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Stan : A Probabilistic Programming Language
Open accessAbstract
Stan is a probabilistic programming language for specifying statistical models. A Stan program imperatively defines a log probability function over parameters conditioned on specified data and constants. As of version 2.14.0, Stan provides full Bayesian inference for continuous-variable models through Markov chain Monte Carlo methods such as the No-U-Turn sampler, an adaptive form of Hamiltonian Monte Carlo sampling. Penalized maximum likelihood estimates are calculated using optimization methods such as the limited memory Broyden-Fletcher-Goldfarb-Shanno algorithm. Stan is also a platform for computing log densities and their gradients and Hessians, which can be used in alternative algorithms such as variational Bayes, expectation propagation, and marginal inference using approximate integration. To this end, Stan is set up so that the densities, gradients, and Hessians, along with intermediate quantities of the algorithm such as acceptance probabilities, are easily accessible. Stan can be called from the command line using the cmdstan package, through R using the rstan package, and through Python using the pystan package. All three interfaces support sampling and optimization-based inference with diagnostics and posterior analysis. rstan and pystan also provide access to log probabilities, gradients, Hessians, parameter transforms, and specialized plotting.
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“When dealing with more complex models that go beyond the staple analyses such as t tests, there exist a number of software packages that allow custom coding, such as JAGS (Plummer, 2003) or Stan (Carpenter et al., 2017).”
From The JASP guidelines for conducting and reporting a Bayesian analysis (2021) · cites this paper as Carpenter et al., 2017
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BackgroundMethodInfluentialSelf-citationInference from iterative simulation using multiple sequences
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SERKET: An Architecture for Connecting Stochastic Models to Realize a Large-Scale Cognitive Model
Nakamura, T., Nagai, T., & Taniguchi, T. (2018) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2018.00025 · Cited by 50
“One approach to develop a large-scale cognitive model is the use of probabilistic programming languages (PPLs), which make it easy to construct Bayesian models (Patil et al., 2010; Goodman et al., 2012; Wood et al., 2014; Carpenter et al., 2016; Tran et al., 2016).”
Lee, W., Hiura, L. C., Yang, E., Denney, K. A., Ophir, A. G., & Curley, J. P. (2019) · Hormones and Behavior · doi:10.1016/j.yhbeh.2019.06.015 · Cited by 49
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Temporal microstructure of dyadic social behavior during relationship formation in mice
Lee, W., Fu, J., Bouwman, N., Farago, P., & Curley, J. P. (2019) · PLoS ONE · doi:10.1371/journal.pone.0220596 · Cited by 50
“To test for differences in the duration of each behavior between dominant and subordinate animals across days we used Bayesian fitted in Stan [33] via with the duration of behavior as the outcome variable, individual social status (dominant vs subordinate) and day as fixed factors and pair-id and individual mouse-id a…”
MethodA Bayesian predictive approach for dealing with pseudoreplication
Lazic, S., Mellor, J., Ashby, M. C., & Munafo, M. (2019) · Scientific Reports · doi:10.1038/s41598-020-59384-7 · Cited by 44
Effects of ocean climate on spatiotemporal variation in sea urchin settlement and recruitment
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Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models
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“In addition to the loo R package (Vehtari et al., 2018), for this analysis we use the brms interface (Bürkner, 2017, 2018) to Stan (Carpenter et al., 2017) to generate a Stan program and fit the model.”
MethodHsieh, N., Chen, Z., Rusyn, I., & Chiu, W. A. (2021) · Environmental Health Perspectives · doi:10.1289/ehp7600 · Cited by 50
Graham, A. W., & Sahu, N. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2019 · Cited by 50
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Hezemans, F. H., Wolpe, N., O’Callaghan, C., Ye, R., Rua, C., Jones, P. S., Murley, A. G., Holland, N., Regenthal, R., Tsvetanov, K. A., et al. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010079 · Cited by 51
“The hierarchical Bayesian modelling was implemented in Stan [134].”
