Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition

Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition

Volumn , Issue , 2016, Pages 1-395

  Faraway, Julian J ^a  

^a UNIVERSITY OF BATH   (United Kingdom)

Author keywords

[No Author keywords available]

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EID: 85130663148     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/9781315382722     Document Type: Book

References (183)

1. Agresti, A. (1984). Analysis of Ordinal Categorical Data. New York: Wiley.
2. Agresti, A. (2013). Categorical Data Analysis (3 ed.). New York: John Wiley.
3. Allison, T. and D. Cicchetti (1976). Sleep in mammals: Ecological and constitutional correlates. Science 194, 732-734.
4. Andrews, D. and A. Herzberg (1985). Data: A Collection of Problems from Many Fields for the Student and Research Worker. New York: Springer-Verlag.
5. Appleton, D., J. French, and M. Vanderpump (1996). Ignoring a covariate: An example of Simpson’s paradox. American Statistician 50, 340-341.
6. Bates, D. (2005). Fitting linear mixed models in R. R News 5(1), 27-30.
7. Bellman, R. (1961). Adaptive Control Processes: A Guided Tour. Princeton, NJ: Princeton University Press.
8. k−p series. Technometrics 39, 191-198.
9. Bickel, P. and K. Doksum (2015). Mathematical Statistics: Basic Ideas and Selected Topics (2 ed.). Boca Raton, FL: CRC Press.
10. Bilder, C. R. and T. M. Loughin (2014). Analysis of Categorical Data with R. Boca Raton, FL: CRC Press.
11. Bishop, C. (1995). Neural Networks for Pattern Recognition. Oxford: Clarendon Press.
12. Bishop, Y., S. Fienberg, and P. Holland (1975). Discrete Multivariate Analysis: Theory and Practice. Cambridge, MA: MIT Press.
13. Blasius, J. and M. Greenacre (1998). Visualization of Categorical Data. San Diego: Academic Press.
14. Bliss, C. I. (1935). The calculation of the dose-mortality curve. Annals of Applied Biology 22, 134-167.
15. Bliss, C. I. (1967). Statistics in Biology. New York: McGraw Hill.
16. Bowman, A. and A. Azzalini (1997). Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations. Oxford: Oxford University Press.
17. Box, G. and R. Meyer (1986). Dispersion effects from fractional designs. Technometrics 28, 19-27.
18. Box, G. P., S. Bisgaard, and C. Fung (1988). An explanation and critique of Taguchi’s contributions to quality engineering. Quality and Reliability Engineering International 4, 123-131.
19. Box, G. P., W. G. Hunter, and J. S. Hunter (1978). Statistics for Experimenters. New York: Wiley.
20. Breiman, L. (2001a). Random forests. Machine Learning 45(1), 5-32.
21. Breiman, L. (2001b). Statistical modeling: The two cultures (with comments and a rejoinder by the author). Statistical Science 16, 199-231.
22. Breiman, L., J. Friedman, R. Olshen, and C. Stone (1984). Classification and Regression Trees. Boca Raton, FL: Chapman & Hall.
23. Breiman, L. and J. H. Friedman (1985). Estimating optimal transformations for multiple regression and correlation. Journal of the American Statistical Association 80, 580-598.
24. Breslow, N. (1982). Covariance adjustment of relative-risk estimates in matched studies. Biometrics 38, 661-672.
25. Breslow, N. E. and D. G. Clayton (1993). Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9-25.
26. Cameron, A. and P. Trivedi (1998). Regression Analysis of Count Data. Cambridge: Cambridge University Press.
27. Christensen, R. (1997). Log-Linear Models and Logistic Regression (2 ed.). New York: Springer.
28. Cleveland, W. (1979). Robust locally weighted regression and smoothing scatter-plots. Journal of the American Statistical Association 74, 829-836.
29. Clogg, C. and E. Shihadeh (1994). Statistical Models for Ordinal Variables. Thousands Oaks, CA: Sage.
30. 2 tests. Biometrics 10, 417-451.
