Citations

Citations

cite & be cited

Let us know if you write a paper citing Stan and we can cite it here.

How to Cite Stan

We appreciate citations for the Stan software because it lets us find out what people have been doing with Stan and motivate further grant funding. Here are citations for the manual, the core libraries, and the interfaces.

Language Manual

  • Stan Development Team. 2016. Stan Modeling Language Users Guide and Reference Manual, Version 2.14.0.   http://mc-stan.org

Stan Math Library

  • Stan Development Team. 2016. The Stan Math Library, Version 2.14.0.   http://mc-stan.org

Stan C++ Library

  • Stan Development Team. 2016. The Stan C++ Library, Version 2.14.0.   http://mc-stan.org

RStan

  • Stan Development Team. 2016. RStan: the R interface to Stan. R package version 2.14.1.   http://mc-stan.org

PyStan

  • Stan Development Team. 2016. PyStan: the Python interface to Stan, Version 2.14.0.0.   http://mc-stan.org

CmdStan

  • Stan Development Team. 2016. CmdStan: the command-line interface to Stan, Version 2.14.0.   http://mc-stan.org

MatlabStan

  • Stan Development Team. 2016. MatlabStan: the MATLAB interface to Stan.   http://mc-stan.org

Stan.jl

  • Stan Development Team. 2016. Stan.jl: the Julia interface to Stan.   http://mc-stan.org

StataStan

  • Stan Development Team. 2016. StataStan: the Stata interface to Stan.   http://mc-stan.org

RStanArm

  • Stan Development Team. 2016. RStanArm: Bayesian applied regression modeling via Stan. R package version 2.13.1.   http://mc-stan.org

ShinyStan

  • Stan Development Team. 2016. ShinyStan: Interactive Visual and Numerical Diagnostics and Posterior Analysis for Bayesian Models. R package version 2.2.1.   http://mc-stan.org

Books about Stan

  • Richard McElreath. 2016. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Chapman & Hall/CRC Press.

  • John K. Kruschke. 2015. Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan. Academic Press. [chapter on Stan]

  • Fränzi Korner-Nievergelt, Tobias Roth, Stefanie von Felten, Jérôme Guélat, Bettina Almasi, and Pius Korner-Nievergelt. 2015. Bayesian Data Analysis in Ecology Using Linear Models with R, BUGS, and Stan. Academic Press. [chapter on Stan]

  • Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. 2013. Bayesian Data Analysis, 3rd Edition. Chapman & Hall/CRC Press, London. [appendix on Stan]

Books with Examples Translated to Stan

Code at: Stan example models repo

  • Lunn, D., Jackson, C., Best, N., Thomas, A., and Spiegelhalter, D. 2013. The BUGS Book: A Practical Introduction to Bayesian Analysis. Chapman & Hall/CRC Press.

  • Eric-Jan Wagenmakers and Michael D. Lee. 2014. Bayesian Cognitive Modeling: A Practical Course. Cambridge Univesity Press.

  • Marc Kéry and Michael Schaub. 2011. Bayesian Population Analysis using WinBUGS: A hierarchical perspective. Academic Press. [chapters 3–10]

  • Andrew Gelman and Jennifer Hill. 2009. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press.

Papers about Stan

  • Bob Carpenter, Andrew Gelman, Matthew D. Hoffman, Daniel Lee, Ben Goodrich, Michael Betancourt, Marcus Brubaker, Jiqiang Guo, Peter Li, and Allen Riddell. 2017. Stan: A probabilistic programming language. Journal of Statistical Software 76(1). DOI 10.18637/jss.v076.i01

  • Michael Betancourt. 2017. A Conceptual Introduction to Hamiltonian Monte Carlo. arXiv:1701.02434.

  • Cole C. Monnahan, James T. Thorson, and Trevor A. Branch. 2016. Faster estimation of Bayesian models in ecology using Hamiltonian Monte Carlo. Methods in Ecology and Evolution.

  • Robert L. Grant, Daniel C. Furr, Bob Carpenter, and Andrew Gelman. 2016. Fitting Bayesian item response models in Stata and Stan. arXiv 1601.03443.

  • Michael J. Betancourt. 2016. Identifying the Optimal Integration Time in Hamiltonian Monte Carlo. arXiv:1601.00225.

  • Andrew Gelman, Daniel Lee, and Jiqiang Guo. 2015. Stan: A probabilistic programming language for Bayesian inference and optimization. Journal of Education and Behavioral Statistics. 40(5):530–543.

  • Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman and David M. Blei. 2015. Automatic Variational Inference in Stan, NIPS.

  • Bob Carpenter, Matthew D. Hoffman, Marcus Brubaker, Daniel Lee, Peter Li, and Michael J. Betancourt. 2015. The Stan Math Library: Reverse-Mode Automatic Differentiation in C++. arXiv 1509.07164.

  • Matthew D. Hoffman and Andrew Gelman. 2014. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. Journal of Machine Learning Research. 15(Apr):1593–1623.

  • Michael J. Betancourt. 2013. A General Metric for Riemannian Manifold Hamiltonian Monte Carlo. arXiv 1212.4693.

  • Michael J. Betancourt. 2013. Generalizing the No-U-Turn Sampler to Riemannian Manifolds. arXiv 1304.1920.

  • Matthew D. Hoffman and Andrew Gelman. 2011. The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo. arXiv 1111:4246.

Papers Using Stan

  • Luc E. Coffeng , Cornelus C. Hermsen, Robert W. Sauerwein, Sake J. de Vlas. 2017. The Power of Malaria Vaccine Trials Using Controlled Human Malaria Infection. PLoS Computational Biology 13(1): e1005255.

  • Mahar, R. K., Carlin, J. B., Ranganathan, S., Ponsonby, A. L., Vuillermin, P., & Vukcevic, D. 2016. Bayesian modelling of repeated measures lung function data from multiple-breath washout tests. arXiv:1612.08617.

  • Smyl, S. and K. Kuber. 2016. Data preprocessing and augmentation for multiple short time series forecasting with recurrent neural networks. 36th International Symposium on Forecasting. Santander.

  • Matthew B. Espe, Kenneth G. Cassman, Haishun Yang, Nicolas Guilpart, Patricio Grassini, Justin Van Wart, Merle Anders, Donn Beighley, Dustin Harrell, Steve Linscombe, Kent McKenzie, Randall Mutters, Lloyd T. Wilson, and Bruce A. Linquist. 2016. Yield gap analysis of US rice production systems shows opportunities for improvement. Field Crops Research 196:276–283.

  • V. K. Jirsa, T. Proix, D. Perdikis, M. M. Woodman, H. Wang, J. Gonzalez-Martinez, C. Bernard, C. Bénar, M. Guye, P. Chauvel, F. Bartolomei. 2016. The virtual epileptic patient: individualized whole-brain models of epilepsy spread. NeuroImage.

  • Burchfield, Emily K., and Jonathan M. Gilligan. 2016. Dynamics of individual and collective agricultural adaptation to water scarcity. Proceedings of the 2016 Winter Simulation Conference. IEEE Press.

  • Nay, John J., Martin Van der Linden, and Jonathan M. Gilligan. Betting and belief: prediction markets and attribution of climate change. Proceedings of the 2016 Winter Simulation Conference. IEEE Press. Also available as arXiv 1603.08961.

  • Hélène Peltier, Matthieu Authier, Rob Deaville, Willy Dabin, Paul D. Jepson, Olivier van Canneyt, Pierre Daniel, Vincent Ridoux. 2016. Small cetacean bycatch as estimated from stranding schemes: The common dolphin case in the northeast Atlantic. Environmental Science & Policy 63:7–18.

  • Gerrit Kentner and Shravan Vasishth. 2016. Prosodic focus marking in silent reading: Effects of discourse context and rhythm. 7(319). Frontiers in Psychology.

  • Molood Sadat Safavi, Samar Husain, and Shravan Vasishth. 2016. Dependency resolution difficulty increases with distance in Persian separable complex predicates: Implications for expectation and memory-based accounts. Frontiers in Psychology 7 (Special Issue on Encoding and Navigating Linguistic Representations in Memory)

  • Wallis, Thomas S. A., Matthias Bethge, and Felix A. Wichmann. 2016. Testing models of peripheral encoding using metamerism in an oddity paradigm. Journal of Vision 16(2).

  • Nicenboim, Bruno, Pavel Logačev, Carolina Gattei, and Shravan Vasishth. 2016. When high-capacity readers slow down and low-capacity readers speed up: Working memory differences in unbounded dependencies. Frontiers in Psychology 7(280).

  • Shantz, Andrew A., Lemoine, Nathan P., and Burkepile, Deron E. 2016. Nutrient loading alters the performance of key nutrient exchange mutualisms. Ecology Letters 19(1):20–28.