MethodLewis, J. M., Mphasa, M., Banda, R. L., Beale, M. A., Heinz, E., Mallewa, J., Jewell, C., Faragher, B. E., Thomson, N. R., & Feasey, N. (2022) · Nature Microbiology · doi:10.1038/s41564-022-01216-7 · Cited by 47
Different components of cognitive-behavioral therapy affect specific cognitive mechanisms
Norbury, A., Hauser, T. U., Fleming, S. M., Dolan, R. J., & Huys, Q. J. M. (2024) · Science Advances · doi:10.1126/sciadv.adk3222 · Cited by 63
“Model parameters were estimated using Markov-Chain Monte Carlo (MCMC) sampling as implemented in Stan 2.21.0 ( 66 ), using RStan 2.21.3 (Stan Development Team, 2021).”
InfluentialExcitation and inhibition in anterior cingulate predict use of past experiences
Scholl, J., Kolling, N., Nelissen, N., Stagg, C. J., Harmer, C. J., & Rushworth, M. F. S. (2017) · eLife · doi:10.7554/elife.20365 · Cited by 49
“All models were fit using Bayesian parameter estimation (Lee and Wagenmakers, 2014) as implemented in Stan (Carpenter et al., 2016).”
MethodVirtually Being Lenin Enhances Presence and Engagement in a Scene From the Russian Revolution
Slater, M., Navarro, X., Valenzuela, J., Oliva, R., Beacco, A., Thorn, J. T., & Watson, Z. (2018) · Frontiers in Robotics and AI · doi:10.3389/frobt.2018.00091 · Cited by 48
“For the Bayesian analysis we used the Stan system (Carpenter et al., 2016) (http://mc-stan.”
MethodTransverse kinematics of the Galactic bar-bulge from VVV and Gaia
Sanders, J. L., Smith, L. C., Evans, N. W., & Lucas, P. W. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz1630 · Cited by 49
“Large-scale photometric studies (Optical Gravitational Lensing Experiment [OGLE], Two Micron All-Sky Survey [2MASS], UKIRT Infrared Deep Sky Survey [UKIDSS], Vista Variables in the Via Lactea [VVV]) of the red giants towards the Galactic centre have produced a coherent picture of the bulge as an elongated triaxial bar…”
BackgroundTranslation elongation and mRNA stability are coupled through the ribosomal A-site
Hanson, G. K., Alhusaini, N., Morris, N. J., Sweet, T. J., & Coller, J. (2018) · RNA · doi:10.1261/rna.066787.118 · Cited by 51
“All models in Stan were run with 1000 warmup samples and 1000 acquisition samples, across four separate chains.”
MethodInfluentialBowers, W. H., Mercer, J. J., Pleasants, M. S., et al. (2020) · Hydrology and earth system sciences · doi:10.5194/hess-24-4045-2020 · Cited by 49
Rubin, D., & Heitlauf, J. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/ab7a16 · Cited by 49
“Software: Matplotlib (Hunter 2007), Numpy (van der Walt et al. 2011), pystan (https://doi.org/10.5281/zenodo.598257), Python, SciPy (Jones et al. 2001), Stan (Carpenter et al. 2017)”
MethodInfluentialClimate warming restructures an aquatic food web over 28 years
Tanentzap, A. J., Morabito, G., Volta, P., Rogora, M., Yan, N. D., & Manca, M. (2020) · Global Change Biology · doi:10.1111/gcb.15347 · Cited by 49
“19 from R (Carpenter et al., 2017).”