31. Collett, D. (2003). Modelling Binary Data (2 ed.). London: Chapman & Hall.
32. Comizzoli, R. B., J. M. Landwehr, and J. D. Sinclair (1990). Robust materials and processes: Key to reliability. AT&T Technical Journal 69(6), 113-128.
33. Cox, D. (1970). Analysis of Binary Data. London: Spottiswoode, Ballantyne and Co.
34. Cox, D. and D. Hinkley (1974). Theoretical Statistics. London: Chapman & Hall.
35. Crainiceanu, C. and D. Ruppert (2004). Likelihood ratio tests in linear mixed models with one variance component. Journal of the Royal Statistical Society, Series B 66, 165-185.
36. Crowder, M. (1978). Beta-binomial anova for proportions. Applied Statistics 27, 34-37.
37. Crowder, M. J. and D. J. Hand (1990). Analysis of Repeated Measures. London: Chapman & Hall.
38. Dalal, S., E. Fowlkes, and B. Hoadley (1989). Risk analysis of the space shuttle: Pre-Challenger prediction of failure. Journal of the American Statistical Association 84, 945-957.
39. Dalgaard, P. (2002). Introductory Statistics with R. New York: Springer.
40. Daubechies, I. (1991). Ten Lectures on Wavelets. Philadelphia: SIAM.
41. Davies, O. (1954). The Design and Analysis of Industrial Experiments. New York: Wiley.
42. Dey, D., S. Ghosh, and B. Mallick (2000). Generalized Linear Models: A Bayesian Perspective. New York: Marcel Dekker.
43. Diggle, P. J., P. Heagerty, K. Y. Liang, and S. L. Zeger (2013). Analysis of Longitudinal Data (2 ed.). Oxford: Oxford University Press.
44. Dobson, A. and A. Barnett (2008). An Introduction to Generalized Linear Models (3 ed.). London: Chapman & Hall.
45. Draper, N. and H. Smith (1998). Applied Regression Analysis (3rd ed.). New York: Wiley.
46. Dunn, P. K. and G. K. Smyth (2005). Series evaluation of Tweedie exponential dispersion model densities. Statistics and Computing 15(4), 267-280.
47. Eubank, R. (1988). Spline Smoothing and Nonparametric Regression. New York: Marcel Dekker.
48. Fahrmeir, L. and G. Tutz (2001). Multivariate Statistical Modelling Based on Generalized Linear Models. New York: Springer.
49. Faraway, J. (2014). Linear Models with R (2 ed.). Boca Raton, FL: Chapman & Hall/CRC.
50. Faraway, J. and C. Chatfield (1998). Time series forecasting with neural networks: A case study. Applied Statistics 47, 231-250.
51. Firth, D. (1993). Bias reduction of maximum likelihood estimates. Biometrika 80, 27-38.
52. Fitzmaurice, G., N. Laird, and J. Ware (2004). Hoboken, NJ: Wiley-Interscience. Applied Longitudinal Analysis.
53. Fletcher, R. (1987). Practical Methods of Optimization (2 ed.). Chichester, UK: John Wiley.
54. Fox, J. (2003). Effect displays in R for generalised linear models. Journal of Statistical Software 8(15), 1-27.
55. Frees, E. (2004). Longitudinal and Panel Data: Analysis and Applications in the Social Sciences. Cambridge: Cambridge University Press.
56. Friedman, J. (1991). Multivariate adaptive regression splines (with discussion). Annals of Statistics 19, 1-141.
57. Frome, E. and R. DuFrain (1986). Maximum likelihood estimation for cytogenic dose-response curves. Biometrics 42, 73-84.
58. Gelman, A. (2005). Analysis of variance — why it is more important than ever (with discussion). Annals of Statistics 33, 1-53.
59. Gelman, A., J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin (2013). Bayesian Data Analysis (3 ed.). Boca Raton, FL: CRC Press.
60. Gelman, A. and J. Hill (2006). Data Analysis Using Regression and Multi-level/Hierarchical Models. Cambridge: Cambridge University Press.
