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ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

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  1. Henry T. Hsueh & Renee Ti Chou & Usha Rai & Wathsala Liyanage & Yoo Chun Kim & Matthew B. Appell & Jahnavi Pejavar & Kirby T. Leo & Charlotte Davison & Patricia Kolodziejski & Ann Mozzer & HyeYoung Kw, 2023. "Machine learning-driven multifunctional peptide engineering for sustained ocular drug delivery," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
  2. László Pásztor & Katalin Takács & János Mészáros & Gábor Szatmári & Mátyás Árvai & Tibor Tóth & Gyöngyi Barna & Sándor Koós & Zsófia Adrienn Kovács & Péter László & Kitti Balog, 2023. "Indirect Prediction of Salt Affected Soil Indicator Properties through Habitat Types of a Natural Saline Grassland Using Unmanned Aerial Vehicle Imagery," Land, MDPI, vol. 12(8), pages 1-23, July.
  3. Fogliato Riccardo & Oliveira Natalia L. & Yurko Ronald, 2021. "TRAP: a predictive framework for the Assessment of Performance in Trail Running," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(2), pages 129-143, June.
  4. Edward J Gregr & Dana R Haggarty & Sarah C Davies & Cole Fields & Joanne Lessard, 2021. "Comprehensive marine substrate classification applied to Canada’s Pacific shelf," PLOS ONE, Public Library of Science, vol. 16(10), pages 1-28, October.
  5. Daniel Baier & Björn Stöcker, 2022. "Profit uplift modeling for direct marketing campaigns: approaches and applications for online shops," Journal of Business Economics, Springer, vol. 92(4), pages 645-673, May.
  6. Hornung, Roman & Boulesteix, Anne-Laure, 2022. "Interaction forests: Identifying and exploiting interpretable quantitative and qualitative interaction effects," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
  7. Hapfelmeier, Alexander & Hornung, Roman & Haller, Bernhard, 2023. "Efficient permutation testing of variable importance measures by the example of random forests," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
  8. Filippi, Patrick & Whelan, Brett M. & Vervoort, R. Willem & Bishop, Thomas F.A., 2020. "Mid-season empirical cotton yield forecasts at fine resolutions using large yield mapping datasets and diverse spatial covariates," Agricultural Systems, Elsevier, vol. 184(C).
  9. Mohnen Sigrid M. & Rotteveel Adriënne H. & Doornbos Gerda & Polder Johan J., 2020. "Healthcare Expenditure Prediction with Neighbourhood Variables – A Random Forest Model," Statistics, Politics and Policy, De Gruyter, vol. 11(2), pages 111-138, December.
  10. Albert Stuart Reece & Gary Kenneth Hulse, 2022. "Epidemiological Patterns of Cannabis- and Substance- Related Congenital Uronephrological Anomalies in Europe: Geospatiotemporal and Causal Inferential Study," IJERPH, MDPI, vol. 19(21), pages 1-61, October.
  11. Roman Hornung, 2020. "Ordinal Forests," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 4-17, April.
  12. Mariana Oliveira & Luís Torgo & Vítor Santos Costa, 2021. "Evaluation Procedures for Forecasting with Spatiotemporal Data," Mathematics, MDPI, vol. 9(6), pages 1-27, March.
  13. Elliot Beck & Damian Kozbur & Michael Wolf, 2023. "Hedging Forecast Combinations With an Application to the Random Forest," Papers 2308.15384, arXiv.org, revised Aug 2023.
  14. Albert Stuart Reece & Gary Kenneth Hulse, 2022. "Cannabis- and Substance-Related Epidemiological Patterns of Chromosomal Congenital Anomalies in Europe: Geospatiotemporal and Causal Inferential Study," IJERPH, MDPI, vol. 19(18), pages 1-51, September.
  15. Matthew Harding & Gabriel F. R. Vasconcelos, 2022. "Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?," Papers 2202.04218, arXiv.org.
  16. Nicole Ellenbach & Anne-Laure Boulesteix & Bernd Bischl & Kristian Unger & Roman Hornung, 2021. "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 212-231, July.
