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Report R environment (packages, system, etc.)

Usage

# S3 method for class 'sessionInfo'
report(x, ...)

report_packages(session = NULL, include_R = TRUE, ...)

cite_packages(session = NULL, include_R = TRUE, ...)

report_system(session = NULL)

Arguments

x

The R object that you want to report (see list of of supported objects above).

...

Arguments passed to or from other methods.

session

A sessionInfo object.

include_R

Include R in the citations.

Value

For report_packages, a data frame of class with information on package name, version and citation.

An object of class report().

Examples


library(report)

session <- sessionInfo()

r <- report(session)
r
#> Analyses were conducted using the R Statistical language (version 4.4.0; R Core
#> Team, 2024) on Ubuntu 22.04.4 LTS, using the packages Matrix (version 1.7.0;
#> Bates D et al., 2024), lme4 (version 1.1.35.3; Bates D et al., 2015), brms
#> (version 2.21.0; Bürkner P, 2017), Rcpp (version 1.0.12; Eddelbuettel D et al.,
#> 2024), performance (version 0.11.0.9; Lüdecke D et al., 2021), bayestestR
#> (version 0.13.2.2; Makowski D et al., 2019), modelbased (version 0.8.7.1;
#> Makowski D et al., 2020), report (version 0.5.8.3; Makowski D et al., 2023),
#> BayesFactor (version 0.9.12.4.7; Morey R, Rouder J, 2024), coda (version
#> 0.19.4.1; Plummer M et al., 2006), lavaan (version 0.6.17; Rosseel Y, 2012) and
#> dplyr (version 1.1.4; Wickham H et al., 2023).
#> 
#> References
#> ----------
#>   - Bates D, Maechler M, Jagan M (2024). _Matrix: Sparse and Dense Matrix Classes
#> and Methods_. R package version 1.7-0,
#> <https://CRAN.R-project.org/package=Matrix>.
#>   - Bates D, Mächler M, Bolker B, Walker S (2015). “Fitting Linear Mixed-Effects
#> Models Using lme4.” _Journal of Statistical Software_, *67*(1), 1-48.
#> doi:10.18637/jss.v067.i01 <https://doi.org/10.18637/jss.v067.i01>.
#>   - Bürkner P (2017). “brms: An R Package for Bayesian Multilevel Models Using
#> Stan.” _Journal of Statistical Software_, *80*(1), 1-28.
#> doi:10.18637/jss.v080.i01 <https://doi.org/10.18637/jss.v080.i01>. Bürkner P
#> (2018). “Advanced Bayesian Multilevel Modeling with the R Package brms.” _The R
#> Journal_, *10*(1), 395-411. doi:10.32614/RJ-2018-017
#> <https://doi.org/10.32614/RJ-2018-017>. Bürkner P (2021). “Bayesian Item
#> Response Modeling in R with brms and Stan.” _Journal of Statistical Software_,
#> *100*(5), 1-54. doi:10.18637/jss.v100.i05
#> <https://doi.org/10.18637/jss.v100.i05>.
#>   - Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N, Ucar I,
#> Bates D, Chambers J (2024). _Rcpp: Seamless R and C++ Integration_. R package
#> version 1.0.12, https://dirk.eddelbuettel.com/code/rcpp.html,
#> https://github.com/RcppCore/Rcpp, <https://www.rcpp.org>. Eddelbuettel D,
#> François R (2011). “Rcpp: Seamless R and C++ Integration.” _Journal of
#> Statistical Software_, *40*(8), 1-18. doi:10.18637/jss.v040.i08
#> <https://doi.org/10.18637/jss.v040.i08>. Eddelbuettel D (2013). _Seamless R and
#> C++ Integration with Rcpp_. Springer, New York. doi:10.1007/978-1-4614-6868-4
#> <https://doi.org/10.1007/978-1-4614-6868-4>, ISBN 978-1-4614-6867-7.
#> Eddelbuettel D, Balamuta J (2018). “Extending R with C++: A Brief Introduction
#> to Rcpp.” _The American Statistician_, *72*(1), 28-36.
#> doi:10.1080/00031305.2017.1375990
#> <https://doi.org/10.1080/00031305.2017.1375990>.
#>   - Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D (2021).
#> “performance: An R Package for Assessment, Comparison and Testing of
#> Statistical Models.” _Journal of Open Source Software_, *6*(60), 3139.
#> doi:10.21105/joss.03139 <https://doi.org/10.21105/joss.03139>.
