MCMCpack: Markov chain Monte Carlo in R

Journal of Statistical Software

Volumn 42, Issue 9, 2011, Pages 1-21

  Martin, Andrew D ^a   Quinn, Kevin M ^b   Park, Jong Hee ^c  

^a WASHINGTON UNIVERSITY SCHOOL OF LAW   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CHICAGO   (United States)

Author keywords

Bayesian inference; Markov chain Monte Carlo; R

Indexed keywords

EID: 79961213152     PISSN: None     EISSN: 15487660     Source Type: Journal    
DOI: 10.18637/jss.v042.i09     Document Type: Article

References (62)

1. Albert JH, Chib S (1993). Bayesian Analysis of Binary and Polychotomous Response Data. Journal of the American Statistical Association, 88, 669-679.
2. Anderson B, Fox J, Frankin C, Gill J (2003). The Times They R Achanging: The State of Advanced Methodology Curriculum at ICPSR. The Political Methodologist, 11, 22-24.
3. Becker RA, Chambers JM, Wilks AR (1988). The New S Language. Chapman & Hall, London.
4. Bernardo J, Smith AFM (1994). Bayesian Theory. John Wiley & Sons, New York.
5. Besag J, Green P, Higdon D, Mengersen K (1995). Bayesian Computation and Stochastic Systems. Statistical Science, 10, 3-66.
6. Chib S (1995). Marginal Likelihood From the Gibbs Output. Journal of the American Statistical Association, 90, 1313-1321.
7. Chib S (1998). Estimation and Comparison of Multiple Change-Point Models. Journal of Econometrics, 86(2), 221-241.
8. Chib S, Jeliazkov I (2001). Marginal Likelihood From the Metropolis Hastings Output. Journal of the American Statistical Association, 96, 270-281.
9. Clinton J, Jackman S, Rivers D (2004). The Statistical Analysis of Roll Call Data. American Political Science Review, 98, 355-370.
10. Cowles MK (1996). Accelerating Monte Carlo Markov Chain Convergence for Cumulative-Link Generalized Linear Models. Statistics and Computing, 6, 101-110.
11. Fox J (2002). An R and S-PLUS Companion to Applied Regression. Sage Publications, Thousand Oaks, CA.
12. Frühwirth-Schnatter S, Wagner H (2006). Auxiliary Mixture Sampling for Parameter-driven Models of Time Series of Small Counts with Applications to State Space Modelling. Biometrika, 93, 827-841.
13. Gelfand AE, Smith AFM (1990). Sampling-Based Approaches to Calculating Marginal Densities. Journal of the American Statistical Association, 85, 398-409.
14. Gelman A, Carlin JB, Stern HS, Rubin DB (2003). Bayesian Data Analysis. 2nd edition. Chapman & Hall/CRC, Boca Raton.
15. Gelman A, King G (1990). Estimating the Electoral Consequences of Legislative Redistricting. Journal of the American Statistical Association, 85, 274-282.
16. Geweke J (1999). Using Simulation Methods for Bayesian Econometric Models: Inference, Development, and Commnication. Econometric Reviews, 18, 1-126.
17. Geweke J, Keane M (2000). An Empirical Analysis of Income Dynamics Among Men in the PSID: 1968-1989. Journal of Econometrics, 96, 293-356.
18. Gill J (2002). Bayesian Methods: A Social and Behavioral Sciences Approach. Chapman & Hall, Boca Raton.
19. Gilpin R (1981). War and Change in World Politics. Cambridge University Press, Cambridge.
20. Goldstein J (1988). Long Cycles: Prosperity and War in the Modern Age. Yale University Press, New Haven.
21. Hoeting JA, Madigan D, Raftery AE, Volinsky CT (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14, 382-417.
22. Hoff PD, Raftery AE, Handcock MS (2002). Latent Space Approaches to Social Network Analysis. Journal of the American Statistical Association, 97, 1090-1098.
23. Hoff PD, Ward MD (2004). Modeling Dependencies in International Relations Networks. Political Analysis, 12, 160-175.
24. Jeffreys SH (1998). Theory of Probability. 3rd edition. Clarendon Press, Oxford.
25. Johnson VE, Albert JH (1999). Ordinal Data Modeling. Springer-Verlag, New York.
26. Jones DM, Bremer SA, Singer JD (1996). Militarized Interstate Disputes, 1816-1992: Rationale, Coding Rules, and Empirical Patterns. Conflict Management and Peace Science, 15, 163-213.
27. Kass RE, Raftery AE (1995).Bayes Factors. Journal of the American Statistical Association, 90, 773-795.
28. King G, Rosen O, Tanner MA (1999). Binomial-Beta Hierarchical Models for Ecological Inference. Sociological Research and Methods, 28, 61-90.
29. L'Ecuyer P, Simard R, Chen EJ, Kelton WD (2002). An Object-Oriented Random-Number Package with Many Long Streams and Substreams. Operations Research, 50, 1073-1075.
30. Ligges U, Thomas A, Spiegelhalter D, Best N, Lunn D, Rice K, Sturtz S (2009). BRugs 0.5: OpenBUGS and Its R/S-PLUS Interface BRugs. URL http://www.stats.ox.ac.uk/pub/RWin/src/contrib/.