61. Gelman, A. and Y.-S. Su (2013). arm: Data Analysis Using Regression and Multi-level/Hierarchical Models. R package version 1.6-10.
62. Gill, J. (2001). Generalized Linear Models: A Unified Approach. Thousand Oaks, CA: Sage.
63. Goldstein, H. (1995). Multilevel Statistical Models (2 ed.). London: Arnold.
64. Green, P. and B. Silverman (1993). Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach. London: Chapman & Hall.
65. Gu, C. (2002). Smoothing Spline ANOVA Models. New York: Springer-Verlag.
66. Haberman, S. (1977). The Analysis of Frequency Data. Chicago, IL: University of Chicago Press.
67. Hadfield, J. D. (2010). MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. Journal of Statistical Software 33(2), 1-22.
68. Halekoh, U. and S. Højsgaard (2014). A Kenward-Roger approximation and parametric bootstrap methods for tests in linear mixed models—the R package pbkrtest. Journal of Statistical Software 59, 1-32.
69. Hall, S. (1994). Analysis of defectivity of semiconductor wafers by contigency table. Proceedings of the Institute of Environmental Sciences 1, 177-183.
70. Hallin, M. and J.-F. Ingenbleek (1983). The Swedish automobile portfolio in 1977. A statistical study. Scandinavian Actuarial Journal 83, 49-64.
71. Hardin, J. and J. Hilbe (2003). Generalized Estimating Equations. Boca Raton, FL: Chapman & Hall/CRC Press.
72. Härdle, W. (1991). Smoothing Techniques with Implementation in S. New York: Springer.
73. Harrell, F. (2001). Regression Modelling Strategies. New York: Springer-Verlag.
74. Hart, J. (1997). Nonparametric Smoothing and Lack-of-Fit Tests. New York: Springer.
75. Hartigan, J. and B. Kleiner (1981). Mosaics for contingency tables. In W. Eddy (Ed.), Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface, New York, pp. 268-273. Springer-Verlag.
76. Hastie, T. and R. Tibshirani (1990). Generalized Additive Models. London: Chapman & Hall.
77. Hastie, T., R. Tibshirani, and J. Friedman (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. New York: Springer.
78. Hauck, W. and A. Donner (1977). Wald’s test as applied to hypotheses in logit analysis. Journal of the American Statistical Association 72, 851-853.
79. Haykin, S. (1998). Neural Networks: A Comprehensive Foundation (2 ed.). Upper Saddle River, NJ: Prentice Hall.
80. Hertz, J., A. Krogh, and R. Palmer (1991). Introduction to the Theory of Neural Computation. Redwood City, CA: Addison-Wesley.
81. Hilbe, J. M. (2009). Logistic Regression Models. Boca Raton, FL: CRC Press.
82. Hill, M. S. (1992). The Panel Study of Income Dynamics: A User’s Guide. Newbury Park, CA: Sage.
83. Hinde, J. and C. Demetrio (1988). Overdispersion: Models and estimation. Computational Statistics and Data Analysis 27, 151-170.
84. Højsgaard, S., U. Halekoh, and J. Yan (2005). The R package geepack for generalized estimating equations. Journal of Statistical Software 15(2), 1-11.
85. Hornik, K., M. Stinchcombe, and H. White (1989). Multilayer feedforward networks are universal approximators. Neural Networks 2, 359-366.
86. Hosmer, D. and S. Lemeshow (2013). Applied Logistic Regression (3 ed.). New York: Wiley.
87. Johnson, M. P. and P. H. Raven (1973). Species number and endemism: The Galá-pagos archipelago revisited. Science 179, 893-895.
88. Kenward, M. and J. Roger (1997). Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53, 983-997.
89. Kleinbaum, D. G. and M. Klein (2002). Logistic Regression: A Self-Learning Text. New York: Springer.
90. Kosmidis, I. (2013). brglm: Bias Reduction in Binary-Response Generalized Linear Models. R package version 0.5-9.
91. Kovac, A. and B. W. Silverman (2000). Extending the scope of wavelet regression methods by coefficient-dependent thresholding. Journal of the American Statistical Association 95(449), 172-183.