#>   - Makowski D, Ben-Shachar M, Lüdecke D (2019). “bayestestR: Describing Effects
#> and their Uncertainty, Existence and Significance within the Bayesian
#> Framework.” _Journal of Open Source Software_, *4*(40), 1541.
#> doi:10.21105/joss.01541 <https://doi.org/10.21105/joss.01541>,
#> <https://joss.theoj.org/papers/10.21105/joss.01541>.
#>   - Makowski D, Ben-Shachar M, Patil I, Lüdecke D (2020). “Estimation of
#> Model-Based Predictions, Contrasts and Means.” _CRAN_.
#> <https://github.com/easystats/modelbased>.
#>   - Makowski D, Lüdecke D, Patil I, Thériault R, Ben-Shachar M, Wiernik B (2023).
#> “Automated Results Reporting as a Practical Tool to Improve Reproducibility and
#> Methodological Best Practices Adoption.” _CRAN_.
#> <https://easystats.github.io/report/>.
#>   - Morey R, Rouder J (2024). _BayesFactor: Computation of Bayes Factors for
#> Common Designs_. R package version 0.9.12-4.7,
#> <https://richarddmorey.github.io/BayesFactor/>.
#>   - Plummer M, Best N, Cowles K, Vines K (2006). “CODA: Convergence Diagnosis and
#> Output Analysis for MCMC.” _R News_, *6*(1), 7-11.
#> <https://journal.r-project.org/archive/>.
#>   - R Core Team (2024). _R: A Language and Environment for Statistical
#> Computing_. R Foundation for Statistical Computing, Vienna, Austria.
#> <https://www.R-project.org/>.
#>   - Rosseel Y (2012). “lavaan: An R Package for Structural Equation Modeling.”
#> _Journal of Statistical Software_, *48*(2), 1-36. doi:10.18637/jss.v048.i02
#> <https://doi.org/10.18637/jss.v048.i02>.
#>   - Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar
#> of Data Manipulation_. R package version 1.1.4,
#> https://github.com/tidyverse/dplyr, <https://dplyr.tidyverse.org>.
summary(r)
#> The analysis was done using the R Statistical language (v4.4.0; R Core Team,
#> 2024) on Ubuntu 22.04.4 LTS, using the packages Matrix (v1.7.0), lme4
#> (v1.1.35.3), brms (v2.21.0), Rcpp (v1.0.12), performance (v0.11.0.9),
#> bayestestR (v0.13.2.2), modelbased (v0.8.7.1), report (v0.5.8.3), BayesFactor
#> (v0.9.12.4.7), coda (v0.19.4.1), lavaan (v0.6.17) and dplyr (v1.1.4).
as.data.frame(r)
#> Package     |    Version |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          Reference
#> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#> BayesFactor | 0.9.12.4.7 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            Morey R, Rouder J (2024). _BayesFactor: Computation of Bayes Factors for Common Designs_. R package version 0.9.12-4.7, <https://richarddmorey.github.io/BayesFactor/>.
#> Matrix      |      1.7.0 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Bates D, Maechler M, Jagan M (2024). _Matrix: Sparse and Dense Matrix Classes and Methods_. R package version 1.7-0, <https://CRAN.R-project.org/package=Matrix>.
#> R           |      4.4.0 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              R Core Team (2024). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.
#> Rcpp        |     1.0.12 | Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N, Ucar I, Bates D, Chambers J (2024). _Rcpp: Seamless R and C++ Integration_. R package version 1.0.12, https://dirk.eddelbuettel.com/code/rcpp.html, https://github.com/RcppCore/Rcpp, <https://www.rcpp.org>. Eddelbuettel D, François R (2011). “Rcpp: Seamless R and C++ Integration.” _Journal of Statistical Software_, *40*(8), 1-18. doi:10.18637/jss.v040.i08 <https://doi.org/10.18637/jss.v040.i08>. Eddelbuettel D (2013). _Seamless R and C++ Integration with Rcpp_. Springer, New York. doi:10.1007/978-1-4614-6868-4 <https://doi.org/10.1007/978-1-4614-6868-4>, ISBN 978-1-4614-6867-7. Eddelbuettel D, Balamuta J (2018). “Extending R with C++: A Brief Introduction to Rcpp.” _The American Statistician_, *72*(1), 28-36. doi:10.1080/00031305.2017.1375990 <https://doi.org/10.1080/00031305.2017.1375990>.
