R: A Language and Environment for Statistical Computing

Fitting Linear Mixed-Effects Models Using lme4

Bates, D., Mächler, M., Bolker, B., & Walker, S. (2015) · Journal of Statistical Software · doi:10.18637/jss.v067.i01 · Cited by 86,835

A new criterion for assessing discriminant validity in variance-based structural equation modeling

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459

lmerTest Package: Tests in Linear Mixed Effects Models

Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017) · Journal of Statistical Software · doi:10.18637/jss.v082.i13 · Cited by 24,346

Conducting Meta-Analyses in R with the metafor Package

Viechtbauer, W. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i03 · Cited by 20,464

The WEKA data mining software: an update

Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1656274.1656278 · Cited by 17,750

bibliometrix: An R-tool for comprehensive science mapping analysis

Aria, M., & Cuccurullo, C. (2017) · Journal of Informetrics · doi:10.1016/j.joi.2017.08.007 · Cited by 16,064

Integrated analysis of multimodal single-cell data

Hao, Y., Hao, S., Andersen-Nissen, E., Mauck, W. M., Zheng, S., Butler, A., Lee, M. J., Wilk, A. J., Darby, C., Zagar, M., et al. (2020) · doi:10.1101/2020.10.12.335331 · Cited by 15,508

GSVA: gene set variation analysis for microarray and RNA-Seq data

Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440

pROC: an open-source package for R and S+ to analyze and compare ROC curves

Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697

Finite Mixture Models

McLachlan, G., & Peel, D. (2000) · Wiley Series in Probability and Statistics · doi:10.1002/0471721182 · Cited by 10,991

A general and simple method for obtaining R 2 from generalized linear mixed‐effects models

Nakagawa, S., & Schielzeth, H. (2013) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2012.00261.x · Cited by 10,356

Random effects structure for confirmatory hypothesis testing: Keep it maximal

Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013) · Journal of Memory and Language · doi:10.1016/j.jml.2012.11.001 · Cited by 10,328

brms : An R Package for Bayesian Multilevel Models Using Stan

Bürkner, P.-C. (2017) · Journal of Statistical Software · doi:10.18637/jss.v080.i01 · Cited by 9,476

Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R: A Workbook

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021) · Classroom Companion: Business · doi:10.1007/978-3-030-80519-7 · Cited by 10,343

Inferring tumour purity and stromal and immune cell admixture from expression data

Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013) · Nature Communications · doi:10.1038/ncomms3612 · Cited by 9,778

Measuring individual differences in implicit cognition: the implicit association test

Greenwald, A. G., McGhee, D. E., & Schwartz, J. L. K. (1998) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.74.6.1464 · Cited by 9,210

Mixed-effects modeling with crossed random effects for subjects and items

Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008) · Journal of Memory and Language · doi:10.1016/j.jml.2007.12.005 · Cited by 8,540

A protocol for data exploration to avoid common statistical problems: Data exploration

Zuur, A. F., Ieno, E. N., & Elphick, C. S. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2009.00001.x · Cited by 8,228

Stan : A Probabilistic Programming Language

Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A. (2017) · Journal of Statistical Software · doi:10.18637/jss.v076.i01 · Cited by 7,754

IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045

Cho, N. H., Shaw, J. E., Karuranga, S., Huang, Y., da Rocha Fernandes, J. D., Ohlrogge, A. W., & Malanda, B. (2018) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.02.023 · Cited by 7,283

Global causes of maternal death: a WHO systematic analysis

Say, L., Chou, D., Gemmill, A., Tunçalp, Ö., Moller, A.-B., Daniels, J., Gülmezoglu, A. M., Temmerman, M., & Alkema, L. (2014) · The Lancet Global Health · doi:10.1016/s2214-109x(14)70227-x · Cited by 6,582

A working guide to boosted regression trees

Elith, J., Leathwick, J. R., & Hastie, T. (2008) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2008.01390.x · Cited by 6,484

BUSCO Update: Novel and Streamlined Workflows along with Broader and Deeper Phylogenetic Coverage for Scoring of Eukaryotic, Prokaryotic, and Viral Genomes

Manni, M., Berkeley, M. R., Seppey, M., Simão, F. A., & Zdobnov, E. M. (2021) · Molecular Biology and Evolution · doi:10.1093/molbev/msab199 · Cited by 6,187