92. Kunsch, H. R., L. A. Stefanski, and R. J. Carroll (1989). Conditionally unbiased bounded-influence estimation in general regression models, with applications to generalized linear models. Journal of the American Statistical Association 84(406), 460-466.
93. Lawless, J. (1987). Negative binomial and mixed poisson regression. Canadian Journal of Statistics 15, 209-225.
94. Le, C. T. (1998). Applied Categorical Data Analysis. Newbury Park, CA: Wiley.
95. Leonard, T. (2000). A Course in Categorical Data Analysis. Boca Raton, FL: Chapman & Hall/CRC Press.
96. Liaw, A. and M. Wiener (2002). Classification and regression by randomforest. R News 2(3), 18-22.
97. Lindsey, J. K. (1997). Applying Generalized Linear Models. New York: Springer.
98. Lindsey, J. K. (1999). Models for Repeated Measurements (2 ed.). Oxford: Oxford University Press.
99. Loader, C. (1999). Local Regression and Likelihood. New York: Springer.
100. Long, J. S. (1990). The origins of sex differences in science. Social Forces 68(4), 1297-1316.
101. Lowe, C., C. Roberts, and S. Lloyd (1971). Malformations of the central nervous system and softness of local water supplies. British Medical Journal 15, 357-361.
102. Lunn, D., C. Jackson, N. Best, A. Thomas, and D. Spiegelhalter (2012). The BUGS Book: A Practical Introduction to Bayesian Analysis. Boca Raton, FL: CRC Press.
103. Maindonald, J. and J. Braun (2010). Data Analysis and Graphics Using R (3 ed.). Cambridge, UK: Cambridge University Press.
104. Manly, B. (1978). Regression models for proportions with extraneous variance. Biometrie-Praximetrie 18, 1-18.
105. Mantel, N. and W. Haenszel (1959). Statistical aspects of the analysis of data from retrospective studies of disease. Journal of the National Cancer Institute 22, 719- 748.
106. McCullagh, P. (1983). Quasi-likelihood functions. Annals of Statistics 11, 59-67.
107. McCullagh, P. and J. Nelder (1989). Generalized Linear Models (2 ed.). London: Chapman & Hall.
108. McCulloch, C. and S. Searle (2002). Generalized, Linear, and Mixed Models. New York: Wiley.
109. McCulloch, W. and W. Pitts (1943). A logical calculus of ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115-133.
110. Mehta, C. and N. Patel (1995). Exact logistic regression: Theory and examples. Statistics in Medicine 14, 2143-2160.
111. Menard, S. (2002). Applied Logistic Regression Analysis (2 ed.). Thousands Oaks, CA: Sage.
112. Meyer, M. (2002). Uncounted votes: Does voting equipment matter? Chance 15(4), 33-38.
113. Milliken, G. A. and D. E. Johnson (1992). Analysis of Messy Data, Volume 1. New York: Van Nostrand Reinhold.
114. Morgan, J. and J. Sonquist (1963). Problems in the analysis of survey data, and a proposal. Journal of the American Statistical Association 58, 415-434.
115. Mortimore, P., P. Sammons, L. Stoll, D. Lewis, and R. Ecob (1988). School Matters. Wells, UK: Open Books.
116. Müller, S., J. L. Scealy, and A. H. Welsh (2013). Model selection in linear mixed models. Statistical Science 28(2), 135-167.
117. Myers, R. and D. Montgomery (1997). A tutorial on generalized linear models. Journal of Quality Technology 29, 274-291.
118. Myers, R., D. Montgomery, and G. Vining (2002). Generalized Linear Models: With Applications in Engineering and the Sciences. New York: Wiley.
119. Nagelkerke, N. (1991). A note on a general definition of the coefficient of determination. Biometrika 78, 691-692.
120. Nason, G. (2013). wavethresh: Wavelets Statistics and Transforms. R package version 4.6.6.
121. Neal, R. (1996). Bayesian Learning for Neural Networks. New York: Springer- Verlag.
122. Nelder, J., Y. Lee, B. Bergman, A. Hynen, A. Huele, and J. Engel (1998). Letter to editor: Joint modeling of mean and dispersion. Technometrics 40, 168-175.
