This repository contains all of the data and R scripts necessary to reproduce the analyses presented in From the margins to the Mainstream: understanding the Polychrome Tradition Expansion in Central Amazon through spatial and chronological modelling, currently in review (16/06/2023).
The repository is organised into four folders:
Code
- containing R scriptsData
- containing the radiocarbon and spatial dataFigures
- comprising the images presented in the manuscript and its supplementary informationR_Images
- .RData files of the modelling output, for visualisation and diagnostics
R version 4.3.0 (2023-04-21 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19044)
Matrix products: default
locale:
[1] LC_COLLATE=English_United Kingdom.utf8 LC_CTYPE=English_United Kingdom.utf8
[3] LC_MONETARY=English_United Kingdom.utf8 LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.utf8
time zone: Europe/London
tzcode source: internal
attached base packages:
[1] parallel grid stats graphics grDevices utils datasets methods base
other attached packages:
[1] MCMCvis_0.16.0 patchwork_1.1.2 latex2exp_0.9.6 coda_0.19-4 truncnorm_1.0-9
[6] nimbleCarbon_0.2.1 nimble_1.0.0 ggsn_0.5.0 ggplot2_3.4.2 rgdal_1.6-7
[11] sf_1.0-13 rworldmap_1.3-6 sp_1.6-1 rcarbon_1.5.0
loaded via a namespace (and not attached):
[1] bitops_1.0-7 DBI_1.1.3 deldir_1.0-9 gridExtra_2.3
[5] rlang_1.1.1 magrittr_2.0.3 maptools_1.1-7 cAIC4_1.0
[9] e1071_1.7-13 compiler_4.3.0 spatstat.geom_3.2-1 mgcv_1.8-42
[13] spatstat.model_3.2-4 png_0.1-8 spatstat.linnet_3.1-1 vctrs_0.6.2
[17] maps_3.4.1 reshape2_1.4.4 stringr_1.5.0 pkgconfig_2.0.3
[21] backports_1.4.1 utf8_1.2.3 pracma_2.4.2 nloptr_2.0.3
[25] purrr_1.0.1 xfun_0.39 goftest_1.2-3 sjmisc_2.8.9
[29] ggeffects_1.2.2 spatstat.utils_3.0-3 jpeg_0.1-10 broom_1.0.4
[33] R6_2.5.1 stringi_1.7.12 spatstat.data_3.0-1 car_3.1-2
[37] boot_1.3-28.1 rpart_4.1.19 numDeriv_2016.8-1.1 estimability_1.4.1
[41] Rcpp_1.0.10 iterators_1.0.14 knitr_1.43 tensor_1.5
[45] modelr_0.1.11 fields_14.1 snow_0.4-4 igraph_1.4.3
[49] Matrix_1.5-4 splines_4.3.0 tidyselect_1.2.0 abind_1.4-5
[53] viridis_0.6.3 codetools_0.2-19 spatstat.random_3.1-5 spatstat.explore_3.2-1
[57] RLRsim_3.1-8 sjlabelled_1.2.0 lattice_0.21-8 tibble_3.2.1
[61] plyr_1.8.8 withr_2.5.0 spatstat_3.0-6 bayestestR_0.13.1
[65] foreign_0.8-84 units_0.8-2 proxy_0.4-27 polyclip_1.10-4
[69] pillar_1.9.0 carData_3.0-5 KernSmooth_2.23-20 foreach_1.5.2
[73] stats4_4.3.0 insight_0.19.2 generics_0.1.3 munsell_0.5.0
[77] scales_1.2.1 minqa_1.2.5 class_7.3-21 glue_1.6.2
[81] sjPlot_2.8.14 emmeans_1.8.6 tools_4.3.0 lme4_1.1-33
[85] mvtnorm_1.2-1 dotCall64_1.0-2 cowplot_1.1.1 tidyr_1.3.0
[89] colorspace_2.1-0 nlme_3.1-162 performance_0.10.4 RgoogleMaps_1.4.5.3
[93] cli_3.6.1 spatstat.sparse_3.0-1 ggmap_3.0.2 spam_2.9-1
[97] fansi_1.0.4 viridisLite_0.4.2 sjstats_0.18.2 dplyr_1.1.2
[101] doSNOW_1.0.20 gtable_0.3.3 classInt_0.4-9 lifecycle_1.0.3
[105] httr_1.4.6 MASS_7.3-58.4