Large effects of brief meditation intervention on EEG spectra in meditation novices
Stapleton, P., Dispenza, J., McGill, S., Sabot, D., Peach, M., & Raynor, D. (2020) · IBRO Reports · doi:10.1016/j.ibror.2020.10.006 · Cited by 56
Redistribution of salmon populations in the northeast Pacific ocean in response to climate
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Ensuring identifiability in hierarchical mixed effects Bayesian models
Ogle, K., & Barber, J. J. (2020) · Ecological Applications · doi:10.1002/eap.2159 · Cited by 48
Inference of the chromospheric magnetic field orientation in the Ca ii 8542 Å line fibrils
Ramos, A. A., Rodríguez, J. D. L. C., González, M. J. M., & Socas‐Navarro, H. (2017) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201629755 · Cited by 50
Measurement uncertainty in energy monitoring: Present state of the art
Carstens, H., Xia, X., & Yadavalli, V. S. S. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.10.006 · Cited by 48
Associability-modulated loss learning is increased in posttraumatic stress disorder
Brown, V. M., Zhu, L., Wang, J. M., Frueh, B. C., King‐Casas, B., & Chiu, P. H. (2018) · eLife · doi:10.7554/elife.30150 · Cited by 50
“Estimation was done using Hamiltonian Markov Chain Monte Carlo using Stan (Carpenter, 2017) in R. Group level parameters were specified as normally distributed, with an inverse logit transformation on parameters constrained to be between 0 and 1 (learning rate and associability weight) and a lower bound of 0 on parame…”
Colombo, M., McGurnaghan, S. J., Blackbourn, L. A. K., et al. (2020) · Diabetologia · doi:10.1007/s00125-019-05081-8 · Cited by 48
Haq, M. T., Zlatkovic, M., & Ksaibati, K. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105693 · Cited by 48
A model for naturalistic glance behavior around Tesla Autopilot disengagements
Morando, A., Gershon, P., Mehler, B. L., & Reimer, B. (2021) · Accident Analysis & Prevention · doi:10.1016/j.aap.2021.106348 · Cited by 48
A keystone gene underlies the persistence of an experimental food web
Barbour, M. A., Kliebenstein, D. J., & Bascompte, J. (2022) · Science · doi:10.1126/science.abf2232 · Cited by 48
Albuquerque, A. M., Eckert, I. D. C., Tramujas, L., Butler‐Laporte, G., McDonald, E. G., Brophy, J. M., & Lee, T. C. (2022) · Clinical Microbiology and Infection · doi:10.1016/j.cmi.2022.07.008 · Cited by 49
“Models were fitted using Stan [34] through the R package brms (R Foundation for Statistical Computing, Vienna, Austria) [35].”
Effect of lysergic acid diethylamide (LSD) on reinforcement learning in humans
Kanen, J. W., Luo, Q., Kandroodi, M. R., Cardinal, R. N., Robbins, T. W., Nutt, D. J., Carhart-Harris, R. L., & den Ouden, H. E. M. (2022) · Psychological Medicine · doi:10.1017/s0033291722002963 · Cited by 47
“We fitted three RL models to the behavioural data using a hierarchical Bayesian method, via Hamiltonian Markov chain Monte Carlo sampling implemented in Stan 2.17.2 (Carpenter et al., 2017).”
MethodInfluentialOfficer bias, over-patrolling and ethnic disparities in stop and search
Vomfell, L., & Stewart, N. (2021) · Nature Human Behaviour · doi:10.1038/s41562-020-01029-w · Cited by 89
Pavlidis, P., Tsakmaki, A., Treveil, A., Li, K., Cozzetto, D., Yang, F., Niazi, U. H. K., Hayee, B., Saqi, M. A. S., Friedman, J. R., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111439 · Cited by 55
A host–microbiota interactome reveals extensive transkingdom connectivity
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Rodríguez‐Aranda, C., Mittner, M., & Vasylenko, O. (2016) · Perceptual and Motor Skills · doi:10.1177/0031512516628370 · Cited by 47
A descriptive marker gene approach to single-cell pseudotime inference
Campbell, K. R., & Yau, C. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty498 · Cited by 48
Using language input and lexical processing to predict vocabulary size
Mahr, T., & Edwards, J. (2018) · Developmental Science · doi:10.1111/desc.12685 · Cited by 49
“(EVT-2 Standard) 118.4 14.2 81–160 Rec.”
InfluentialBayesian Statistics in Archaeology
Otárola‐Castillo, E., & Torquato, M. G. (2018) · Annual Review of Anthropology · doi:10.1146/annurev-anthro-102317-045834 · Cited by 50
Multiple Perspectives on Inference for Two Simple Statistical Scenarios
van Dongen, N. N. N., van Doorn, J. B., Gronau, Q. F., van Ravenzwaaij, D., Hoekstra, R., Haucke, M. N., Lakens, D., Hennig, C. M., Morey, R. D., Homer, S., et al. (2019) · The American Statistician · doi:10.1080/00031305.2019.1565553 · Cited by 49
“Inference was conducted with Stan (Carpenter et al., 2017; Stan Development Team, 2016) and the bridgesampling package (Gronau et al.”