123. Nelder, J. and R. Wedderburn (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A 132, 370-384.
124. Payne, C. (1987). The GLIM System Release 3.77 Manual (2 ed.). Oxford: Numerical Algorithms Group.
125. Pignatiello, J. J. and J. S. Ramberg (1985). Contribution to discussion of offline quality control, parameter design and the Taguchi method. Journal of Quality Technology 17, 198-206.
126. Pinheiro, J. C. and D. M. Bates (2000). Mixed-Effects Models in S and S-PLUS. New York: Springer.
127. Powers, D. and Y. Xie (2000). Statistical Methods for Categorical Data Analysis. San Diego, CA: Academic Press.
128. Pregibon, D. (1981). Logistic regression diagnostics. Annals of Statistics 9, 705- 724.
129. Purott, R. and E. Reeder (1976). The effect of changes in dose rate on the yield of chromosome aberrations in human lymphocytes exposed to gamma radiation. Mutation Research 35, 437-444.
130. Quinlan, J. (1993). C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufman.
131. R Core Team (2015). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
132. Rasmussen, C. and C. Williams (2006). Gaussian Processes for Machine Learning. Cambridge, MA: The MIT Press.
133. Raudenbush, S. and A. Bryk (2002). Hierarchical Linear Models: Applications and Data Analysis Methods (2 ed.). Thousand Oaks, CA: Sage.
134. Rice, J. (1998). Mathematical Statistics and Data Analysis. Monterey, CA: Brooks Cole.
135. Ripley, B. (1996). Pattern Recognition and Neural Networks. Cambridge, UK: Cambridge University Press.
136. Rosenman, R. H., R. J. Brand, C. D. Jenkins, M. Friedman, R. Straus, and M. Wurm (1975). Coronary heart disease in the western collaborative group study: Final follow-up experience of 8 1/2 years. JAMA 233(8), 872-877.
137. Rosenstone, S. J., D. R. Kinder, and W. E. Miller (1997). American National Election Study. Ann Arbor, MI: Inter-University Consortium for Political and Social Research.
138. Rue, H., S. Martino, and N. Chopin (2009). Approximate bayesian inference for latent gaussian models by using integrated nested laplace approximations. Journal of the Royal Statistical Society: Series B 71(2), 319-392.
139. Santner, T. and D. Duffy (1989). The Statistical Analysis of Discrete Data. New York: Springer.
140. Schall, R. (1991). Estimation in generalized linear models with random effects. Biometrika 78(4), 719-727.
141. Scheffé, H. (1959). The Analysis of Variance. New York: Wiley.
142. Scheipl, F., S. Greven, and H. Kuechenhoff (2008). Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models. Computational Statistics and Data Analysis 52(7), 3283-3299.
143. Scrucca, L. (2012). dispmod: Dispersion Models. R package version 1.1.
144. Searle, S., G. Casella, and C. McCulloch (1992). Variance Components. New York: Wiley.
145. Seshadri, V. (1993). The Inverse Gaussian Distribution. Oxford: Clarendon.
146. Sheldon, F. (1960). Statistical techniques applied to production situations. Industrial and Engineering Chemistry 52, 507-509.
147. Simonoff, J. (1996). Smoothing Methods in Statistics. New York: Springer.
148. Simonoff, J. (2003). Analyzing Categorical Data. New York: Springer.
149. Simpson, D. P., T. G. Martins, A. Riebler, G.-A. Fuglstad, H. Rue, and S. H. Sørbye (2014). Penalising model component complexity: A principled, practical approach to constructing priors. arXiv:1403.4630.
150. Simpson, E. (1951). The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society, Series B 13, 238-241.
151. Smyth, G., F. Huele, and A. Verbyla (2001). Exact and approximate reml for het-eroscedastic regression. Statistical Modelling: An International Journal 1, 161- 175.