  • Charpentier, Emmanuel and Looten, Vincent and Fahlgren, Björn and Barna, Alexandre and Guillevin, Loïc. 2016. Meta-analytic estimation of measurement variability and assessment of its impact on decision-making: the case of perioperative haemoglobin concentration monitoring. BMC Medical Research Methodology. 16(1):1–14.

  • Jonathan M. Fawcett and Michael A. Lawrence and Tracy L. Taylor. 2016. The representational consequences of intentional forgetting: Impairments to both the probability and fidelity of long-term memory. Journal Of Experimental Psychology: General 145(1):56–81.

  • Nicenboim, Bruno and Vasishth, Shravan. 2016. Statistical methods for linguistics research: Foundational Ideas—Part II. Language and Linguistics Compass 10(11):591–613.

  • P. Almaraz. 2015. Bordeaux wine quality and climate fluctuations during the last century: changing temperatures and changing industry. Climate Research 64:187–199.

  • Adler, Avraham. 2015. Estimating the parameter risk of a loss ratio distribution—revisited. Variance 9(1):114–139.

  • Samar Husain, Shravan Vasishth, and Narayanan Srinivasan. 2015. Integration and prediction difficulty in Hindi sentence comprehension: Evidence from an eye-tracking corpus. Journal of Eye Movement Research 8(2):1–12.

  • Dario Paape and Shravan Vasishth. 2015. Local coherence and preemptive digging-in effects in German. Language and Speech.

  • Douglas Bates, Reinhold Kliegl, Shravan Vasishth, and Harald Baayen. 2015. Parsimonious mixed models. arXiv 1506.04967.

  • Logačev, Pavel and Sharavn Vasishth. 2015. Understanding underspecification: A comparison of two computational implementations. Quarterly Journal of Experimental Psychology 69(5):996-1012.

  • Wallis, Thomas S. A., Michael Dorr and Peter J. Bex. 2015. Sensitivity to gaze-contingent contrast increments in naturalistic movies. Journal of Vision 15(8).

  • Notebaert, Lies and Stijn Masschelein, Bridget Wright, and Colin MacLeod. 2015. To risk or not to risk: anxiety and the calibration between risk perception and danger mitigation. Journal of Experimental Psychology: Learning, Memory, and Cognition. ePub.

  • Maxwell B. Joseph, William E. Stutz, and Pieter T. J. Johnson. 2015. Multilevel models for the distribution of hosts and symbionts. PeerJ PrePrints 3:e1876.

  • Coffeng, Luc and Bakker, Roel and Montresor, Antonio and de Vlas, Sake. 2015. Feasibility of controlling hookworm infection through preventive chemotherapy: a simulation study using the individual-based WORMSIM modelling framework, Parasites & Vectors 8(1):541.

  • Lemoine, Nathan P and Shue, Jessica and Verrico, Brittany and Erickson, David and Kress, W John and Parker, John D. 2015. Phylogenetic relatedness and leaf functional traits, not introduced status, influence community assembly. Ecology 96(10):2605–2612.

  • Peter D. Smits. 2015, Expected time-invariant effects of biological traits on mammal species duration, PNAS.

  • Slawek Smyl and Qinqin Zhang. 2015. Fitting and Extending Exponential Smoothing Models with Stan. In Proceedings of the 35th International Symposium on Forecasting (ISF 2015).

  • Bigorgne, Emilie and Custer, Thomas W. and Dummer, Paul M. and Erickson, Richard A. and Karouna-Renier, Natalie and Schultz, Sandra and Custer, Christine M. and Thogmartin, Wayne E. and Matson, Cole W., 2015. Chromosomal damage and EROD induction in tree swallows (Tachycineta bicolor) along the Upper Mississippi River, Minnesota, USA, Ecotoxicology 1–12.

  • Scott Steinschneider and Upmanu Lall. 2015. A hierarchical Bayesian regional model for nonstationary precipitation extremes in Northern California conditioned on tropical moisture exports. Water Resources Research 51.

  • Stefan L. Frank and Thijs Trompenaars and Shravan Vasishth. 2015. Cross-linguistic differences in processing double-embedded relative clauses: Working-memory constraints or language statistics? Cognitive Science.

  • Lemoine, Nathan P and Burkepile, Deron E and Parker, John D. 2014. Variable effects of temperature on insect herbivory. PeerJ 2:e376.