  17. Bommert, Andrea & Sun, Xudong & Bischl, Bernd & Rahnenführer, Jörg & Lang, Michel, 2020. "Benchmark for filter methods for feature selection in high-dimensional classification data," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
  18. Gordon Burtch & Yili Hong & Senthil Kumar, 2021. "When Does Dispute Resolution Substitute for a Reputation System? Empirical Evidence from a Service Procurement Platform," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1565-1582, June.
  19. Anna Gogleva & Dimitris Polychronopoulos & Matthias Pfeifer & Vladimir Poroshin & Michaël Ughetto & Matthew J. Martin & Hannah Thorpe & Aurelie Bornot & Paul D. Smith & Ben Sidders & Jonathan R. Dry &, 2022. "Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
  20. Jana Emmenegger & Ralf Münnich & Jannik Schaller, 2022. "Evaluating Data Fusion Methods to Improve Income Modelling," Research Papers in Economics 2022-03, University of Trier, Department of Economics.
  21. Vincenzo Cribari & Michael P. Strager & Aaron E. Maxwell & Charles Yuill, 2021. "Landscape Changes in the Southern Coalfields of West Virginia: Multi-Level Intensity Analysis and Surface Mining Transitions in the Headwaters of the Coal River from 1976 to 2016," Land, MDPI, vol. 10(7), pages 1-32, July.
  22. Feuerhake, Jörg & Lange, Kerstin & Siegismund, Annelen & Vigneau, Elsa, 2020. "Kodierung des Geburtsstaats in der Wanderungsstatistik: Ein Vergleich regelbasierter Signierung mit Verfahren des maschinellen Lernens," WISTA – Wirtschaft und Statistik, Statistisches Bundesamt (Destatis), Wiesbaden, vol. 72(3), pages 98-110.
  23. Ashesh Rambachan, 2022. "Identifying Prediction Mistakes in Observational Data," NBER Chapters, in: Economics of Artificial Intelligence, National Bureau of Economic Research, Inc.
  24. Levy Jonathan & van der Laan Mark & Hubbard Alan & Pirracchio Romain, 2021. "A fundamental measure of treatment effect heterogeneity," Journal of Causal Inference, De Gruyter, vol. 9(1), pages 83-108, January.
  25. Ramakhanna, Selebalo Joseph & Mapeshoane, Botle Esther & Omuto, Christian Thine, 2022. "Carbon sequestration potential in croplands in Lesotho," Ecological Modelling, Elsevier, vol. 471(C).
  26. Bogdan Oancea, 2023. "Automatic Product Classification Using Supervised Machine Learning Algorithms in Price Statistics," Mathematics, MDPI, vol. 11(7), pages 1-32, March.
  27. Lillo-Bravo, I. & Vera-Medina, J. & Fernandez-Peruchena, C. & Perez-Aparicio, E. & Lopez-Alvarez, J.A. & Delgado-Sanchez, J.M., 2023. "Random Forest model to predict solar water heating system performance," Renewable Energy, Elsevier, vol. 216(C).
  28. Fitzpatrick, Trevor & Mues, Christophe, 2021. "How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments," European Journal of Operational Research, Elsevier, vol. 294(2), pages 711-722.
  29. Thorsten Ruf & Mario Gilcher & Thomas Udelhoven & Christoph Emmerling, 2021. "Implications of Bioenergy Cropping for Soil: Remote Sensing Identification of Silage Maize Cultivation and Risk Assessment Concerning Soil Erosion and Compaction," Land, MDPI, vol. 10(2), pages 1-16, January.
  30. Van Belle, Jente & Guns, Tias & Verbeke, Wouter, 2021. "Using shared sell-through data to forecast wholesaler demand in multi-echelon supply chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 466-479.
  31. Schmid, Lena & Gerharz, Alexander & Groll, Andreas & Pauly, Markus, 2023. "Tree-based ensembles for multi-output regression: Comparing multivariate approaches with separate univariate ones," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
  32. Ma, Shaohui & Fildes, Robert, 2021. "Retail sales forecasting with meta-learning," European Journal of Operational Research, Elsevier, vol. 288(1), pages 111-128.
  33. Allison L Hicks & Nicole Wheeler & Leonor Sánchez-Busó & Jennifer L Rakeman & Simon R Harris & Yonatan H Grad, 2019. "Evaluation of parameters affecting performance and reliability of machine learning-based antibiotic susceptibility testing from whole genome sequencing data," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-21, September.
