references.bib

@book{agresti_cda_2013,
  author     = {Agresti, Alan},
  edition    = {Third},
  isbn       = {978-0-470-46363-5},
  mrclass    = {62H17 (62H30 62J12)},
  mrnumber   = {3087436},
  mrreviewer = {Vassilis\ G. S. Vasdekis},
  pages      = {xvi+714},
  publisher  = {Wiley-Interscience [John Wiley \& Sons], Hoboken, NJ},
  series     = {Wiley Series in Probability and Statistics},
  title      = {Categorical data analysis},
  year       = {2013}
}

@article{christensen_cumulative_2018,
  title={Cumulative link models for ordinal regression with the R package ordinal},
  author={Christensen, Rune Haubo B},
  journal={Submitted in J. Stat. Software},
  volume={35},
  pages={1--46},
  year={2018}
}

@book{chambers_hastie_sms_1992,
  title     = {Statistical Models in S},
  author    = {Chambers, John M. and Hastie, Trevor J.},
  year      = {1992},
  publisher = {Wadsworth \& Brooks/Cole},
  address   = {Pacific Grove, CA}
}

@misc{bartlett_deviance_2014,
  author   = {Bartlett, Jonathan},
  file     = {Snapshot:/Users/jccum/Zotero/storage/QPBMQJN8/deviance-goodness-of-fit-test-for-poisson-regression.html:text/html},
  journal  = {The Stats Geek},
  language = {en-US},
  month    = apr,
  title    = {Deviance goodness of fit test for {Poisson} regression},
  url      = {https://thestatsgeek.com/2014/04/26/deviance-goodness-of-fit-test-for-poisson-regression/},
  urldate  = {2026-02-04},
  year     = {2014}
}

@article{bates_fitting_2015,
  abstract     = {Maximum likelihood or restricted maximum likelihood ({REML}) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled {REML} criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or {REML} criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.},
  author       = {Bates, Douglas and Mächler, Martin and Bolker, Ben and Walker, Steve},
  date         = {2015-10-07},
  doi          = {10.18637/jss.v067.i01},
  journaltitle = {Journal of Statistical Software},
  number       = {1},
  pages        = {1 -- 48},
  shortjournal = {J. Stat. Soft.},
  title        = {Fitting Linear Mixed-Effects Models Using lme4},
  url          = {https://www.jstatsoft.org/index.php/jss/article/view/v067i01},
  urldate      = {2026-04-06},
  volume       = {67}
}

@book{collett_modelling_2023,
  abstract  = {Modelling Survival Data in Medical Research, Fourth Edition, describes the analysis of survival data, illustrated using a wide range of examples from biomedical research. Written in a non-technical style, it concentrates on how the techniques are used in practice. Starting with standard methods for summarising survival data, Cox regression and parametric modelling, the book covers many more advanced techniques, including interval-censoring, frailty modelling, competing risks, analysis of multiple events, and dependent censoring.This new edition contains chapters on Bayesian survival analysis and use of the R software. Earlier chapters have been extensively revised and expanded to add new material on several topics. These include methods for assessing the predictive ability of a model, joint models for longitudinal and survival data, and modern methods for the analysis of interval-censored survival data.Features: Presents an accessible account of a wide range of statistical methods for analysing survival data Contains practical guidance on modelling survival data from the author's many years of experience in teaching and consultancy Shows how Bayesian methods can be used to analyse survival data Includes details on how R can be used to carry out all the methods described, with guidance on the interpretation of the resulting output Contains many real data examples and additional data sets that can be used for coursework All data sets used are available in electronic format from the publisher's website Modelling Survival Data in Medical Research, Fourth Edition, is an invaluable resource for statisticians in the pharmaceutical industry and biomedical research centres, research scientists and clinicians who are analysing their own data, and students following undergraduate or postgraduate courses in survival analysis.},
  author    = {Collett, David},
  date      = {2023-01-01},
  edition   = {Fourth edition},
  isbn      = {978-1-032-25285-8},
  keywords  = {Clinical trials--Statistical methods, {MATHEMATICS} / Probability \& Statistics / Regression Analysis, {MEDICAL} / Biostatistics, Survival analysis (Biometry)},
  location  = {Boca Raton},
  publisher = {Chapman and Hall/{CRC}},
  series    = {Chapman \& Hall/{CRC} Texts in Statistical Science},
  title     = {Modelling Survival Data in Medical Research},
  url       = {https://research.ebsco.com/linkprocessor/plink?id=d1071170-8881-33d1-a2fe-2cff38fe2053}
}

@manual{conflicted_package,
  author = {Hadley Wickham},
  doi    = {10.32614/CRAN.package.conflicted},
  note   = {{R} package version 1.2.0},
  title  = {{conflicted}: An Alternative Conflict Resolution Strategy},
  url    = {https://CRAN.R-project.org/package=conflicted},
  year   = {2023}
}

