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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] patchwork_1.1.2 brms_2.18.0 Rcpp_1.0.9 DHARMa_0.4.6
## [5] statmod_1.5.0 glmmTMB_1.1.5 ggeffects_1.1.4 see_0.7.5.5
## [9] report_0.5.7.4 parameters_0.20.3 performance_0.10.3 modelbased_0.8.6.3
## [13] insight_0.19.1.4 effectsize_0.8.3.6 datawizard_0.7.1.1 correlation_0.8.4
## [17] bayestestR_0.13.1 easystats_0.6.0.8 lme4_1.1-31 Matrix_1.5-1
## [21] forcats_0.5.2 stringr_1.5.0 dplyr_1.0.10 purrr_1.0.0
## [25] readr_2.1.3 tidyr_1.2.1 tibble_3.1.8 ggplot2_3.4.2
## [29] tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] readxl_1.4.1 backports_1.4.1 plyr_1.8.8
## [4] igraph_1.3.5 TMB_1.9.1 splines_4.2.2
## [7] crosstalk_1.2.0 gap.datasets_0.0.5 inline_0.3.19
## [10] rstantools_2.2.0 digest_0.6.31 htmltools_0.5.4
## [13] fansi_1.0.3 magrittr_2.0.3 checkmate_2.1.0
## [16] googlesheets4_1.0.1 tzdb_0.3.0 modelr_0.1.10
## [19] RcppParallel_5.1.6 matrixStats_0.63.0 xts_0.12.2
## [22] timechange_0.1.1 prettyunits_1.1.1 colorspace_2.0-3
## [25] rvest_1.0.3 haven_2.5.1 xfun_0.36
## [28] callr_3.7.3 crayon_1.5.2 jsonlite_1.8.4
## [31] zoo_1.8-11 glue_1.6.2 gtable_0.3.3
## [34] gargle_1.2.1 emmeans_1.8.3 V8_4.2.2
## [37] distributional_0.3.1 pkgbuild_1.4.0 rstan_2.26.13
## [40] abind_1.4-5 scales_1.2.1 mvtnorm_1.1-3
## [43] DBI_1.1.3 miniUI_0.1.1.1 xtable_1.8-4
## [46] StanHeaders_2.26.13 stats4_4.2.2 DT_0.27
## [49] htmlwidgets_1.6.1 httr_1.4.4 threejs_0.3.3
## [52] posterior_1.3.1 ellipsis_0.3.2 pkgconfig_2.0.3
## [55] loo_2.5.1 farver_2.1.1 sass_0.4.5
## [58] dbplyr_2.2.1 utf8_1.2.2 labeling_0.4.2
## [61] tidyselect_1.2.0 rlang_1.1.1 reshape2_1.4.4
## [64] later_1.3.0 munsell_0.5.0 cellranger_1.1.0
## [67] tools_4.2.2 cachem_1.0.6 cli_3.6.0
## [70] generics_0.1.3 broom_1.0.2 evaluate_0.20
## [73] fastmap_1.1.0 yaml_2.3.7 processx_3.8.0
## [76] knitr_1.42 fs_1.5.2 nlme_3.1-160
## [79] mime_0.12 xml2_1.3.3 gap_1.4-2
## [82] compiler_4.2.2 bayesplot_1.10.0 shinythemes_1.2.0
## [85] rstudioapi_0.14 png_0.1-8 curl_4.3.3
## [88] reprex_2.0.2 bslib_0.4.2 stringi_1.7.8
## [91] highr_0.10 ps_1.7.2 Brobdingnag_1.2-9
## [94] lattice_0.20-45 nloptr_2.0.3 markdown_1.4
## [97] shinyjs_2.1.0 tensorA_0.36.2 vctrs_0.5.1
## [100] pillar_1.9.0 lifecycle_1.0.3 jquerylib_0.1.4
## [103] bridgesampling_1.1-2 estimability_1.4.1 httpuv_1.6.7
## [106] R6_2.5.1 bookdown_0.31 promises_1.2.0.1
## [109] gridExtra_2.3 codetools_0.2-18 boot_1.3-28
## [112] colourpicker_1.2.0 MASS_7.3-58.1 gtools_3.9.4
## [115] assertthat_0.2.1 withr_2.5.0 shinystan_2.6.0
## [118] parallel_4.2.2 hms_1.1.2 grid_4.2.2
## [121] coda_0.19-4 minqa_1.2.5 rmarkdown_2.20
## [124] googledrive_2.0.0 numDeriv_2016.8-1.1 shiny_1.7.4
## [127] lubridate_1.9.0 base64enc_0.1-3 dygraphs_1.1.1.6References
Chang, W. (2018). R graphics cookbook: Practical recipes for visualizing data. “O’Reilly Media, Inc.”
Fox, G., & Sosa, V. (2015). Mixture models for overdispersed data. In Ecological statictics: Contemporary theory and application (pp. 284–308). Oxford 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).
Wickham, H., & Grolemund, G. (2016). R for data science: Import, tidy, transform, visualize, and model data. “O’Reilly Media, Inc.”
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.
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