How to perform a meta-analysis with R: a practical tutorial

Balduzzi, S., Rücker, G., & Schwarzer, G. (2019) · Evidence Based Mental Health · doi:10.1136/ebmental-2019-300117 · Cited by 5,827

performance: An R Package for Assessment, Comparison and Testing of Statistical Models

Lüdecke, D., Ben-Shachar, M., Patil, I., Waggoner, P., & Makowski, D. (2021) · Journal of Open Source Software · doi:10.21105/joss.03139 · Cited by 5,836

Untitled work

(2015) · doi:10.1007/s11222-016-9696-4 · Cited by 5,611

Using PLS Path Modeling for Assessing Hierarchical Construct Models: Guidelines and Empirical Illustration1

Wetzels, M., Odekerken-Schröder, G., & van Oppen, C. (2009) · MIS Quarterly · doi:10.2307/20650284 · Cited by 4,944

Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics

Giambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261

Global estimates of diabetes prevalence for 2013 and projections for 2035

Guariguata, L., Whiting, D. R., Hambleton, I., Beagley, J., Linnenkamp, U., & Shaw, J. E. (2014) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2013.11.002 · Cited by 4,642

Global effects of land use on local terrestrial biodiversity

Newbold, T., Hudson, L. N., Hill, S. L. L., Contu, S., Lysenko, I., Senior, R. A., Börger, L., Bennett, D. J., Choimes, A., Collen, B., et al. (2015) · Nature · doi:10.1038/nature14324 · Cited by 4,352

The Brazilian Atlantic Forest: How much is left, and how is the remaining forest distributed? Implications for conservation

Ribeiro, M. C., Metzger, J. P., Martensen, A. C., Ponzoni, F. J., & Hirota, M. M. (2009) · Biological Conservation · doi:10.1016/j.biocon.2009.02.021 · Cited by 4,282

mixOmics: An R package for ‘omics feature selection and multiple data integration

Rohart, F., Gautier, B., Singh, A., & Lê Cao, K.-A. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005752 · Cited by 4,056

A critique of the cross-lagged panel model

Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. P. P. (2015) · Psychological Methods · doi:10.1037/a0038889 · Cited by 4,005

Multiple imputation by chained equations: what is it and how does it work?

Azur, M. J., Stuart, E. A., Frangakis, C., & Leaf, P. J. (2011) · International Journal of Methods in Psychiatric Research · doi:10.1002/mpr.329 · Cited by 3,542

NbClust : An R Package for Determining the Relevant Number of Clusters in a Data Set

Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014) · Journal of Statistical Software · doi:10.18637/jss.v061.i06 · Cited by 3,396

Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models

Jaeger, T. F. (2008) · Journal of Memory and Language · doi:10.1016/j.jml.2007.11.007 · Cited by 3,317

Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013)

Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014) · Organizational Research Methods · doi:10.1177/1094428114526928 · Cited by 3,332

Generalized additive models for location, scale and shape.

Rigby, R. A., & Stasinopoulos, D. M. (2005) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/j.1467-9876.2005.00510.x · Cited by 3,228

Simple means to improve the interpretability of regression coefficients

Schielzeth, H. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2010.00012.x · Cited by 3,027

A tutorial on regularized partial correlation networks

Epskamp, S., & Fried, E. I. (2018) · Psychological Methods · doi:10.1037/met0000167 · Cited by 2,918

Consistent Partial Least Squares Path Modeling1

Dijkstra, T. K., & Henseler, J. (2015) · MIS Quarterly · doi:10.25300/misq/2015/39.2.02 · Cited by 2,898

The art of modelling range-shifting species: The art of modelling range-shifting species

Elith, J., Kearney, M., & Phillips, S. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2010.00036.x · Cited by 2,813

Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0

Percie du Sert, N., Ahluwalia, A., Alam, S., Avey, M. T., Baker, M., Browne, W. J., Clark, A., Cuthill, I. C., Dirnagl, U., Emerson, M., et al. (2020) · PLOS Biology · doi:10.1371/journal.pbio.3000411 · Cited by 2,677

Checking consistency in mixed treatment comparison meta-analysis

Dias, S., Welton, N. J., Caldwell, D. M., & Ades, A. E. (2010) · Statistics in Medicine · doi:10.1002/sim.3767 · Cited by 2,521

Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists.

