実行環境
## R version 4.2.2 (2022-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 22621)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Japanese_Japan.utf8 LC_CTYPE=Japanese_Japan.utf8
## [3] LC_MONETARY=Japanese_Japan.utf8 LC_NUMERIC=C
## [5] LC_TIME=Japanese_Japan.utf8
##
## attached base packages:
## [1] parallel stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] rstan_2.32.3 StanHeaders_2.26.28
## [3] DHARMa.helpers_0.0.0.9000 DHARMa_0.4.6
## [5] brms_2.20.5 Rcpp_1.0.11
## [7] rethinking_2.13.2 glmmTMB_1.1.7
## [9] systemfonts_1.0.4 extrafont_0.18
## [11] concaveman_1.1.0 ggforce_0.4.1
## [13] ggdag_0.2.7 dagitty_0.3-1
## [15] kableExtra_1.3.4 knitr_1.45
## [17] DT_0.30 patchwork_1.1.2
## [19] data.table_1.14.6 see_0.7.5.5
## [21] report_0.5.7.4 parameters_0.20.3
## [23] performance_0.10.3 modelbased_0.8.6.3
## [25] insight_0.19.1.4 effectsize_0.8.3.6
## [27] datawizard_0.7.1.1 correlation_0.8.4
## [29] bayestestR_0.13.1 easystats_0.6.0.8
## [31] ggsci_2.9 lemon_0.4.6
## [33] lubridate_1.9.2 forcats_1.0.0
## [35] stringr_1.5.0 dplyr_1.1.2
## [37] purrr_1.0.2 readr_2.1.4
## [39] tidyr_1.3.0 tibble_3.2.1
## [41] ggplot2_3.4.4 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.2.0 lme4_1.1-34
## [4] htmlwidgets_1.6.2 grid_4.2.2 munsell_0.5.0
## [7] codetools_0.2-18 miniUI_0.1.1.1 withr_2.5.2
## [10] Brobdingnag_1.2-9 colorspace_2.0-3 highr_0.10
## [13] rstudioapi_0.15.0 stats4_4.2.2 Rttf2pt1_1.3.12
## [16] bayesplot_1.10.0 labeling_0.4.3 emmeans_1.8.8
## [19] polyclip_1.10-4 farver_2.1.1 bridgesampling_1.1-2
## [22] coda_0.19-4 vctrs_0.6.3 generics_0.1.3
## [25] TH.data_1.1-2 xfun_0.39 timechange_0.1.1
## [28] R6_2.5.1 markdown_1.11 cachem_1.0.8
## [31] promises_1.2.0.1 scales_1.2.1 multcomp_1.4-25
## [34] gtable_0.3.4 processx_3.8.0 tidygraph_1.2.2
## [37] sandwich_3.0-2 rlang_1.1.1 splines_4.2.2
## [40] extrafontdb_1.0 TMB_1.9.6 checkmate_2.1.0
## [43] inline_0.3.19 yaml_2.3.7 reshape2_1.4.4
## [46] abind_1.4-5 threejs_0.3.3 crosstalk_1.2.0
## [49] backports_1.4.1 httpuv_1.6.7 tensorA_0.36.2
## [52] tools_4.2.2 bookdown_0.35 ellipsis_0.3.2
## [55] jquerylib_0.1.4 posterior_1.5.0 plyr_1.8.8
## [58] base64enc_0.1-3 ps_1.7.2 prettyunits_1.2.0
## [61] zoo_1.8-11 magrittr_2.0.3 colourpicker_1.3.0
## [64] mvtnorm_1.2-3 matrixStats_0.63.0 hms_1.1.3
## [67] shinyjs_2.1.0 mime_0.12 evaluate_0.23
## [70] xtable_1.8-4 shinystan_2.6.0 gridExtra_2.3
## [73] shape_1.4.6 rstantools_2.3.1.1 compiler_4.2.2
## [76] V8_4.2.2 crayon_1.5.2 minqa_1.2.5
## [79] htmltools_0.5.4 later_1.3.0 tzdb_0.3.0
## [82] RcppParallel_5.1.6 tweenr_2.0.2 MASS_7.3-58.1
## [85] boot_1.3-28 Matrix_1.6-1 cli_3.6.1
## [88] igraph_1.3.5 pkgconfig_2.0.3 numDeriv_2016.8-1.1
## [91] xml2_1.3.5 dygraphs_1.1.1.6 svglite_2.1.1
## [94] QuickJSR_1.0.5 bslib_0.5.1 webshot_0.5.5
## [97] estimability_1.4.1 rvest_1.0.3 distributional_0.3.2
## [100] callr_3.7.3 digest_0.6.31 rmarkdown_2.25
## [103] curl_4.3.3 shiny_1.7.5.1 gtools_3.9.4
## [106] nloptr_2.0.3 lifecycle_1.0.4 nlme_3.1-160
## [109] jsonlite_1.8.4 viridisLite_0.4.2 fansi_1.0.3
## [112] pillar_1.9.0 lattice_0.20-45 loo_2.6.0
## [115] fastmap_1.1.1 httr_1.4.7 pkgbuild_1.4.2
## [118] survival_3.5-5 glue_1.6.2 xts_0.12.2
## [121] shinythemes_1.2.0 stringi_1.7.12 sass_0.4.7
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., & White, J. S. S. (2009). Generalized linear mixed models: A practical guide for ecology and evolution. In Trends in Ecology and Evolution (No. 3; Vol. 24, pp. 127–135).
Brooks, M. E., Kristensen, K., van Benthem, K. J., Magnusson, A., Berg, C. W., Nielsen, A., Skaug, H. J., Maechler, M., & Bolker, B. M. (2017). glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. The R Journal, 9(2), 378–400. https://doi.org/10.32614/RJ-2017-066
Chang, W. (2018). R graphics cookbook: Practical recipes for visualizing data. “O’Reilly Media, Inc.”
Dunn, P. K., & Smyth, G. K. (2018). Generalized linear models with examples in R. Springer New York.
Fox, G. A., Negrete-Yankelevich, S., & Sosa, V. J. (2015). Ecological statistics: Contemporary theory and application. Oxford University Press.
Gelman, A., Hill, J., & Vehtari, A. (2020). Regression and other stories. Cambridge University Press.
Harrison, X. A., Donaldson, L., Correa-Cano, M. E., Evans, J., Fisher, D. N., Goodwin, C. E. D., Robinson, B. S., Hodgson, D. J., & Inger, R. (2018). A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ, 2018(5).
McElreath, R. (2009). Statistical rethinking: A bayeasian course with example in r and stan. CRC Press.
Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data. “O’Reilly Media, Inc.”
Zuur, A. F. (2009). Mixed effects models and extensions in ecology with R. Springer.
大東健太郎. (2010). 線形モデルから一般化線形モデル(GLM)へ. 雑草研究, 55(4), 268–274.
松村優哉., 湯谷啓明., 紀ノ定保礼., & 前田和. (2021). RユーザのためのRstudio[実践]入門 tidyverseによるモダンな分析フローの世界 改訂2版. 技術評論社.
松浦健太郎. (2012). StanとRでベイズ統計モデリング. 共立出版.
松浦健太郎. (2016). StanとRでベイズ統計モデリング. 共立出版.
馬場真哉. (2015). 平均・分散から始める一般化線形モデル入門. プレアデス出版.
馬場真哉. (2019). RとStanではじめるベイズ統計モデリングによるデータ分析. 講談社.