@manual{dplyr_package,
  author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller and Davis Vaughan},
  doi    = {10.32614/CRAN.package.dplyr},
  note   = {{R} package version 1.1.4},
  title  = {{dplyr}: A Grammar of Data Manipulation},
  url    = {https://CRAN.R-project.org/package=dplyr},
  year   = {2023}
}

@book{dunn_smyth_2018,
  author    = {Dunn, Peter K. and Smyth, Gordon K.},
  doi       = {10.1007/978-1-4419-0118-7},
  isbn      = {978-1-4419-0117-0; 978-1-4419-0118-7},
  mrclass   = {62-01 (62-04 62J12)},
  mrnumber  = {3887706},
  pages     = {xx+562},
  publisher = {Springer, New York},
  series    = {Springer Texts in Statistics},
  title     = {Generalized linear models with examples in {R}},
  url       = {https://doi.org/10.1007/978-1-4419-0118-7},
  year      = {2018}
}

@book{fahrmeir_multivariate_2001,
  author    = {Fahrmeir, Ludwig and Tutz, Gerhard},
  date      = {2001},
  doi       = {10.1007/978-1-4757-3454-6},
  edition   = {Second Edition},
  file      = {Full Text PDF:/Users/jccum/Zotero/storage/7AH5RLKD/Fahrmeir and Tutz - 2001 - Multivariate Statistical Modelling Based on Generalized Linear Models.pdf:application/pdf},
  isbn      = {978-1-4419-2900-6 978-1-4757-3454-6},
  keywords  = {best fit, data analysis, expectation–maximization algorithm, Fitting, Generalized linear model, Regression analysis, Survival analysis, Time series},
  location  = {New York, {NY}},
  publisher = {Springer},
  rights    = {http://www.springer.com/tdm},
  series    = {Springer Series in Statistics},
  title     = {Multivariate Statistical Modelling Based on Generalized Linear Models},
  url       = {http://link.springer.com/10.1007/978-1-4757-3454-6},
  urldate   = {2026-01-28}
}

@manual{fahrmeir_package,
  author = {compiled by Kjetil B Halvorsen},
  doi    = {10.32614/CRAN.package.Fahrmeir},
  note   = {{R} package version 2016.5.31},
  title  = {{Fahrmeir}: Data from the Book "Multivariate Statistical Modelling Based on
            Generalized Linear Models", First Edition, by Ludwig Fahrmeir
            and Gerhard Tutz},
  url    = {https://CRAN.R-project.org/package=Fahrmeir},
  year   = {2016}
}

@book{faraway_extending_2016,
  abstract  = {Start Analyzing a Wide Range of Problems Since the publication of the bestselling, highly recommended first edition, R has considerably expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition takes advantage of the greater functionality now available in R and substantially revises and adds several topics.New to the Second Edition Expanded coverage of binary and binomial responses, including proportion responses, quasibinomial and beta regression, and applied considerations regarding these models New sections on Poisson models with dispersion, zero inflated count models, linear discriminant analysis, and sandwich and robust estimation for generalized linear models ({GLMs}) Revised chapters on random effects and repeated measures that reflect changes in the lme4 package and show how to perform hypothesis testing for the models using other methods New chapter on the Bayesian analysis of mixed effect models that illustrates the use of {STAN} and presents the approximation method of {INLA} Revised chapter on generalized linear mixed models to reflect the much richer choice of fitting software now available Updated coverage of splines and confidence bands in the chapter on nonparametric regression New material on random forests for regression and classification Revamped R code throughout, particularly the many plots using the ggplot2 package Revised and expanded exercises with solutions now included Demonstrates the Interplay of Theory and {PracticeThis} textbook continues to cover a range of techniques that grow from the linear regression model. It presents three extensions to the linear framework: {GLMs}, mixed effect models, and nonparametric regression models. The book explains data analysis using real examples and includes all the R commands necessary to reproduce the analyses.},
  author    = {Faraway, Julian J.},
  date      = {2016-01-01},
  edition   = {Second edition},
  isbn      = {978-1-4987-2096-0},
  keywords  = {Analysis of variance, {MATHEMATICS} / Probability \& Statistics / General, R (Computer program language)--Mathematical models, Regression analysis},
  location  = {Boca Raton},
  publisher = {Chapman and Hall/{CRC}},
  series    = {Chapman \& Hall/{CRC} Texts in Statistical Science Series},
  title     = {Extending the Linear Model with R : Generalized Linear, Mixed Effects and Nonparametric Regression Models, Second Edition},
  url       = {https://research.ebsco.com/plink/3806c0de-f79d-330d-9c4e-7bb136efaa53}
}

@article{friedman_regularization_2010,
  author       = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert},
  date         = {2010},
  doi          = {10.18637/jss.v033.i01},
  journaltitle = {Journal of Statistical Software},
  number       = {1},
  pages        = {1--22},
  title        = {Regularization Paths for Generalized Linear Models via Coordinate Descent},
  volume       = {33}
}