Nakagawa, S., & Schielzeth, H. (2010) · Biological Reviews · doi:10.1111/j.1469-185x.2010.00141.x · Cited by 2,584

Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0517-x · Cited by 2,712

SIMR : an R package for power analysis of generalized linear mixed models by simulation

Green, P., & MacLeod, C. J. (2016) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12504 · Cited by 2,467

agriGO: a GO analysis toolkit for the agricultural community

Du, Z., Zhou, X., Ling, Y., Zhang, Z., & Su, Z. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq310 · Cited by 2,396

Distributed lag non‐linear models

Gasparrini, A., Armstrong, B., & Kenward, M. G. (2010) · Statistics in Medicine · doi:10.1002/sim.3940 · Cited by 2,481

Prevalence of Depression, Depressive Symptoms, and Suicidal Ideation Among Medical Students: A Systematic Review and Meta-Analysis

Rotenstein, L. S., Ramos, M. A., Torre, M., Segal, J. B., Peluso, M. J., Guille, C., Sen, S., & Mata, D. A. (2016) · JAMA · doi:10.1001/jama.2016.17324 · Cited by 2,479

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

Malta, T. M., Sokolov, A., Gentles, A. J., Burzykowski, T., Poisson, L., Weinstein, J. N., Kamińska, B., Huelsken, J., Omberg, L., Gevaert, O., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.034 · Cited by 2,180

International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-Sectional Study of the INTERGROWTH-21st Project

Villar, J., Ismail, L. C., Victora, C. G., Ohuma, E. O., Bertino, E., Altman, D. G., Lambert, A., Papageorghiou, A. T., Carvalho, M., Jaffer, Y. A., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(14)60932-6 · Cited by 2,281

Exploratory Factor Analysis: A Guide to Best Practice

Watkins, M. W. (2018) · Journal of Black Psychology · doi:10.1177/0095798418771807 · Cited by 2,218

The Rediscovery of Bifactor Measurement Models

Reise, S. P. (2012) · Multivariate Behavioral Research · doi:10.1080/00273171.2012.715555 · Cited by 2,112

A dendrochronology program library in R (dplR)

Bunn, A. G. (2008) · Dendrochronologia · doi:10.1016/j.dendro.2008.01.002 · Cited by 2,073

World Health Organization Global Estimates and Regional Comparisons of the Burden of Foodborne Disease in 2010

Havelaar, A. H., Kirk, M. D., Torgerson, P. R., Gibb, H. J., Hald, T., Lake, R. J., Praet, N., Bellinger, D. C., de Silva, N. R., Gargouri, N., et al. (2015) · PLoS Medicine · doi:10.1371/journal.pmed.1001923 · Cited by 2,067

Ridge Regression and Other Kernels for Genomic Selection with R Package rrBLUP

Endelman, J. B. (2011) · The Plant Genome · doi:10.3835/plantgenome2011.08.0024 · Cited by 2,094

A brief introduction to mixed effects modelling and multi-model inference in ecology

Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E. D., Robinson, B. S., Hodgson, D. J., & Inger, R. (2018) · PeerJ · doi:10.7717/peerj.4794 · Cited by 2,094

Matrix eQTL: ultra fast eQTL analysis via large matrix operations

Shabalin, A. A. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts163 · Cited by 1,694

Model-based influences on humans’ choices and striatal prediction errors

Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P., & Dolan, R. J. (2011) · Neuron · doi:10.1016/j.neuron.2011.02.027 · Cited by 1,779

The neuroactive potential of the human gut microbiota in quality of life and depression

Valles-Colomer, M., Falony, G., Darzi, Y., Tigchelaar, E. F., Wang, J., Tito, R. Y., Schiweck, C., Kurilshikov, A., Joossens, M., Wijmenga, C., et al. (2019) · Nature Microbiology · doi:10.1038/s41564-018-0337-x · Cited by 1,963

cocor: A Comprehensive Solution for the Statistical Comparison of Correlations

Diedenhofen, B., & Musch, J. (2015) · PLOS ONE · doi:10.1371/journal.pone.0121945 · Cited by 1,919

Initial construction of a maladaptive personality trait model and inventory for DSM-5

Krueger, R. F., Derringer, J., Markon, K. E., Watson, D., & Skodol, A. E. (2012) · Psychological Medicine · doi:10.1017/s0033291711002674 · Cited by 1,908

Formation of soil organic matter via biochemical and physical pathways of litter mass loss

Cotrufo, M. F., Soong, J. L., Horton, A. J., Campbell, E. E., Haddix, M. L., Wall, D. H., & Parton, W. J. (2015) · Nature Geoscience · doi:10.1038/ngeo2520 · Cited by 1,874