Self-citationMetabotyping as a Stopover in Genome-to-Phenome Mapping
Handakumbura, P., Stanfill, B., Rivas‐Ubach, A., Fortin, D., Vogel, J. P., & Jansson, C. (2019) · Scientific Reports · doi:10.1038/s41598-019-38483-0 · Cited by 48
Carr, G. J., & Belanger, S. E. (2019) · Environmental Toxicology and Chemistry · doi:10.1002/etc.4445 · Cited by 47
“Bayesian analysis was performed through the rstan package in R, which interfaces to the STAN programming language for Bayesian methods (Carpenter et al. 2017).”
Unbiased Hamiltonian Monte Carlo with couplings
Heng, J., & Jacob, P. E. (2018) · Biometrika · doi:10.1093/biomet/asy074 · Cited by 46
“In practice, Hamiltonian Monte Carlo is at the core of the No-U-Turn sampler [Hoffman and Gelman, 2014] which is implemented in the software Stan [Carpenter et al., 2016].”
MethodThe Barker Proposal: Combining Robustness and Efficiency in Gradient-Based MCMC
Livingstone, S., & Zanella, G. (2022) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12482 · Cited by 47
“t the posterior distribution under consideration is non-trivial, we also attempted to t the same model using the No-U-Turn Sampler variant of Hamiltonian Monte Carlo implemented in the Stan software [Carpenter et al., 2017], a state-of-the-art gradient-based MCMC implementation. Full details and results are provided i…”
MethodSeverini, A. D., Wasson, A., Evans, J. R., Richards, R. A., & Watt, M. (2020) · Field Crops Research · doi:10.1016/j.fcr.2020.107870 · Cited by 47
A Bayesian neural network for toxicity prediction
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Extreme and variable torpor among high-elevation Andean hummingbird species
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Bayesian Nonparametric Customer Base Analysis with Model-Based Visualizations
Dew, R., & Ansari, A. (2018) · Marketing Science · doi:10.1287/mksc.2017.1050 · Cited by 52
“In particular, we use the No U-Turn Sampling (NUTS) variant of HMC as implemented in the Stan probabilistic programming language (Ho ↵ man and Gelman, 2014; Carpenter et al., 2016).”
Increased mortality of tropical tree seedlings during the extreme 2015–16 El Niño
Browne, L., Markesteijn, L., Engelbrecht, B. M. J., Jones, F. A., Lewis, O. T., Manzané‐Pinzón, E., Wright, S. J., & Comita, L. S. (2021) · Global Change Biology · doi:10.1111/gcb.15809 · Cited by 50
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NT‐proBNP Qualifies as a Surrogate for Clinical End Points in Heart Failure
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Tracking an invasion front with environmental DNA
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Multispecies coexistence in fragmented landscapes
Luo, M., Wang, S., Saavedra, S., Ebert, D., & Altermatt, F. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2201503119 · Cited by 49
Performance persistence in the presence of higher‐order resources
Wibbens, P. D. (2018) · Strategic Management Journal · doi:10.1002/smj.2979 · Cited by 46
PyBioNetFit and the Biological Property Specification Language
Mitra, E. D., Suderman, R., Colvin, J. C., Ionkov, A., Hu, A., Sauro, H. M., Posner, R. G., & Hlavacek, W. S. (2019) · iScience · doi:10.1016/j.isci.2019.08.045 · Cited by 47
Birds invest wingbeats to keep a steady head and reap the ultimate benefits of flying together
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Saber, M., Giordano, K. R., Hur, Y., Ortiz, J. B., Morrison, H., Godbout, J. P., Murphy, S. M., Lifshitz, J., & Rowe, R. K. (2020) · European Journal of Neuroscience · doi:10.1111/ejn.14611 · Cited by 46
“6.0) (Gelman, Lee, & Guo, 2015; Bürkner, 2017; Carpenter et al., 2017; Bürkner, 2018; Bürkner, 2019; R Core Team, 2019).”
