1. Scamander, N. Fantastic Beasts and Where to Find Them. Hogwarts, UK: Hogwarts Library Book; 1927.
Google Scholar2. Gelman, A, Hill, J, Vehtari, A. Regression and Other Stories. Cambridge, UK: Cambridge University Press; 2020.
Google Scholar | Crossref3. Mac Nally, R . Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables. Biodivers Conserv. 2002;11:1397-1401.
Google Scholar | Crossref4. Zuur, AF, Ieno, EN, Smith, GM. Analysing Ecological Data. New York, NY: Springer; 2007.
Google Scholar | Crossref5. Bolker, BM, Brooks, ME, Clark, CJ, et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol Evol. 2009;24:127-135.
Google Scholar | Crossref | Medline | ISI6. Zuur, AF, Ieno, EN, Walker, NJ, Saveliev, AA, Smith, GM. Mixed Effects Models and Extensions in Ecology With R. New York, NY: Springer; 2009.
Google Scholar | Crossref7. Harrison, XA, Donaldson, L, Correa-Cano, ME, et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ. 2018;6:e4794.
Google Scholar | Crossref8. Burnham, KP, Anderson, DR. Model Selection and Multimodel Inference. 2nd ed. New York, NY: Springer; 2002.
Google Scholar9. Krausman, PR. Important considerations when using models. J Wildlife Manage. 2020;84:1221-1223.
Google Scholar | Crossref10. McElreath, R. Statistical Rethinking: A Bayesian Course With Examples in R and Stan. 2nd ed. London, England: Chapman and Hall/CRC Press; 2020.
Google Scholar | Crossref11. Ellison, AM, Dennis, B. Paths to statistical fluency for ecologists. Front Ecol Environ. 2010;8:362-370.
Google Scholar | Crossref12. delMas, RC. Statistical literacy, reasoning, and learning: a commentary. J Stat Educ. 2002;10:3.
Google Scholar | Crossref13. Cohen, J, Cohen, P, West, SG, Alken, LS. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. 3rd ed. Mahwah, NJ: Lawrence Erlbaum Associates; 2003.
Google Scholar14. Korner-Nievergelt, F, Roth, T, von Felten, S, Guélat, J, Almasi, B, Korner-Nievergelt, P. Bayesian Data Analysis in Ecology Using Linear Models With R, BUGS, and Stan. 1st ed. London, England: Academic Press; 2015.
Google Scholar15. Zuur, AF, Ieno, EN. A protocol for conducting and presenting results of regression-type analyses. Methods Ecol Evol. 2016;7:636-645.
Google Scholar | Crossref16. Sutherland, WJ . Planning a research programme. In: Sutherland, WJ , ed. Ecological Census Techniques. Cambridge, UK: Cambridge University Press; 2006:1-10.
Google Scholar | Crossref17. Hamilton, IM . Habitat selection. In: Breed, MD, Moore, J, eds. Encyclopedia of Animal Behavior. London, England: Academic Press; 2010:38-43.
Google Scholar | Crossref18. Boyce, MS, Vernier, PR, Nielsen, SE, Schmiegelow, FKA. Evaluating resource selection functions. Ecol Model. 2002;157:281-300.
Google Scholar | Crossref19. Murray, DL, Sandercock, BK. Population Ecology in Practice. New York, NY: Wiley; 2020.
Google Scholar20. R Core Team . R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2020. https://www.R-project.org/
Google Scholar21. R Studio Team . RStudio: Integrated Development for R. Boston, MA: RStudio Inc.; 2020.
Google Scholar22. Buckley, YM . Generalized linear models. In: Fox, GA, Negrete-Yankelevich, S, Sosa, VJ, eds. Ecological Statistics: Contemporary Theory and Application. Oxford, UK: Oxford University Press; 2015:131-148.
Google Scholar | Crossref23. Bolker, BM. Ecological Models and Data in R. Princeton, NJ: Princeton University Press; 2009.
Google Scholar24. Pekár, S, Brabec, M. Modern Analysis of Biological Data: Generalized Linear Models in R. Brno, Czech Republic: Masaryk University Press; 2016.
Google Scholar25. Fox, J. Applied Regression Analysis and Generalized Linear Models. Los Angeles, CA: SAGE; 2016.
Google Scholar26. Dunteman, GH, Ho, M-HR. An Introduction to Generalized Linear Models. Thousand Oaks, CA: SAGE; 2006.
Google Scholar | Crossref27. Dormann, CF, Elith, J, Bacher, S, et al. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography. 2013;36:27-46.
Google Scholar | Crossref | ISI28. Fox, J, Weisberg, S. An R Companion to Applied Regression. 3rd ed. Thousand Oaks, CA: SAGE; 2019.
Google Scholar29. Zuur, AF, Ieno, EN, Elphick, CS. A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol. 2010;1:3-14.
Google Scholar | Crossref30. Fox, J. Regression Diagnostics: An Introduction. 2nd ed. Los Angeles, CA: SAGE; 2020.
Google Scholar31. Westfall, PH, Arias, AL. Understanding Regression Analysis: A Conditional Distribution Approach. Boca Raton, FL: CRC Press; 2020.