Sparse and Compositionally Robust Inference of Microbial Ecological Networks

Kurtz, Z. D., Müller, C. L., Miraldi, E. R., Littman, D. R., Blaser, M. J., & Bonneau, R. A. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004226 · Cited by 1,799

How do mindfulness-based cognitive therapy and mindfulness-based stress reduction improve mental health and wellbeing? A systematic review and meta-analysis of mediation studies

Gu, J., Strauss, C., Bond, R., & Cavanagh, K. (2015) · Clinical Psychology Review · doi:10.1016/j.cpr.2015.01.006 · Cited by 1,712

The irace package: Iterated racing for automatic algorithm configuration

López-Ibáñez, M., Dubois-Lacoste, J., Pérez Cáceres, L., Birattari, M., & Stützle, T. (2016) · Operations Research Perspectives · doi:10.1016/j.orp.2016.09.002 · Cited by 1,761

Ranking treatments in frequentist network meta-analysis works without resampling methods

Rücker, G., & Schwarzer, G. (2015) · BMC Medical Research Methodology · doi:10.1186/s12874-015-0060-8 · Cited by 1,732

stm : An R Package for Structural Topic Models

Roberts, M. E., Stewart, B. M., & Tingley, D. (2019) · Journal of Statistical Software · doi:10.18637/jss.v091.i02 · Cited by 1,714

Productivity limits and potentials of the principles of conservation agriculture

Pittelkow, C. M., Liang, X., Linquist, B. A., van Groenigen, K. J., Lee, J., Lundy, M. E., van Gestel, N., Six, J., Venterea, R. T., & van Kessel, C. (2015) · Nature · doi:10.1038/nature13809 · Cited by 1,688

Methods to estimate the between‐study variance and its uncertainty in meta‐analysis

Veroniki, A. A., Jackson, D., Viechtbauer, W., Bender, R., Bowden, J., Knapp, G., Kuss, O., Higgins, J. P., Langan, D., & Salanti, G. (2016) · Research Synthesis Methods · doi:10.1002/jrsm.1164 · Cited by 1,685

The Colletotrichum gloeosporioides species complex

Weir, B. S., Johnston, P. R., & Damm, U. (2012) · Studies in Mycology · doi:10.3114/sim0011 · Cited by 1,646

Soil carbon debt of 12,000 years of human land use

Sanderman, J., Hengl, T., & Fiske, G. J. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706103114 · Cited by 1,495

An update on the prevalence of cerebral palsy: a systematic review and meta‐analysis

Oskoui, M., Coutinho, F., Dykeman, J., Jetté, N., & Pringsheim, T. (2013) · Developmental Medicine & Child Neurology · doi:10.1111/dmcn.12080 · Cited by 1,512

Positive biodiversity-productivity relationship predominant in global forests

Liang, J., Crowther, T. W., Picard, N., Wiser, S., Zhou, M., Alberti, G., Schulze, E.-D., McGuire, A. D., Bozzato, F., Pretzsch, H., et al. (2016) · Science · doi:10.1126/science.aaf8957 · Cited by 1,490

Exercise and associated dietary extremes impact on gut microbial diversity

Clarke, S. F., Murphy, E. F., O'Sullivan, O., Lucey, A. J., Humphreys, M., Hogan, A., Hayes, P., O'Reilly, M., Jeffery, I. B., Wood-Martin, R., et al. (2014) · Gut · doi:10.1136/gutjnl-2013-306541 · Cited by 1,439

Alien species as a driver of recent extinctions

Bellard, C., Cassey, P., & Blackburn, T. M. (2016) · Biology Letters · doi:10.1098/rsbl.2015.0623 · Cited by 1,424

Robustness of linear mixed-effects models to violations of distributional assumptions

Schielzeth, H., Dingemanse, N. J., Nakagawa, S., Westneat, D. F., Allegue, H., Teplitsky, C., Réale, D., Dochtermann, N. A., Garamszegi, L. Z., & Araya‐Ajoy, Y. G. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13434 · Cited by 1,393

Pollutants bioavailability and toxicological risk from microplastics to marine mussels

Avio, C. G., Gorbi, S., Milan, M., Benedetti, M., Fattorini, D., d'Errico, G., Pauletto, M., Bargelloni, L., & Regoli, F. (2015) · Environmental Pollution · doi:10.1016/j.envpol.2014.12.021 · Cited by 1,413

Exercise interventions for cognitive function in adults older than 50: a systematic review with meta-analysis