InfluentialNeural hierarchical models of ecological populations
Joseph, M. B. (2020) · Ecology Letters · doi:10.1111/ele.13462 · Cited by 53
Karno, M. P., Rawson, R. A., Rogers, B., Spear, S. E., Grella, C. E., Mooney, L. J., Saitz, R., Kagan, B. L., & Glasner, S. V. (2020) · Addiction · doi:10.1111/add.15114 · Cited by 52
Litzow, M. A., Hunsicker, M. E., Ward, E. J., Anderson, S. C., Gao, J., Zador, S. G., Batten, S. D., Dressel, S. C., Duffy‐Anderson, J. T., Fergusson, E. A., et al. (2020) · Progress In Oceanography · doi:10.1016/j.pocean.2020.102393 · Cited by 48
Bennett, D., Sutcliffe, K., Tan, N. P., Smillie, L. D., & Bode, S. (2020) · Journal of Experimental Psychology General · doi:10.1037/xge0000966 · Cited by 43
Vibrio cholerae O1 transmission in Bangladesh: insights from a nationally representative serosurvey
Azman, A. S., Lauer, S. A., Bhuiyan, T. R., Luquero, F. J., Leung, D. T., Hegde, S. T., Harris, J. B., Paul, K. K., Khaton, F., Ferdous, J., et al. (2020) · The Lancet Microbe · doi:10.1016/s2666-5247(20)30141-5 · Cited by 38
“We ran 5,000 iterations (4 chains with 1,500 iterations each with 250 for warm-up) and assessed convergence visually and using the R-hat statistic.[2,3] S1.”
MethodTrial and error: A hierarchical modeling approach to test-retest reliability
Chen, G., Pine, D. S., Brotman, M. A., Smith, A. R., Cox, R. W., & Haller, S. P. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118647 · Cited by 49
“The two BML models, one for a single condition through (15) (or (17)) and the other for a contrast through (16) (or (18)), are implemented in the program TRR through Markov Chain Monte Carlo (MCMC) simulations using Stan (Carpenter et al., 2017) and the R package brms (Bürkner, 2017).”
MethodInfluentialBajzik, V., DeBerg, H. A., Garabatos, N., Rust, B. J., O’Brien, K., Nguyen, Q., O’Rourke, C., Smith, A., Walker, A. H., Quinn, C., et al. (2022) · Allergy · doi:10.1111/all.15276 · Cited by 46
Lower vs Higher Fluid Volumes in Adult Patients With Sepsis
Sivapalan, P., Ellekjaer, K. L., Jessen, M. K., Meyhoff, T. S., Cronhjort, M., Hjortrup, P. B., Wetterslev, J., Granholm, A., Møller, M. H., & Perner, A. (2023) · CHEST · doi:10.1016/j.chest.2023.04.036 · Cited by 45
Statistical bias control in typology
Naranjo, M. G., & Becker, L. (2021) · Linguistic Typology · doi:10.1515/lingty-2021-0002 · Cited by 45
“All models were fitted with Stan (Carpenter et al. 2017) using the brms package (Bürkner 2018) in R.”
MethodPatterson, K., Davey, R., Keegan, R. J., Kunstler, B., Woodward, A. P., & Freene, N. (2022) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-022-01319-8 · Cited by 45
“Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, et al. Stan: A probabilistic programming language.”
MethodReproductive inequality in humans and other mammals
Ross, C. T., Hooper, P. L., Smith, J. E., Jaeggi, A. V., Smith, E. A., Sergey Gavrilets, B., Zohora, F. T., Ziker, J. P., Xygalatas, D., Wroblewski, E. E., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2220124120 · Cited by 47
Neural-network quantum states for many-body physics
Medvidović, M., & Moreno, J. R. (2024) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-024-05311-y · Cited by 73
“Another option for continuous systems is Hamiltonian Monte Carlo (HMC) [49–51], offering a systematic way of making large steps in proposals while still keeping acceptance probabilities high, unlike RWM.”