Google Scholar | Crossref32. Faraway, JJ. Linear Models With R. 2nd ed. Boca Raton, FL: CRC Press; 2014.
Google Scholar33. Faraway, JJ. Extending the Linear Models With R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. 2nd ed. Boca Raton, FL: CRC Press; 2016.
Google Scholar34. Halsey, LG. The reign of the p-value is over: what alternative analyses could we employ to fill the power vacuum? Biol Lett. 2019;15:20190174.
Google Scholar | Crossref | Medline35. Dunn, PK, Smyth, GK. Generalized Linear Models With Examples in R. New York, NY: Springer; 2018.
Google Scholar | Crossref36. Dunn, KP, Smyth, GK. Randomized quantile residuals. J Comput Graph Stat. 1996;5:1-10.
Google Scholar37. Hartig, F . DHARMa: residual diagnostics for hierarchical (multi-level / mixed) regression models, R package version 0.3.2.0, 2020. https://CRAN.R-project.org/package=DHARMa
Google Scholar38. Lüdecke, D, Ben-Shachar, MS, Makowski, D. Describe and understand your model’s parameters. CRAN, 2020. https://easystats.github.io/parameters
Google Scholar39. Canty, A, Ripley, B. boot: Bootstrap R (S-Plus) functions, R package version 1.3-25, 2020. https://cran.r-project.org/web/packages/boot
Google Scholar40. Upton, G, Cook, I. Oxford Dictionary of Statistics. Oxford, UK: Oxford University Press; 2002.
Google Scholar41. Best, H, Wolf, C. Logistic regression. In: Best, H, Wolf, C, eds. The Sage Handbook of Regression Analysis and Causal Inference. Los Angeles, CA: SAGE; 2015:153-172.
Google Scholar42. Lüdecke, D, Makowski, D, Waggoner, P, Patil, I. Assessment of regression models performance. CRAN, 2020. https://easystats.github.io/performance
Google Scholar43. Tjur, T. Coefficients of determination in logistic regression models—a new proposal: the coefficient of discrimination. Am Stat. 2009;63:366-372.
Google Scholar | Crossref | ISI44. Fawcett, T. An introduction to ROC analysis. Pattern Recogn Lett. 2006;27:861-874.
Google Scholar | Crossref | ISI45. Robin, X, Turck, N, Hainard, A, et al. PROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.
Google Scholar | Crossref | Medline | ISI46. Harrell, FE. Regression Modeling Strategies. With Applications to Linear Models, Logistic and Ordinal Regression, and Survival Analysis. New York, NY: Springer; 2015.
Google Scholar | Crossref47. Firth, D. Bias reduction of maximum likelihood estimates. Biometrika. 1993;80:27-38.
Google Scholar | Crossref | ISI48. Heinze, G, Ploner, M. logistf: Firth’s bias-reduced logistic regression, R package version 1.23.1, 2020. https://CRAN.R-project.org/package=logistf
Google Scholar49. Breheny, P, Burchett, W. Visualization of regression models using visreg. R J. 2017;9:56-71.
Google Scholar | Crossref50. Ferrari, S, Cribari-Neto, F. Beta regression for modelling rates and proportions. J Appl Stat. 2004;31:799-815.
Google Scholar | Crossref | ISI51. Smithson, M, Verkuilen, J. A better lemon squeezer? Maximum-likelihood regression with beta-distributed dependent variables. Psychol Methods. 2006;11:54-71.
Google Scholar | Crossref | Medline | ISI52. Cribari-Neto, F, Zeileis, A. Beta regression in R. J Stat Softw. 2010;34:1-24.
Google Scholar | Crossref | ISI53. Brooks, ME, Kristensen, K, van Benthem, KJ, et al. GlmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 2017;9:378-400.
Google Scholar | Crossref54. Kruschke, JK. Doing Bayesian Data Analysis. London, England: Academic Press; 2015.
Google Scholar55. Bürkner, P-C . Brms: an R package for Bayesian multilevel models using Stan. J Stat Softw. 2017;80:1-28.
Google Scholar | Crossref56. Bürkner, P-C . Advanced Bayesian multilevel modeling with the R package brms. R J. 2018;10:395-411.
Google Scholar | Crossref57. Gabry, J . shinystan: interactive visual and numerical diagnostics and posterior analysis for Bayesian models, R package version 2.5.0, 2018. http://CRAN.R-project.org/package=shinystan
Google Scholar58. van de Schoot, R, Miočević, M. Small Sample Size Solutions: A Guide for Applied Researchers and Practitioners. Abingdon, UK: Routledge; 2020.
Google Scholar | Crossref59. Goodrich, B, Gabry, J, Ali, I, Brilleman, S. rstanarm: Bayesian applied regression modeling via Stan, R package version 2.21.1, 2020. https://mc-stan.org/rstanarm
Google Scholar60. Zuur, AF, Hilbe, JM, Ieno, EN. A Beginner’s Guide to GLM and GLMM With R. A Frequentist and Bayesian Perspective for Ecologists. Newburgh, UK: Highland Statistics Ltd.; 2013.
Google Scholar