#> bayestestR  |   0.13.2.2 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Makowski D, Ben-Shachar M, Lüdecke D (2019). “bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework.” _Journal of Open Source Software_, *4*(40), 1541. doi:10.21105/joss.01541 <https://doi.org/10.21105/joss.01541>, <https://joss.theoj.org/papers/10.21105/joss.01541>.
#> brms        |     2.21.0 |                                                                                                                                                                                                                                                                                                   Bürkner P (2017). “brms: An R Package for Bayesian Multilevel Models Using Stan.” _Journal of Statistical Software_, *80*(1), 1-28. doi:10.18637/jss.v080.i01 <https://doi.org/10.18637/jss.v080.i01>. Bürkner P (2018). “Advanced Bayesian Multilevel Modeling with the R Package brms.” _The R Journal_, *10*(1), 395-411. doi:10.32614/RJ-2018-017 <https://doi.org/10.32614/RJ-2018-017>. Bürkner P (2021). “Bayesian Item Response Modeling in R with brms and Stan.” _Journal of Statistical Software_, *100*(5), 1-54. doi:10.18637/jss.v100.i05 <https://doi.org/10.18637/jss.v100.i05>.
#> coda        |   0.19.4.1 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         Plummer M, Best N, Cowles K, Vines K (2006). “CODA: Convergence Diagnosis and Output Analysis for MCMC.” _R News_, *6*(1), 7-11. <https://journal.r-project.org/archive/>.
#> dplyr       |      1.1.4 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. R package version 1.1.4, https://github.com/tidyverse/dplyr, <https://dplyr.tidyverse.org>.
#> lavaan      |     0.6.17 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Rosseel Y (2012). “lavaan: An R Package for Structural Equation Modeling.” _Journal of Statistical Software_, *48*(2), 1-36. doi:10.18637/jss.v048.i02 <https://doi.org/10.18637/jss.v048.i02>.
#> lme4        |   1.1.35.3 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Bates D, Mächler M, Bolker B, Walker S (2015). “Fitting Linear Mixed-Effects Models Using lme4.” _Journal of Statistical Software_, *67*(1), 1-48. doi:10.18637/jss.v067.i01 <https://doi.org/10.18637/jss.v067.i01>.
#> modelbased  |    0.8.7.1 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Makowski D, Ben-Shachar M, Patil I, Lüdecke D (2020). “Estimation of Model-Based Predictions, Contrasts and Means.” _CRAN_. <https://github.com/easystats/modelbased>.
#> performance |   0.11.0.9 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D (2021). “performance: An R Package for Assessment, Comparison and Testing of Statistical Models.” _Journal of Open Source Software_, *6*(60), 3139. doi:10.21105/joss.03139 <https://doi.org/10.21105/joss.03139>.
#> report      |    0.5.8.3 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              Makowski D, Lüdecke D, Patil I, Thériault R, Ben-Shachar M, Wiernik B (2023). “Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption.” _CRAN_. <https://easystats.github.io/report/>.
summary(as.data.frame(r))
#> Package     |    Version
#> ------------------------
#> BayesFactor | 0.9.12.4.7
#> Matrix      |      1.7.0
#> R           |      4.4.0
#> Rcpp        |     1.0.12
#> bayestestR  |   0.13.2.2
#> brms        |     2.21.0
#> coda        |   0.19.4.1
#> dplyr       |      1.1.4
#> lavaan      |     0.6.17
#> lme4        |   1.1.35.3
#> modelbased  |    0.8.7.1
#> performance |   0.11.0.9
#> report      |    0.5.8.3

