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Chapter 26 Reproducibility
It is important for work to be reproducible by others. Reproducibility and replicability are key goals of the open science movement (Gandrud, 2020; Open Science Collaboration, 2015; Tackett, Brandes, King, et al., 2019; Tackett, Brandes, & Reardon, 2019). To that aim, I provide information below on my setup that was used to generate the results found in this book. If you run (all of) my code in the same order with the exact same setup, you should get the same results—but let me know if not!
26.1 Session Info
R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
[4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
[7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
time zone: UTC
tzcode source: system (glibc)
attached base packages:
[1] grid stats4 splines stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] mirtCAT_1.14 shiny_1.9.1 strucchange_1.5-4
[4] sandwich_3.1-1 zoo_1.8-12 dmacs_0.1.0.9002
[7] dagitty_0.3-4 nFactors_2.4.1.1 corrplot_0.95
[10] glmnet_4.1-8 LiblineaR_2.10-24 elasticnet_1.3
[13] lars_1.3 ordinalForest_2.4-4 ranger_0.17.0
[16] e1071_1.7-16 randomForest_4.7-1.2 caret_6.0-94
[19] ggrepel_0.9.6 car_3.1-3 carData_3.0-5
[22] msir_1.3.3 ggpubr_0.6.0 gridExtra_2.3
[25] uroc_0.1.0 PredictABEL_1.2-4 ResourceSelection_0.3-6
[28] rms_6.8-2 Hmisc_5.2-0 ROCR_1.0-11
[31] pROC_1.18.5 magrittr_2.0.3 mirt_1.42
[34] lattice_0.22-6 drc_3.0-1 semTools_0.5-6
[37] nonnest2_0.5-8 quantreg_5.99 SparseM_1.84-2
[40] mice_3.16.0 snow_0.4-4 simsem_0.5-16
[43] DT_0.33 simstandard_0.6.3 MASS_7.3-61
[46] kableExtra_1.4.0 rockchalk_1.8.157 semPlot_1.1.6
[49] here_1.0.1 MOTE_1.0.2 performance_0.12.4
[52] gtheory_0.1.2 lme4_1.1-35.5 Matrix_1.7-1
[55] irrNA_0.2.3 irrICC_1.0 irrCAC_1.0
[58] psychmeta_2.7.0 lavaan_0.6-19 MBESS_4.9.3
[61] blandr_0.6.0 psych_2.4.6.26 viridis_0.6.5
[64] viridisLite_0.4.2 bookdown_0.41 rmarkdown_2.29
[67] knitr_1.49 tinytex_0.54 lubridate_1.9.3
[70] forcats_1.0.0 stringr_1.5.1 purrr_1.0.2
[73] readr_2.1.5 tidyr_1.3.1 tibble_3.2.1
[76] ggplot2_3.5.1 tidyverse_2.0.0 dplyr_1.1.4
[79] petersenlab_1.0.11
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.2 vroom_1.6.5 progress_1.2.3
[4] kutils_1.73 nnet_7.3-19 TH.data_1.1-2
[7] vctrs_0.6.5 digest_0.6.37 png_0.1-8
[10] corpcor_1.6.10 shape_1.4.6.1 proxy_0.4-27
[13] deldir_2.0-4 parallelly_1.39.0 combinat_0.0-8
[16] permute_0.9-7 reshape_0.8.9 reshape2_1.4.4
[19] foreach_1.5.2 httpuv_1.6.15 withr_3.0.2
[22] xfun_0.49 survival_3.7-0 MatrixModels_0.5-3
[25] SimDesign_2.17.1 systemfonts_1.1.0 gtools_3.9.5
[28] V8_6.0.0 pbapply_1.7-2 R.oo_1.27.0
[31] Formula_1.2-5 prettyunits_1.2.0 promises_1.3.0
[34] beepr_2.0 rstatix_0.7.2 globals_0.16.3
[37] rstudioapi_0.17.1 pan_1.9 generics_0.1.3