Andreae, M. H., White, R. S., Chen, K. Y., Nair, S., Hall, C. B., & Shaparin, N. (2016) · Pain Medicine · doi:10.1093/pm/pnw161 · Cited by 45
Vandenberg‐Rodes, A., Moftakhari, H. R., AghaKouchak, A., Shahbaba, B., Sanders, B. F., & Matthew, R. A. (2016) · Journal of Geophysical Research Oceans · doi:10.1002/2016jc012084 · Cited by 47
A Pairwise Comparison Framework for Fast, Flexible, and Reliable Human Coding of Political Texts
CARLSON, D., & Montgomery, J. (2017) · American Political Science Review · doi:10.1017/s0003055417000302 · Cited by 45
“There is a related function that fits the hierarchical Stan model.”
MethodInfluentialA factor graph approach to automated design of Bayesian signal processing algorithms
Cox, M., van de Laar, T., & de Vries, B. (2018) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2018.11.002 · Cited by 45
“More recently, techniques like black-box variational inference (BBVI) [3] have been added to the mix, for example in the popular probabilistic programming packages Stan [4] and Edward [5].”
MethodMovement reveals reproductive tactics in male elephants
Taylor, L. A., Vollrath, F. W., Lambert, B. C., Lunn, D., Douglas‐Hamilton, I., & Wittemyer, G. (2019) · Journal of Animal Ecology · doi:10.1111/1365-2656.13035 · Cited by 45
“RStan: The R interface to Stan.”
MethodInfluentialKain, M. P., & Bolker, B. M. (2019) · Parasites & Vectors · doi:10.1186/s13071-019-3656-8 · Cited by 45
Transcriptional Profiling of the Murine Airway Response to Acute Ozone Exposure
Tovar, A., Smith, G. J., Thomas, J. M., Crouse, W. L., Harkema, J. R., & Kelada, S. N. P. (2019) · Toxicological Sciences · doi:10.1093/toxsci/kfz219 · Cited by 41
Mediterranean marine protected areas have higher biodiversity via increased evenness, not abundance
Blowes, S. A., Chase, J. M., FRANCO, A. D., Frid, O., Gotelli, N. J., Guidetti, P., Knight, T. M., May, F., McGlinn, D. J., Micheli, F., et al. (2020) · Journal of Applied Ecology · doi:10.1111/1365-2664.13549 · Cited by 46
Genetic variance for behavioural ‘predictability’ of stress response
Prentice, P. M., Houslay, T. M., Martin, J. G. A., & Wilson, A. J. (2020) · Journal of Evolutionary Biology · doi:10.1111/jeb.13601 · Cited by 45
“Both DHGLM and DHAM were fitted in a Bayesian framework using Stan (Carpenter et al., 2017) within R version 3.4.1 (http:// www.r-proje ct.org/) via the package RStan version 2.18.0 (Stan Development Team, 2018).”
MethodInfluentialThe effects of elaboration on working memory and long-term memory across age
Bartsch, L. M., & Oberauer, K. (2021) · Journal of Memory and Language · doi:10.1016/j.jml.2020.104215 · Cited by 49
Reproduction of East-African bats may guide risk mitigation for coronavirus spillover
Montecino‐Latorre, D., Goldstein, T., Gilardi, K. V. K., Wolking, D. J., VanWormer, E., Kazwala, R. R., Ssebide, B., Nziza, J., Sijali, Z., Cranfield, M. R., et al. (2020) · One Health Outlook · doi:10.1186/s42522-019-0008-8 · Cited by 45
Effects of lake warming on the seasonal risk of toxic cyanobacteria exposure
Hayes, N. M., Haig, H. A., Simpson, G. L., & Leavitt, P. R. (2020) · Limnology and Oceanography Letters · doi:10.1002/lol2.10164 · Cited by 45
“Models were estimated in a Bayesian framework using the brms package (version 1.10.2; Bürkner 2017) for R (version 3.6.3; R Core Team 2019), using the probabilistic programming language Stan (Carpenter et al. 2017) and Hamiltonian Monte Carlo with the No U-Turn sampler (NUTS; Hoffman and Gelman 2014).”