# Convenience functions
report_packages(include_R = FALSE)
#>   - Matrix (version 1.7.0; Bates D et al., 2024)
#>   - lme4 (version 1.1.35.3; Bates D et al., 2015)
#>   - brms (version 2.21.0; Bürkner P, 2017)
#>   - Rcpp (version 1.0.12; Eddelbuettel D et al., 2024)
#>   - performance (version 0.11.0.9; Lüdecke D et al., 2021)
#>   - bayestestR (version 0.13.2.2; Makowski D et al., 2019)
#>   - modelbased (version 0.8.7.1; Makowski D et al., 2020)
#>   - report (version 0.5.8.3; Makowski D et al., 2023)
#>   - BayesFactor (version 0.9.12.4.7; Morey R, Rouder J, 2024)
#>   - coda (version 0.19.4.1; Plummer M et al., 2006)
#>   - lavaan (version 0.6.17; Rosseel Y, 2012)
#>   - dplyr (version 1.1.4; Wickham H et al., 2023)
cite_packages(prefix = "> ")
#> > Bates D, Maechler M, Jagan M (2024). _Matrix: Sparse and Dense Matrix Classes and Methods_. R package version 1.7-0, <https://CRAN.R-project.org/package=Matrix>.
#> > Bates D, Mächler M, Bolker B, Walker S (2015). “Fitting Linear Mixed-Effects Models Using lme4.” _Journal of Statistical Software_, *67*(1), 1-48. doi:10.18637/jss.v067.i01 <https://doi.org/10.18637/jss.v067.i01>.
#> > Bürkner P (2017). “brms: An R Package for Bayesian Multilevel Models Using Stan.” _Journal of Statistical Software_, *80*(1), 1-28. doi:10.18637/jss.v080.i01 <https://doi.org/10.18637/jss.v080.i01>. Bürkner P (2018). “Advanced Bayesian Multilevel Modeling with the R Package brms.” _The R Journal_, *10*(1), 395-411. doi:10.32614/RJ-2018-017 <https://doi.org/10.32614/RJ-2018-017>. Bürkner P (2021). “Bayesian Item Response Modeling in R with brms and Stan.” _Journal of Statistical Software_, *100*(5), 1-54. doi:10.18637/jss.v100.i05 <https://doi.org/10.18637/jss.v100.i05>.
#> > Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N, Ucar I, Bates D, Chambers J (2024). _Rcpp: Seamless R and C++ Integration_. R package version 1.0.12, https://dirk.eddelbuettel.com/code/rcpp.html, https://github.com/RcppCore/Rcpp, <https://www.rcpp.org>. Eddelbuettel D, François R (2011). “Rcpp: Seamless R and C++ Integration.” _Journal of Statistical Software_, *40*(8), 1-18. doi:10.18637/jss.v040.i08 <https://doi.org/10.18637/jss.v040.i08>. Eddelbuettel D (2013). _Seamless R and C++ Integration with Rcpp_. Springer, New York. doi:10.1007/978-1-4614-6868-4 <https://doi.org/10.1007/978-1-4614-6868-4>, ISBN 978-1-4614-6867-7. Eddelbuettel D, Balamuta J (2018). “Extending R with C++: A Brief Introduction to Rcpp.” _The American Statistician_, *72*(1), 28-36. doi:10.1080/00031305.2017.1375990 <https://doi.org/10.1080/00031305.2017.1375990>.
#> > Lüdecke D, Ben-Shachar M, Patil I, Waggoner P, Makowski D (2021). “performance: An R Package for Assessment, Comparison and Testing of Statistical Models.” _Journal of Open Source Software_, *6*(60), 3139. doi:10.21105/joss.03139 <https://doi.org/10.21105/joss.03139>.
#> > Makowski D, Ben-Shachar M, Lüdecke D (2019). “bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework.” _Journal of Open Source Software_, *4*(40), 1541. doi:10.21105/joss.01541 <https://doi.org/10.21105/joss.01541>, <https://joss.theoj.org/papers/10.21105/joss.01541>.
#> > Makowski D, Ben-Shachar M, Patil I, Lüdecke D (2020). “Estimation of Model-Based Predictions, Contrasts and Means.” _CRAN_. <https://github.com/easystats/modelbased>.
#> > Makowski D, Lüdecke D, Patil I, Thériault R, Ben-Shachar M, Wiernik B (2023). “Automated Results Reporting as a Practical Tool to Improve Reproducibility and Methodological Best Practices Adoption.” _CRAN_. <https://easystats.github.io/report/>.
#> > Morey R, Rouder J (2024). _BayesFactor: Computation of Bayes Factors for Common Designs_. R package version 0.9.12-4.7, <https://richarddmorey.github.io/BayesFactor/>.
#> > Plummer M, Best N, Cowles K, Vines K (2006). “CODA: Convergence Diagnosis and Output Analysis for MCMC.” _R News_, *6*(1), 7-11. <https://journal.r-project.org/archive/>.
#> > R Core Team (2024). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.
#> > Rosseel Y (2012). “lavaan: An R Package for Structural Equation Modeling.” _Journal of Statistical Software_, *48*(2), 1-36. doi:10.18637/jss.v048.i02 <https://doi.org/10.18637/jss.v048.i02>.
#> > Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. R package version 1.1.4, https://github.com/tidyverse/dplyr, <https://dplyr.tidyverse.org>.
report_system()
#> Analyses were conducted using the R Statistical language (version 4.4.0; R Core
#> Team, 2024) on Ubuntu 22.04.4 LTS