  34. Florian Pargent & Florian Pfisterer & Janek Thomas & Bernd Bischl, 2022. "Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features," Computational Statistics, Springer, vol. 37(5), pages 2671-2692, November.
  35. Albert Stuart Reece & Gary Kenneth Hulse, 2022. "European Epidemiological Patterns of Cannabis- and Substance-Related Body Wall Congenital Anomalies: Geospatiotemporal and Causal Inferential Study," IJERPH, MDPI, vol. 19(15), pages 1-38, July.
  36. Peter Kosa & Christopher Barbour & Mihael Varosanec & Alison Wichman & Mary Sandford & Mark Greenwood & Bibiana Bielekova, 2022. "Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
  37. Lyubchich, Vyacheslav & Woodland, Ryan J., 2019. "Using isotope composition and other node attributes to predict edges in fish trophic networks," Statistics & Probability Letters, Elsevier, vol. 144(C), pages 63-68.
  38. Wang, Wu & Harrou, Fouzi & Bouyeddou, Benamar & Senouci, Sidi-Mohammed & Sun, Ying, 2022. "Cyber-attacks detection in industrial systems using artificial intelligence-driven methods," International Journal of Critical Infrastructure Protection, Elsevier, vol. 38(C).
  39. Lechner, Michael & Okasa, Gabriel, 2019. "Random Forest Estimation of the Ordered Choice Model," Economics Working Paper Series 1908, University of St. Gallen, School of Economics and Political Science.
  40. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Feb 2024.
  41. Smith, Geoffrey Peter, 2022. "Predicting the debt-equity decision," Finance Research Letters, Elsevier, vol. 48(C).
  42. Çiflikli, Gökhan & Metternich, Nils W, 2019. "We predict conflict better than we thought! Taking time seriously when evaluating predictions in Binary-Time-Series-Cross-Section-Data," SocArXiv tvshu, Center for Open Science.
  43. Susan Athey & Julie Tibshirani & Stefan Wager, 2016. "Generalized Random Forests," Papers 1610.01271, arXiv.org, revised Apr 2018.
  44. Yuanyuan Shi & Junyu Zhao & Xianchong Song & Zuoyu Qin & Lichao Wu & Huili Wang & Jian Tang, 2021. "Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-15, June.
  45. Zhou Lu & Zhuyao Zhuo, 2021. "Modelling of Chinese corporate bond default – A machine learning approach," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(5), pages 6147-6191, December.
  46. Marchetto, Elisa & Da Re, Daniele & Tordoni, Enrico & Bazzichetto, Manuele & Zannini, Piero & Celebrin, Simone & Chieffallo, Ludovico & Malavasi, Marco & Rocchini, Duccio, 2023. "Testing the effect of sample prevalence and sampling methods on probability- and favourability-based SDMs," Ecological Modelling, Elsevier, vol. 477(C).
  47. Shu Jiang & Yijun Xie & Graham A. Colditz, 2021. "Functional ensemble survival tree: Dynamic prediction of Alzheimer’s disease progression accommodating multiple time‐varying covariates," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 66-79, January.
  48. Giacomo De Giorgi & Matthew Harding & Gabriel Vasconcelos, 2021. "Predicting Mortality from Credit Reports," Papers 2111.03662, arXiv.org.
  49. Alexis Gerossier & Robin Girard & George Kariniotakis, 2019. "Modeling and Forecasting Electric Vehicle Consumption Profiles," Energies, MDPI, vol. 12(7), pages 1-14, April.
  50. Riccardo Di Francesco, 2023. "Ordered Correlation Forest," Papers 2309.08755, arXiv.org.
  51. Marc Deffland & Claudia Spies & Bjoern Weiss & Niklas Keller & Mirjam Jenny & Jochen Kruppa & Felix Balzer, 2020. "Effects of pain, sedation and delirium monitoring on clinical and economic outcome: A retrospective study," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-14, September.
  52. Stephanie Houle & Ryan Macdonald, 2023. "Identifying Nascent High-Growth Firms Using Machine Learning," Staff Working Papers 23-53, Bank of Canada.
  53. Wheeler, Andrew Palmer & Steenbeek, Wouter, 2020. "Mapping the risk terrain for crime using machine learning," SocArXiv xc538, Center for Open Science.
