monaco, A., D’Aiuto, F., Muñoz Aguilera, E., Ortu, E., Giannoni, M., Czesnikiewicz-Guzik, M., Guzik, T. J., Ferri, C., & Del Pinto, R. (2020) · Journal of Hypertension · doi:10.1097/hjh.0000000000002514 · Cited by 56
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Pan, Y., & Xu, J. (2025) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2024.103377 · Cited by 54
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Parsons, D. J., Pelletier, T. A., Wieringa, J. G., Duckett, D. J., & Carstens, B. C. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2103400119 · Cited by 52
Bargheet, A., Klingenberg, C., Esaiassen, E., Hjerde, E., Cavanagh, J. P., Bengtsson-Palme, J., & Pettersen, V. K. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104613 · Cited by 52
Discovery of disease-adapted bacterial lineages in inflammatory bowel diseases
Kumbhari, A., Cheng, T. N. H., Ananthakrishnan, A. N., Kochar, B., Burke, K. E., Shannon, K., Lau, H., Xavier, R. J., & Smillie, C. S. (2024) · Cell Host & Microbe · doi:10.1016/j.chom.2024.05.022 · Cited by 54
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Arbet, J., Brokamp, C., Meinzen-Derr, J., Trinkley, K. E., & Spratt, H. M. (2021) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.513 · Cited by 46
Wallert, J., Boberg, J., Kaldo, V., Mataix-Cols, D., Flygare, O., Crowley, J. J., Halvorsen, M., Ben Abdesslem, F., Boman, M., Andersson, E., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02133-3 · Cited by 48
Clink, D. J., Crofoot, M. C., & Marshall, A. J. (2019) · Bioacoustics · doi:10.1080/09524622.2018.1426042 · Cited by 44
Johnson, N. D., Wu, X., Still, C. D., Chu, X., Petrick, A. T., Gerhard, G. S., Conneely, K. N., & DiStefano, J. K. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01129-y · Cited by 43
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Ogishi, M., Yang, R., Gruber, C., Zhang, P., Pelham, S. J., Spaan, A. N., Rosain, J., Chbihi, M., Han, J. E., Rao, V. K., et al. (2021) · The Journal of Immunology · doi:10.4049/jimmunol.2000854 · Cited by 42
Buma, B., & Livneh, B. (2017) · Environmental Research Letters · doi:10.1088/1748-9326/aa7091 · Cited by 37
Paricharak, S., Cortés-Ciriano, I., IJzerman, A. P., Malliavin, T. E., & Bender, A. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0063-9 · Cited by 38
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Samarra, A., Alcañiz, A. J., Martínez-Costa, C., Marina, A., Comas, I., Segata, N., Quijada, N. M., & Collado, M. C. (2025) · Nature Communications · doi:10.1038/s41467-025-61154-w · Cited by 32
Burford, K. G., Itzkowitz, N. G., Ortega, A. G., Teitler, J. O., & Rundle, A. G. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.25981 · Cited by 33
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Filzen, T. M., Kutchukian, P. S., Hermes, J. D., Li, J., & Tudor, M. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005335 · Cited by 31
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Arora, I. H., Woscoboinik, G. G., Mokhtar, S., Quagliarini, B., Bartal, A., Jagodnik, K. M., Barry, R. L., Edlow, A. G., Orr, S. P., & Dekel, S. (2024) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2023.11.1229 · Cited by 32
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Salvetat, N., Checa-Robles, F. J., Patel, V., Cayzac, C., Dubuc, B., Chimienti, F., Abraham, J.-D., Dupré, P., Vetter, D., Méreuze, S., et al. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-01938-6 · Cited by 31