[40] base64enc_0.1-3 curl_6.0.0 mitools_2.4
[43] jmvcore_2.6.3 quadprog_1.5-8 xtable_1.8-4
[46] OpenMx_2.21.13 evaluate_1.0.1 hms_1.1.3
[49] colorspace_2.1-1 polynom_1.4-1 later_1.3.2
[52] future.apply_1.11.3 XML_3.99-0.17 class_7.3-22
[55] pillar_1.9.0 nlme_3.1-166 iterators_1.0.14
[58] compiler_4.4.2 stringi_1.8.4 gower_1.0.1
[61] jomo_2.7-6 minqa_1.2.8 plyr_1.8.9
[64] crayon_1.5.3 abind_1.4-8 bit_4.5.0
[67] codetools_0.2-20 multcomp_1.4-26 recipes_1.1.0
[70] bslib_0.8.0 mime_0.12 Rcpp_1.0.13-1
[73] ez_4.4-0 qgraph_1.9.8 interp_1.1-6
[76] mix_1.0-12 utf8_1.2.4 pbivnorm_0.6.0
[79] listenv_0.9.1 checkmate_2.3.2 openxlsx_4.2.7.1
[82] ggsignif_0.6.4 lpSolve_5.6.22 tzdb_0.4.0
[85] svglite_2.1.3 pkgconfig_2.0.3 tools_4.4.2
[88] cachem_1.1.0 DBI_1.2.3 numDeriv_2016.8-1.1
[91] fastmap_1.2.0 scales_1.3.0 audio_0.1-11
[94] broom_1.0.7 sass_0.4.9 coda_0.19-4.1
[97] insight_0.20.5 PBSmodelling_2.69.3 rpart_4.1.23
[100] farver_2.1.2 mgcv_1.9-1 yaml_2.3.10
[103] latticeExtra_0.6-30 foreign_0.8-87 cli_3.6.3
[106] lifecycle_1.0.4 mvtnorm_1.3-2 lava_1.8.0
[109] sessioninfo_1.2.2 backports_1.5.0 timechange_0.3.0
[112] gtable_0.3.6 progressr_0.15.0 parallel_4.4.2
[115] testthat_3.2.1.1 jsonlite_1.8.9 mitml_0.4-5
[118] RPushbullet_0.3.4 bit64_4.5.2 brio_1.1.5
[121] glasso_1.11 vegan_2.6-8 zip_2.3.1
[124] RcppParallel_5.1.9 GPArotation_2024.3-1 jquerylib_0.1.4
[127] sem_3.1-16 polspline_1.1.25 R.utils_2.12.3
[130] timeDate_4041.110 dcurver_0.9.2 lisrelToR_0.3
[133] htmltools_0.5.8.1 glue_1.8.0 tcltk_4.4.2
[136] rprojroot_2.0.4 mclust_6.1.1 mnormt_2.1.1
[139] jpeg_0.1-10 boot_1.3-31 igraph_2.1.1
[142] R6_2.5.1 arm_1.14-4 fdrtool_1.2.18
[145] Deriv_4.1.6 CompQuadForm_1.4.3 labeling_0.4.3
[148] cluster_2.1.6 ipred_0.9-15 nloptr_2.1.1
[151] tidyselect_1.2.1 plotrix_3.8-4 htmlTable_2.4.3
[154] xml2_1.3.6 future_1.34.0 ModelMetrics_1.2.2.2
[157] munsell_0.5.1 mi_1.1 animation_2.7
[160] data.table_1.16.2 htmlwidgets_1.6.4 RColorBrewer_1.1-3
[163] rlang_1.1.4 fansi_1.0.6 hardhat_1.4.0
[166] prodlim_2024.06.25 References
Gandrud, C. (2020). Reproducible research with R and R studio (3rd ed.). CRC Press. https://www.routledge.com/Reproducible-Research-with-R-and-RStudio/Gandrud/p/book/9780367143985
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251). https://doi.org/10.1126/science.aac4716
Tackett, J. L., Brandes, C. M., King, K. M., & Markon, K. E. (2019). Psychology’s replication crisis and clinical psychological science. Annual Review of Clinical Psychology, 15(1), 579–604. https://doi.org/10.1146/annurev-clinpsy-050718-095710
Tackett, J. L., Brandes, C. M., & Reardon, K. W. (2019). Leveraging the open science framework in clinical psychological assessment research. Psychological Assessment, 31(12), 1386–1394. https://doi.org/10.1037/pas0000583