Northey, J. M., Cherbuin, N., Pumpa, K. L., Smee, D. J., & Rattray, B. (2018) · British Journal of Sports Medicine · doi:10.1136/bjsports-2016-096587 · Cited by 1,380

Alcohol consumption and site-specific cancer risk: a comprehensive dose–response meta-analysis

Bagnardi, V, Rota, M., Botteri, E., Tramacere, I., Islami, F., Fedirko, V, Scotti, L., Jenab, M., Turati, F., Pasquali, E., et al. (2015) · British Journal of Cancer · doi:10.1038/bjc.2014.579 · Cited by 1,342

Recurrent Neural Networks for Time Series Forecasting: Current status and future directions

Hewamalage, H., Bergmeir, C., & Bandara, K. (2021) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2020.06.008 · Cited by 1,371

SolexaQA: at-a-glance quality assessment of Illumina second-generation sequencing data

Cox, M. P., Peterson, D. A., & Biggs, P. J. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-485 · Cited by 1,357

Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq

Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342

changepoint : An R Package for Changepoint Analysis

Killick, R., & Eckley, I. A. (2014) · Journal of Statistical Software · doi:10.18637/jss.v058.i03 · Cited by 1,330

Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis

Fernandes, A. D., Reid, J. N., Macklaim, J. M., McMurrough, T. A., Edgell, D. R., & Gloor, G. B. (2014) · Microbiome · doi:10.1186/2049-2618-2-15 · Cited by 1,329

A new genomic blueprint of the human gut microbiota

Almeida, A., Mitchell, A. L., Boland, M., Forster, S. C., Gloor, G. B., Tarkowska, A., Lawley, T. D., & Finn, R. D. (2019) · Nature · doi:10.1038/s41586-019-0965-1 · Cited by 1,314

Population estimate of people with clinical Alzheimer's disease and mild cognitive impairment in the United States (2020–2060)

Rajan, K. B., Weuve, J., Barnes, L. L., McAninch, E. A., Wilson, R. S., & Evans, D. A. (2021) · Alzheimer's & Dementia · doi:10.1002/alz.12362 · Cited by 1,268

Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin

Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146

Evaluation metrics and statistical tests for machine learning

Rainio, O., Teuho, J., & Klén, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-56706-x · Cited by 1,338

The State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey

Larson, H. J., de Figueiredo, A., Xiahong, Z., Schulz, W. S., Verger, P., Johnston, I. G., Cook, A. R., & Jones, N. S. (2016) · EBioMedicine · doi:10.1016/j.ebiom.2016.08.042 · Cited by 1,264

SoilGrids1km — Global Soil Information Based on Automated Mapping

Hengl, T., de Jesus, J. M., MacMillan, R. A., Batjes, N. H., Heuvelink, G. B. M., Ribeiro, E., Samuel-Rosa, A., Kempen, B., Leenaars, J. G. B., Walsh, M. G., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105992 · Cited by 1,268

DNA methylation age of blood predicts all-cause mortality in later life

Marioni, R. E., Shah, S., McRae, A. F., Chen, B. H., Colicino, E., Harris, S. E., Gibson, J., Henders, A. K., Redmond, P., Cox, S. R., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0584-6 · Cited by 1,261

Maintenance Therapy With Tumor-Treating Fields Plus Temozolomide vs Temozolomide Alone for Glioblastoma: A Randomized Clinical Trial

Stupp, R., Taillibert, S., Kanner, A. A., Kesari, S., Steinberg, D. M., Toms, S. A., Taylor, L. P., Lieberman, F., Silvani, A., Fink, K. L., et al. (2015) · JAMA · doi:10.1001/jama.2015.16669 · Cited by 1,259

A global quantitative synthesis of local and landscape effects on wild bee pollinators in agroecosystems

Kennedy, C. M., Lonsdorf, E., Neel, M. C., Williams, N. M., Ricketts, T. H., Winfree, R., Bommarco, R., Brittain, C., Burley, A. L., Cariveau, D., et al. (2013) · Ecology Letters · doi:10.1111/ele.12082 · Cited by 1,255

Panel Data Econometrics in R : The plm Package

Croissant, Y., & Millo, G. (2008) · Journal of Statistical Software · doi:10.18637/jss.v027.i02 · Cited by 1,243

Bariatric–metabolic surgery versus conventional medical treatment in obese patients with type 2 diabetes: 5 year follow-up of an open-label, single-centre, randomised controlled trial

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