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Zauli-Sajani, S., Marchesi, S., Pironi, C., Barbieri, C., Poluzzi, V., & Colacci, A. (2021) · Atmospheric Pollution Research · doi:10.1016/j.apr.2020.11.010 · Cited by 30
Moshe, I., Terhorst, Y., Paganini, S., Schlicker, S., Pulkki-Råback, L., Baumeister, H., Sander, L. B., & Ebert, D. D. (2022) · Journal of Medical Internet Research · doi:10.2196/38261 · Cited by 28
Schoeman, E. M., Bringans, S., Peters, K., Casey, T., Andronis, C., Chen, L., Duong, M., Girling, J. E., Healey, M., Boughton, B. A., et al. (2025) · Human Reproduction · doi:10.1093/humrep/deae278 · Cited by 27
Mirzaei, S., Mao, H., Al-Nima, R. R. O., & Woo, W. L. (2023) · Information · doi:10.3390/info15010004 · Cited by 25
Early Detection of Respiratory Diseases in Calves by Use of an Ear-Attached Accelerometer
Ramezani Gardaloud, N., Guse, C., Lidauer, L., Steininger, A., Kickinger, F., Öhlschuster, M., Auer, W., Iwersen, M., Drillich, M., & Klein-Jöbstl, D. (2022) · Animals · doi:10.3390/ani12091093 · Cited by 24
Probing the digital exposome: associations of social media use patterns with youth mental health
Pagliaccio, D., Tran, K. T., Visoki, E., DiDomenico, G. E., Auerbach, R. P., & Barzilay, R. (2024) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-024-00006-9 · Cited by 23
Validation of forensic facial comparison by morphological analysis in photographic and CCTV samples
Bacci, N., Houlton, T. M. R., Briers, N., & Steyn, M. (2021) · International Journal of Legal Medicine · doi:10.1007/s00414-021-02512-3 · Cited by 23
DeSalvio, A. J., Adak, A., Murray, S. C., Jarquín, D., Winans, N. D., Crozier, D., & Rooney, W. L. (2024) · The Plant Genome · doi:10.1002/tpg2.20454 · Cited by 21
“These correspond to those with variable importance scores greater than zero as determined by the lasso function implemented via the caret package in R (Kuhn, 2008).”
MethodKaulich, E., Waselenchuk, Q., Fürst, N., Desch, K., Mosbacher, J., Ciirdaeva, E., Juengling, M., Tushev, G., Langer, J., & Schuman, E. M. (2024) · doi:10.1101/2024.08.05.606570 · Cited by 22
Streun, G. L., Steuer, A. E., Ebert, L. C., Dobay, A., & Kraemer, T. (2021) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2021-0010 · Cited by 18
Neri, F., Takajjart, S. N., Lerner, C. A., Desprez, P.-Y., Schilling, B., Campisi, J., & Gerencser, A. A. (2024) · GeroScience · doi:10.1007/s11357-024-01167-3 · Cited by 17
Pairing Machine Learning and Clinical Psychology: How You Evaluate Predictive Performance Matters
Jacobucci, R., Littlefield, A. K., Millner, A. J., Kleiman, E., & Steinley, D. (2020) · doi:10.31234/osf.io/2yber · Cited by 12
Walker, R. S., Ferguson, J. R., Olmeda, A., Hamilton, M. J., Elghammer, J., & Buchanan, B. (2023) · PeerJ · doi:10.7717/peerj.15137 · Cited by 11
Matthaeus, H., Heim, C., Voelkle, M. C., & Singer, T. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1277929 · Cited by 6
Zhang, Z., Qiu, X., Zeng, X., Liu, X., Lu, J., Xu, C., Huang, J., Zhao, C., & Zhan, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-18650-2 · Cited by 6
Ferariu, A., Chang, H., Kumar, A., Sahl, A., Gorka, S., Wang, L., Thompson, W. K., & Zhang, F. (2025) · doi:10.1101/2025.03.12.25323356 · Cited by 3
Metadata
- Authors
- Kuhn, M.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2008
- Volume
- 28
- Issue
- 5
- ISSN
- 1548-7660
- Cited by
- 8,863
- References
- 1
- Language
- en
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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