31. Liu JS, Wong WH, Kong A (1994). Covariance Structure of the Gibbs Sampler with Applications to the Comparisons of Estimators and Augmentation Schemes. Journal of the American Statistical Association, 89, 958-966.
32. Martin AD, Quinn KM (2002). Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953-1999. Political Analysis, 10, 134-153.
33. Martin AD, Quinn KM, Park JH (2011). MCMCpack: Markov Chain Monte Carlo (MCMC) Package. R package version 1.0-10, URL http://CRAN.R-project.org/package=MCMCpack.
34. Matsumoto M, Nishimura T (1998).Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator. ACM Transactions on Modeling and Computer Simulation, 8, 3-30.
35. Modelski G, Thompson WR (1988). Seapower in Global Politics. University of Washington Press, Seattle.
36. Park JH (2010). Structural Change in U.S. Presidents' Use of Force. American Journal of Political Science, 54(3), 766-782.
37. Park JH, Martin AD, Quinn KM (2010). CRAN Task View: Bayesian Inference. Version 2010-12-14, URL http://CRAN.R-project.org/view=Bayesian.
38. Plummer M (2003). JAGS: A Program for Analysis of Bayesian Graphical Models Using Gibbs Sampling. In K Hornik, F Leisch, A Zeileis (eds.), Proceedings of the 3rd International Workshop on Distributed Statistical Computing, Vienna, Austria. ISSN 1609-395X, URL http://www.ci.tuwien.ac.at/Conferences/DSC-2003/Proceedings/.
39. Plummer M (2005). JAGS: Just Another Gibbs Sampler. Version 0.8, URL http://www-fis.iarc.fr/~martyn/software/jags/.
40. Plummer M (2009). Rjags: Interface to the JAGS MCMC Library. R package version 1.0.3, URL http://CRAN.R-project.org/package=rjags.
41. Plummer M, Best N, Cowles K, Vines K (2006). coda: Convergence Diagnosis and Output Analysis for MCMC. R News, 6(1), 7-11. URL http://CRAN.R-project.org/doc/Rnews/.
42. Pollins BM (1996). Global Political Order, Economic Change, and Armed Conict: Coevolving Systems and the Use of Force. American Political Science Review, 90, 103-117.
43. Poole K, Lewis J, Lo J, Carroll R (2011). Scaling Roll Call Votes with wnominate in R. Journal of Statistical Software, 42(14), 1-21. URL http://www.jstatsoft.org/v42/i14/.
44. Poole KT, Rosenthal H (1997). Congress: A Political-Economic History of Roll Call Voting. Oxford University Press, Oxford.
45. Quinn KM, Martin AD (2002). An Integrated Computational Model of Electoral Competition. Statistical Science, 17, 405-419.
46. Quinn KM, Martin AD, Whitford AB (1999). Voter Choice in Multi-Party Democracies: A Test of Competing Theories and Models. American Journal of Political Science, 43, 1231-1247.
47. R Development Core Team (2011a). An Introduction to R. Vienna, Austria. ISBN 3-900051-12-7, URL http://www.R-project.org/.
48. R Development Core Team (2011b). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
49. R Development Core Team (2011c). R Language Definition. Vienna, Austria. ISBN 3-900051-13-5, URL http://www.R-project.org/.
50. Robert C, Casella G (2004). Monte Carlo Statistical Methods. 2nd edition. Springer-Verlag, New York.
51. Rossi P, McCulloch R (2006). Bayesm: Bayesian Inference for Marketing/Micro-Econometrics. R package version 2.0-8, URL http://CRAN.R-project.org/package=bayesm.
52. Spiegelhalter DJ, Thomas A, Best NG, Lunn D (2003).WinBUGS Version 1.4 User Manual. MRC Biostatistics Unit, Cambridge. URL http://www.mrc-bsu.cam.ac.uk/bugs/.
53. Sturtz S, Ligges U, Gelman A (2005). R2WinBUGS: A Package for Running WinBUGS from R. Journal of Statistical Software, 12(3), 1-16. URL http://www.jstatsoft.org/v12/i03/.
54. Tanner MA, Wong W (1987). The Calculation of Posterior Distributions by Data Augmentation. Journal of the American Statistical Association, 82, 528-550.
55. Thomas A, O'Hara B, Ligges U, Sturtz S (2006). Making BUGS Open. R News, 6(1), 12-17. URL http://CRAN.R-project.org/doc/Rnews/.
56. Tierney L, Kadane JB (1986). Accurate Approximation for Posterior Moments and Marginal Densities. Journal of the American Statistical Association, 81, 82-86.
57. Tierney L, Rossini AJ, Li N, Sevcikova H (2008). Snow: Simple Network of Workstations. R package version 0.2-2, URL http://CRAN.R-project.org/package=snow.
58. Venables WN, Ripley BD (2000). S Programming. Springer-Verlag, New York.
59. Venables WN, Ripley BD (2002). Modern Applied Statistics with S. 4th edition. Springer-Verlag, New York.
60. Wallerstein I (1983). Three Instances of Hegemony in the History of the World Economy. International Jounal of Comparative Sociology, 24, 100-108.
61. Western B (1998). Causal Heterogeneity in Comparative Research: A Bayesian Hierarchical Modelling Approach. American Journal of Political Science, 42, 1233-1259.
62. Wilkerson JD (1999). Killer' Amendments in Congress. American Political Science Review, 93, 535-552.