@manual{gee_package,
  author = {Vincent J Carey},
  doi    = {10.32614/CRAN.package.gee},
  note   = {{R} package version 4.13-29},
  title  = {{gee}: Generalized Estimation Equation Solver},
  url    = {https://CRAN.R-project.org/package=gee},
  year   = {2024}
}
@article{geepack_package_2002,
  author  = {Jun Yan},
  journal = {R-News},
  pages   = {12--14},
  title   = {{geepack}: Yet Another Package for Generalized Estimating Equations},
  volume  = {2/3},
  year    = {2002}
}

@article{hojsgaard_r_2005,
  abstract     = {This paper describes the core features of the R package geepack, which implements the generalized estimating equations ({GEE}) approach for fitting marginal generalized linear models to clustered data. Clustered data arise in many applications such as longitudinal data and repeated measures. The {GEE} approach focuses on models for the mean of the correlated observations within clusters without fully specifying the joint distribution of the observations. It has been widely used in statistical practice. This paper illustrates the application of the {GEE} approach with geepack through an example of clustered binary data.},
  author       = {Højsgaard, Søren and Halekoh, Ulrich and Yan, Jun},
  date         = {2005-12-22},
  doi          = {10.18637/jss.v015.i02},
  journaltitle = {Journal of Statistical Software},
  number       = {2},
  pages        = {1 -- 11},
  shortjournal = {J. Stat. Soft.},
  title        = {The R Package geepack for Generalized Estimating Equations},
  url          = {https://www.jstatsoft.org/index.php/jss/article/view/v015i02},
  urldate      = {2026-02-04},
  volume       = {15}
}

@article{leathem_predictive_1987,
  abstract     = {A new approach for predicting long-term survival of breast-cancer patients is the detection of carbohydrate expression in paraffin-embedded sections of the primary tumour. The binding of a lectin ({HPA}), derived from the albumin gland of the Roman snail, Helix pomatia, to N-acetyl-galactosaminyl oligosaccharides appears valuable in assessing long-term prognosis. The clinical progress of 179 patients, followed-up for 15-20 years, was related to staining of paraffin sections of their primary breast cancers by {HPA}. All patients had had mastectomy but were not stratified by pathology or treatment. There were significant differences, in premenopausal patients, between groups with and without {HPA} staining in both time to first recurrence and survival time. {HPA} binding provides an extra tool for staging to aid decisions in early adjuvant treatment, with the advantage of being applicable to routinely fixed paraffin-embedded material.},
  author       = {Leathem, A.J. and Brooks, {SusanA}.},
  date         = {1987-05-09},
  doi          = {10.1016/S0140-6736(87)90482-X},
  issn         = {0140-6736},
  journaltitle = {The Lancet},
  number       = {8541},
  pages        = {1054--1056},
  publisher    = {Elsevier},
  title        = {Predictive Value of Lectin Binding on Breast-Cancer Recurrence and Survival},
  url          = {https://doi.org/10.1016/S0140-6736(87)90482-X},
  urldate      = {2026-04-08},
  volume       = {329}
}

@misc{noauthor_poisson_2026,
  copyright = {Creative Commons Attribution-ShareAlike License},
  journal   = {Wikipedia},
  language  = {en},
  month     = jan,
  note      = {Page Version ID: 1335765635},
  title     = {Poisson distribution},
  url       = {https://en.wikipedia.org/w/index.php?title=Poisson_distribution&oldid=1335765635},
  urldate   = {2026-02-04},
  year      = {2026}
}

@article{tay_elastic_2023,
  author       = {Tay, J. Kenneth and Narasimhan, Balasubramanian and Hastie, Trevor},
  date         = {2023},
  doi          = {10.18637/jss.v106.i01},
  journaltitle = {Journal of Statistical Software},
  number       = {1},
  pages        = {1--31},
  title        = {Elastic Net Regularization Paths for All Generalized Linear Models},
  volume       = {106}
}

@manual{tidyr_package,
  author = {Hadley Wickham and Davis Vaughan and Maximilian Girlich},
  note   = {{R} package version 1.3.2, https://github.com/tidyverse/tidyr},
  title  = {{tidyr}: Tidy Messy Data},
  url    = {https://tidyr.tidyverse.org},
  year   = {2025}
}

@book{venables_modern_2002,
  address   = {New York},
  author    = {W. N. Venables and B. D. Ripley},
  edition   = {Fourth},
  note      = {ISBN 0-387-95457-0},
  publisher = {Springer},
  title     = {Modern Applied Statistics with {S}},
  url       = {https://www.stats.ox.ac.uk/pub/MASS4/},
  year      = {2002}
}

@book{wickham_ggplot2_2016,
  author    = {Hadley Wickham},
  isbn      = {978-3-319-24277-4},
  publisher = {Springer-Verlag New York},
  title     = {{ggplot2}: Elegant Graphics for Data Analysis},
  url       = {https://ggplot2.tidyverse.org},
  year      = {2016}
}