  54. Preston Thomas Sorenson & Jeremy Kiss & Angela Bedard-Haughn, 2024. "A Proposed Methodology for Determining the Economically Optimal Number of Sample Points for Carbon Stock Estimation in the Canadian Prairies," Land, MDPI, vol. 13(1), pages 1-16, January.
  55. Victor Martínez‐de‐Albéniz & Arnau Planas & Stefano Nasini, 2020. "Using Clickstream Data to Improve Flash Sales Effectiveness," Production and Operations Management, Production and Operations Management Society, vol. 29(11), pages 2508-2531, November.
  56. Andreas D. Meid & Lucas Wirbka, 2022. "Can Machine Learning from Real-World Data Support Drug Treatment Decisions? A Prediction Modeling Case for Direct Oral Anticoagulants," Medical Decision Making, , vol. 42(5), pages 587-598, July.
  57. Jorge Luis Andrade & José Luis Valencia, 2022. "A Fuzzy Random Survival Forest for Predicting Lapses in Insurance Portfolios Containing Imprecise Data," Mathematics, MDPI, vol. 11(1), pages 1-16, December.
  58. Garre, Alberto & Ruiz, Mari Carmen & Hontoria, Eloy, 2020. "Application of Machine Learning to support production planning of a food industry in the context of waste generation under uncertainty," Operations Research Perspectives, Elsevier, vol. 7(C).
  59. Maia, Mateus & Murphy, Keefe & Parnell, Andrew C., 2024. "GP-BART: A novel Bayesian additive regression trees approach using Gaussian processes," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
  60. Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
  61. Eeva-Katri Kumpula & Pauline Norris & Adam C Pomerleau, 2020. "Stocks of paracetamol products stored in urban New Zealand households: A cross-sectional study," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-11, June.
  62. Huber, Martin & Meier, Jonas & Wallimann, Hannes, 2022. "Business analytics meets artificial intelligence: Assessing the demand effects of discounts on Swiss train tickets," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 22-39.
  63. Hirche, Martin & Farris, Paul W. & Greenacre, Luke & Quan, Yiran & Wei, Susan, 2021. "Predicting Under- and Overperforming SKUs within the Distribution–Market Share Relationship," Journal of Retailing, Elsevier, vol. 97(4), pages 697-714.
  64. Calum Robertson & Raphael Suire & Sylvain Dejean, 2023. "Unpacking and Measuring Urban Complexity Evidence from amenities in Paris," Papers in Evolutionary Economic Geography (PEEG) 2315, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Jul 2023.
  65. Michael Bucker & Gero Szepannek & Alicja Gosiewska & Przemyslaw Biecek, 2020. "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring," Papers 2009.13384, arXiv.org.
  66. Andree,Bo Pieter Johannes & Chamorro Elizondo,Andres Fernando & Kraay,Aart C. & Spencer,Phoebe Girouard & Wang,Dieter, 2020. "Predicting Food Crises," Policy Research Working Paper Series 9412, The World Bank.
  67. Maud H. Korte & Gertjan S. Verhoeven & Arianne M. J. Elissen & Silke F. Metzelthin & Dirk Ruwaard & Misja C. Mikkers, 2020. "Using machine learning to assess the predictive potential of standardized nursing data for home healthcare case-mix classification," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(8), pages 1121-1129, November.
  68. Migle Janulaitiene & Vilmantas Gegzna & Lina Baranauskiene & Aistė Bulavaitė & Martynas Simanavicius & Milda Pleckaityte, 2018. "Phenotypic characterization of Gardnerella vaginalis subgroups suggests differences in their virulence potential," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-20, July.
  69. Zabrocki, Léo & Leroutier, Marion & Bind, Marie-Abèle, 2021. "Estimating the Causal Effects of Cruise Traffic on Air Pollution using Randomization-Based Inference," OSF Preprints v7ctk, Center for Open Science.
  70. Justin P. Suraci & Tina G. Mozelewski & Caitlin E. Littlefield & Theresa Nogeire McRae & Ann Sorensen & Brett G. Dickson, 2023. "Management of U.S. Agricultural Lands Differentially Affects Avian Habitat Connectivity," Land, MDPI, vol. 12(4), pages 1-20, March.