  • Philip Hofmeister and Shravan Vasishth. 2014. Distinctiveness and encoding effects in online sentence comprehension. Frontiers in Psychology, (Special issue on Encoding and Navigating Linguistic Representations in Memory) 5(1237).

  • Thorson, James T. and Jensen, Olaf P. and Zipkin, Elise F. 2014. How variable is recruitment for exploited marine fishes? A hierarchical model for testing life history theory. Canadian Journal of Fisheries and Aquatic Sciences 71(7):973–983.

  • Bret A Beheim and Calvin Thigpen and Richard McElreath. 2014. Strategic Social Learning And The Population Dynamics Of Human Behavior: The Game Of Go. Evolution and Human Behavior 35:351–357.

  • Weber, Sebastian and Carpenter, Bob and Lee, Daniel and Bois, Frederic Y. and Gelman, Andrew and Racine, Amy. 2014. Drug Disease Model with applications for incorporating longitudinal data summaries from publications, PAGE — Abstracts of the Annual Meeting of the Population Approach Group in Europe 23.

  • Authier, Matthieu and Peltier, Hélène and Dorémus, Ghislain and Dabin, Willy and Van Canneyt, Olivier and Ridoux, Vincent. 2014. How much are stranding records affected by variation in reporting rates? A case study of small delphinids in the Bay of Biscay, Biodiversity and Conservation 23(10):2591–2612.

  • Peltola and Havulinna and Salomaa and Vehtari. 2014. Hierarchical Bayesian Survival Analysis and Projective Covariate Selection in Cardiovascular Event Risk Prediction. Proceedings of the Eleventh UAI Bayesian Modeling Applications Workshop, CEUR Workshop Proceedings 1218:79–88.

  • W. Y. Ahn and G. Vasilev and S.H. Lee and J.R. Busemeyer and J.K. Kruschke and A. Bechara and J. Vassileva. 2014. Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users. * Frontiers in Psychology* 5(849).

  • Husain, Samar and Vasishth, Shravan and Srinivasan, Narayanan, 2014. Strong Expectations Cancel Locality Effects: Evidence from Hindi. PLoS ONE, 9(7):e100986.

  • Van Beveren, E. and Bonhommeau, S. and Fromentin, J.-M. and Bigot, J.-L. and Bourdeix, J.-H. and Brosset, P. and Roos D. and Saraux, C. 2014. Rapid changes in growth, condition, size and age of small pelagic fish in the Mediterranean, Mar. Biol. 1–14.

  • Meisner, Matthew H. and Rosenheim, Jay A. 2014. Ecoinformatics Reveals Effects of Crop Rotational Histories on Cotton Yield, PLoS ONE 1/9.

  • Francis Ferraro, Benjamin Van Durme and Yanif Ahmad. 2013. Evaluating Progress in Probabilistic Programming through Topic Models. Proceedings of the NIPS Workshop on Topic Models

  • Thomas S. A. Wallis, Christopher Patrick Taylor, Jennifer Wallis, Mary Lou Jackson, and Peter J Bex. 2014. Characterization of field loss based on microperimetry is predictive of face recognition difficulties, Investigative Ophthalmology & Visual Science. 55(1):142–153

  • Michael J. Betancourt, Mark Girolami. 2013. Hamiltonian Monte Carlo for Hierarchical Models. arXiv 1312.0906.

  • Cowling, BJ, Freeman, G, Wong, JY, Wu, P , Liao, Q, Lau, EH, Wu, JT, Fielding, R, and Leung, GM. 2012. Preliminary inferences on the age-specific seriousness of human disease caused by avian influenza A (H7N9) infections in China, March to April 2013, Euro surveillance: bulletin Europeen sur les maladies transmissibles (European communicable disease bulletin 18(19).

  • Kari B Schroeder and Richard McElreath and Daniel Nettle. 2013. Variants at serotonin transporter and 2A receptor genes predict cooperative behavior differentially according to presence of punishment. Proceedings of the National Academy of Sciences, USA 110(10):3955-3960.

  • Bailey R House, Joan B Silk, Joseph Henrich , H Clark Barrett, Brooke Scelza, Adam Boyette , Barry Hewlett, Richard McElreath, and Stephen Laurence. 2013. The Ontogeny of Prosocial Behavior across Diverse Societies. Proceedings of the National Academy of Sciences, USA.