152. Snedecor, G. and W. Cochran (1989). Statistical Methods (8 ed.). Ames, IA: Iowa State University Press.
153. Snee, R. (1974). Graphical display of two-way contingency tables. American Statistician 28, 9-12.
154. Spiegelhalter, D. J., N. G. Best, B. P. Carlin, and A. Van Der Linde (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 64(4), 583-639.
155. Stan Development Team (2015). Stan Modeling Language Users Guide and Reference Manual, Version 2.8.0.
156. Steele, R. (1998). Effect of Surface and Vision on Balance. Ph. D. thesis, Department of Physiotherapy, University of Queensland.
157. Stone, C. (1985). Additive regression and other nonparametric models. Annals of Statistics 13, 689-705.
158. Stram, D. and J. Lee (1994). Variance components testing in the longitudinal mixed-effects model. Biometrics 50, 1171-1179.
159. Stuart, A. (1955). A test for homogeneity of the marginal distributions in a two-way classification. Biometrika 42, 412-416.
160. Thall, P. F. and S. C. Vail (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics 46, 657-671.
161. Tukey, J. (1977). Exploratory Data Analysis. New York: Addison Wesley.
162. Venables, W. and B. Ripley (2002). Modern Applied Statistics with S (4 ed.). New York: Springer.
163. Verbeke, G. and G. Molenberghs (2000). Linear Mixed Models for Longitudinal Data. New York: Springer.
164. Wahba, G. (1990). Spline Models for Observational Data. Philadelphia: SIAM.
165. Wand, M. and M. Jones (1995). Kernel Smoothing. London: Chapman & Hall.
166. Wang, J., R. Zamar, A. Marazzi, V. Yohai, M. Salibian-Barrera, R. Maronna, E. Zivot, D. Rocke, D. Martin, M. Maechler, and K. Konis. (2014). robust: Robust Library. R package version 0.4-16.
167. Wedderburn, R. W. M. (1974). Quasilikelihood functions, generalized linear models and the Gauss-Newton method. Biometrika 61, 439-447.
168. Weisberg, S. (2005). Applied Linear Regression (3 ed.). New York: Wiley.
169. Wheeler, B. (2013). SuppDists: Supplementary Distributions. R package version 1.1-9.1.
170. Whitmore, G. (1986). Inverse Gaussian ratio estimation. Applied Statistics 35, 8-15.
171. Wickham, H. (2009). ggplot2: Elegant Graphics for Data Analysis. New York: Springer.
172. Wickham, H. and R. Francois (2015). dplyr: A Grammar of Data Manipulation. R package version 0.4.1.
173. Wilkinson, G. and C. Rogers (1973). Symbolic description of factorial models for the analysis of variance. Applied Statistics 22, 392-399.
174. Williams, D. (1982). Extra-binomial variation in logistic linear models. Applied Statistics 31, 144-148.
175. Williams, D. (1987). Generalized linear model diagnostics using the deviance and single case deletions. Applied Statistics 36, 181-191.
176. Wold, S., A. Ruhe, H. Wold, and W. Dunn (1984). The collinearity problem in linear regression: The partial least squares (pls) approach to generalized inverses. SIAM Journal on Scientific and Statistical Computing 5, 735-743.
177. Wood, S. (2000). Modelling and smoothing parameter estimation with multiple quadratic penalties. Journal of the Royal Statistal Society, Series B 62, 413-428.
178. Wood, S. (2006). Generalized Additive Models: An Introduction with R. Boca Raton, FL: CRC Press.
179. Wood, S. (2015). Core Statistics. Cambridge: Cambridge University Press.
180. Yee, T. (2010). The vgam package for categorical data analysis. Journal of Statistical Software 32(10), 1-34.
181. Yule, G. (1903). Notes on the theory of association of attributes in statistics. Biometrika 2, 121-134.
182. Zeileis, A. (2004). Econometric computing with hc and hac covariance matrix estimators. Journal of Statistical Software 11(10), 1-17.
183. Zeileis, A., C. Kleiber, and S. Jackman (2008). Regression models for count data in R. Journal of Statistical Software 27(8), 1-25.