MethodInfluentialTuri, Z., Mittner, M., Lehr, A., Bürger, H., Antal, A., & Paulus, W. (2020) · eNeuro · doi:10.1523/eneuro.0126-20.2020 · Cited by 45
“The described computational models were implemented using the R-package rstan (Stan Development Team, 2018).”
MethodŠí́p, V., Hashemi, M., Vattikonda, A. N., Woodman, M., Wang, H., Makhalova, J., Villalon, S. M., Guye, M., Bartoloméi, F., & Jirsa, V. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008689 · Cited by 45
“The implementation in Stan software was used [82].”
MethodMeasuring and Assessing Typing Skills in Writing Research
Waes, L. V., Leijten, M., Roeser, J., Olive, T., & Grabowski, J. (2021) · Journal of Writing Research · doi:10.17239/jowr-2021.13.01.04 · Cited by 66
Kinetochore life histories reveal an Aurora-B-dependent error correction mechanism in anaphase
Sen, O., Harrison, J. U., Burroughs, N. J., & McAinsh, A. D. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.10.007 · Cited by 45
State-level tracking of COVID-19 in the United StatesPreprint
Unwin, H. J. T., Mishra, S., Bradley, V. C., Gandy, A., Mellan, T. A., Coupland, H., Ish-Horowicz, J. S., Vollmer, M., Whittaker, C., Filippi, S. L., et al. (2020) · medRxiv · doi:10.1101/2020.07.13.20152355 · Cited by 45
Jirasek, F., Bamler, R., Fellenz, S., Bortz, M., Kloft, M., Mandt, S., & Hasse, H. (2022) · Chemical Science · doi:10.1039/d1sc07210b · Cited by 44
“Details on the random data split, the model training (including the source code to run the model in Stan), and the hyperparameter selection are given in the ESI.”
MethodMonod, M., Brizzi, A., Galiwango, R. M., Ssekubugu, R., Chen, Y., Xi, X., Kankaka, E. N., Ssempijja, V., Abeler‐Dörner, L., Akullian, A. N., et al. (2023) · Nature Microbiology · doi:10.1038/s41564-023-01530-8 · Cited by 44
“Model (7–10) was fitted with Rstan (version 2.21.0), using Stan’s adaptive HMC sampler 62 with 4 chains for 3,500 iterations including 500 warm-up iterations.”
InfluentialRobust mixture modeling reveals category-free selectivity in reward region neuronal ensembles
Blanchard, T. C., Piantadosi, S. T., & Hayden, B. Y. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00808.2017 · Cited by 44
Metadata
- Authors
- Carpenter, B., Gelman, A., Hoffman, M. D., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M., Guo, J., Li, P., & Riddell, A.
- Affiliations
- Columbia University (US)
- Adobe Systems (United States) (US)
- York University (CA)
- Indiana University (US)
- ORCID
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2017
- Volume
- 76
- Issue
- 1
- Pages
- 1-32
- ISSN
- 1548-7660 (ISSN-L)
- PMC
- PMC9788645
- OpenAlex
- W2577537660
- Semantic Scholar
- f8110d834fcc9a3a1c4416d4b165984219fe26f4
- Cited by
- 7,754
- References
- 49
- Citations by year
- 2026: 672 · 2025: 811 · 2024: 811 · 2023: 835 · 2022: 917 · 2021: 1127 · 2020: 970 · 2019: 752 · 2018: 516 · 2017: 253
- FWCI
- 474.30
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Journal of Statistical Software (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Journal of Systems and Software
- Grantee Submission
- Funders
- National Science Foundation (0934516, 1205516)
- U.S. Department of Energy (DESC0002099)
- Harvard University
- National Institutes of Health (1g20rr030893-01)
- Institute of Education Sciences (R305D090006-09A, R305D090006)
- National Center for Research Resources (G20RR030893)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Physical Sciences › Mathematics › Statistics and Probability › Data Analysis with R
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- Stan, probabilistic programming languages, Bayesian inference, Markov chain Monte Carlo, No-U-Turn Sampler, Hamiltonian Monte Carlo, L-BFGS optimization, penalized likelihood, variational inference, expectation propagation, marginal inference, R interface, Python interface, posterior analysis
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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.
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