  • Richard McElreath and Jeremy Koster. 2013. Using Multilevel Models to Estimate Variation in Foraging Returns: Effects of Failure Rate, Harvest Size, Age, and Individual Heterogeneity. Human Nature.

  • Yajuan Si and Natesh Pillai and Andrew Gelman. 2013. Bayesian nonparametric weighted sampling inference, Journal of the American Statistical Association.

  • Taylor, Sean J, Bakshy, Eytan, and Aral, Sinan. 2013. Selection effects in online sharing: consequences for peer adoption. ACM Conference on Electronic Commerce 821–836.

  • Sutherland, Dougal J. and Póczos, Barnabás and Schneider, Jeff. 2013. Active learning and search on low-rank matrices. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 212–220.

  • Schmettow, M. and Havinga, J. 2013. Are users more diverse than designs? Testing and extending a 25 years old claim. C. Bowers and B. Cowan, ed., Proceedings of BCS HCI 2013—The Internet of Things XXVII.

  • Terada, Ryuta and Inoue, Shingo and Nishihara, Gregory N. 2013. The effect of light and temperature on the growth and photosynthesis of Gracilariopsis chorda (Gracilariales, Rhodophtya) from geographically separated locations of Japan. Journal of Applied Phycology 1–10.

Theses using Stan

  • Peter Starszyk. 2016. Inferring Chemical Reaction Rates from a Sequence of Infrared Spectra. University of Waterloo, Deptartment of Mathematics. MS Thesis.

  • Craig Wang. 2015. Bayesian Hierarchical Modelling of Zero-Inflated Faecal Egg Counts. ETH Zurich, Deptartment of Mathematics. Master Thesis.

  • Grant Cavanaugh. 2013. Direct Climate Markets: the Prospects for Trading Teleconnection Risk., Theses and Dissertations—Agricultural Economics, Paper 16. University of Kentucky. Ph.D. Dissertation.

  • Nemanja Vaci. 2013. Poređenje metoda ocenjivanja parametara na podacima iz psiholingvističkih eksperimenata (Robustness of methods for estimating regression parameters: Case of psycholinguistics data). Univerzitet u Novom Sadu. Master rad.

Papers Citing Stan

  • Natanegara, Fanni, Neuenschwander, Beat, Seaman, John W., Kinnersley, Nelson, Heilmann, Cory R., Ohlssen, David, Rochester, George. 2013. The current state of Bayesian methods in medical product development: survey results and recommendations from the DIA Bayesian Scientific Working Group. Pharmaceutical Statistics.

  • Kronberger, G. 2013. Declarative Modeling and Bayesian Inference of Dark Matter Halos. arXiv 1306.0202.

  • Murray, L.M. 2013. Bayesian State-Space Modelling on High-Performance Hardware Using LibBi. arXiv 1306.3277.

  • Byrne, S. and Girolami, M. 2013. Geodesic Monte Carlo on Embedded Manifolds. arXiv 1301.6064.

  • Bolker, Benjamin M., Gardner, Beth, Maunder, Mark, Berg, Casper W., Brooks, Mollie, Comita, Liza, Crone, Elizabeth, Cubaynes, Sarah, Davies, Trevor, de Valpine, Perry, Ford, Jessica, Gimenez, Olivier, Kéry, Marc, Kim, Eun Jung, Lennert-Cody, Cleridy, Magnusson, Arni, Martell, Steve, Nash, John, Nielsen,, ers, Regetz, Jim, Skaug, Hans, and Zipkin, Elise. 2013. Strategies for fitting nonlinear ecological models in R, AD Model Builder, and BUGS. Methods in Ecology and Evolution 4(6):501–512

  • Nishihara, R. and Murray, I. and Adams, R. P. 2012. Parallel MCMC with Generalized Elliptical Slice Sampling, arXiv 1210.7477.

Papers using NUTS

  • Nigel Goodwin. 2015. Bridging the Gap Between Deterministic and Probabilistic Uncertainty Quantification Using Advanced Proxy Based Methods. Proceedings of the Society of Petroleum Engineers Reservoir Simulation Symposium.

Software using Stan

  • Richard McElreath. 2016. rethinking: Statistical Rethinking book package, version 1.58. GitHub project rmcelreath/rethinking.

Software using NUTS

  • PyMC Developers. 2016. PyMC3: Probabilistic programming in Python. Version beta. GitHub project pymc-devs/pymc3.