  71. Urfels, Anton & Mausch, Kai & Harris, Dave & McDonald, Andrew J. & Kishore, Avinash & Balwinder-Singh, & van Halsema, Gerardo & Struik, Paul C. & Craufurd, Peter & Foster, Timothy & Singh, Vartika & K, 2023. "Farm size limits agriculture's poverty reduction potential in Eastern India even with irrigation-led intensification," Agricultural Systems, Elsevier, vol. 207(C).
  72. Lisa-Katrin Schätzle & Ali Hadizadeh Esfahani & Andreas Schuppert, 2020. "Methodological challenges in translational drug response modeling in cancer: A systematic analysis with FORESEE," PLOS Computational Biology, Public Library of Science, vol. 16(4), pages 1-23, April.
  73. Patrick José Jeetze & Isabelle Weindl & Justin Andrew Johnson & Pasquale Borrelli & Panos Panagos & Edna J. Molina Bacca & Kristine Karstens & Florian Humpenöder & Jan Philipp Dietrich & Sara Minoli &, 2023. "Projected landscape-scale repercussions of global action for climate and biodiversity protection," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
  74. Jian Lu & Raheel Ahmad & Thomas Nguyen & Jeffrey Cifello & Humza Hemani & Jiangyuan Li & Jinguo Chen & Siyi Li & Jing Wang & Achouak Achour & Joseph Chen & Meagan Colie & Ana Lustig & Christopher Dunn, 2022. "Heterogeneity and transcriptome changes of human CD8+ T cells across nine decades of life," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
  75. Gianluca De Nard & Simon Hediger & Markus Leippold, 2022. "Subsampled factor models for asset pricing: The rise of Vasa," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1217-1247, September.
  76. Shengzhao Wang & Meitang Li & Bo Yu & Shan Bao & Yuren Chen, 2022. "Investigating the Impacting Factors on the Public’s Attitudes towards Autonomous Vehicles Using Sentiment Analysis from Social Media Data," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
  77. Timo Schulte & Tillmann Wurz & Oliver Groene & Sabine Bohnet-Joschko, 2023. "Big Data Analytics to Reduce Preventable Hospitalizations—Using Real-World Data to Predict Ambulatory Care-Sensitive Conditions," IJERPH, MDPI, vol. 20(6), pages 1-16, March.
  78. Irene A. Abela & Chloé Pasin & Magdalena Schwarzmüller & Selina Epp & Michèle E. Sickmann & Merle M. Schanz & Peter Rusert & Jacqueline Weber & Stefan Schmutz & Annette Audigé & Liridona Maliqi & Anni, 2021. "Multifactorial seroprofiling dissects the contribution of pre-existing human coronaviruses responses to SARS-CoV-2 immunity," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
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  93. Mirko Di Febbraro & Ludovico Frate & Maria Carla de Francesco & Angela Stanisci & Francesco Pio Tozzi & Marco Varricchione & Maria Laura Carranza, 2021. "Modelling Beach Litter Accumulation on Mediterranean Coastal Landscapes: An Integrative Framework Using Species Distribution Models," Land, MDPI, vol. 10(1), pages 1-17, January.
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  96. Daniel S. Maynard & Lalasia Bialic-Murphy & Constantin M. Zohner & Colin Averill & Johan Hoogen & Haozhi Ma & Lidong Mo & Gabriel Reuben Smith & Alicia T. R. Acosta & Isabelle Aubin & Erika Berenguer , 2022. "Global relationships in tree functional traits," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
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  102. Nicholas Williams & Michael Rosenblum & Iván Díaz, 2022. "Optimising precision and power by machine learning in randomised trials with ordinal and time‐to‐event outcomes with an application to COVID‐19," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2156-2178, October.
  103. Mária Barančoková & Matej Šošovička & Peter Barančok & Peter Barančok, 2021. "Predictive Modelling of Landslide Susceptibility in the Western Carpathian Flysch Zone," Land, MDPI, vol. 10(12), pages 1-28, December.
  104. Michaël Zamo & Liliane Bel & Olivier Mestre, 2021. "Sequential aggregation of probabilistic forecasts—Application to wind speed ensemble forecasts," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 202-225, January.
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