Report R environment (packages, system, etc.)
Usage
# S3 method for 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 4.3.2; R Core
#> Team, 2023) on Ubuntu 22.04.3 LTS, using the packages Matrix (version 1.6.2;
#> Bates D et al., 2023), lme4 (version 1.1.35.1; Bates D et al., 2015), brms
#> (version 2.20.4; Bürkner P, 2017), Rcpp (version 1.0.11; Eddelbuettel D et al.,
#> 2023), performance (version 0.10.8.1; Lüdecke D et al., 2021), bayestestR
#> (version 0.13.1.7; Makowski D et al., 2019), modelbased (version 0.8.6;
#> Makowski D et al., 2020), report (version 0.5.7.13; Makowski D et al., 2023),
#> lavaan (version 0.6.16; Rosseel Y, 2012) and dplyr (version 1.1.3; Wickham H et
#> al., 2023).
#>
#> References
#> ----------
#> - Bates D, Maechler M, Jagan M (2023). _Matrix: Sparse and Dense Matrix Classes
#> and Methods_. R package version 1.6-2, <https://Matrix.R-forge.R-project.org>.
#> - 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 (2023). _Rcpp: Seamless R and C++ Integration_. R package
#> version 1.0.11, 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/>.
#> - R Core Team (2023). _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 1.1.3,
#> https://github.com/tidyverse/dplyr, <https://dplyr.tidyverse.org>.
summary(r)
#> The analysis was done using the R Statistical language (v4.3.2; R Core Team,
#> 2023) on Ubuntu 22.04.3 LTS, using the packages Matrix (v1.6.2), lme4
#> (v1.1.35.1), brms (v2.20.4), Rcpp (v1.0.11), performance (v0.10.8.1),
#> bayestestR (v0.13.1.7), modelbased (v0.8.6), report (v0.5.7.13), lavaan
#> (v0.6.16) and dplyr (v1.1.3).
as.data.frame(r)
#> Package | Version | Reference
#> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#> Matrix | 1.6.2 | Bates D, Maechler M, Jagan M (2023). _Matrix: Sparse and Dense Matrix Classes and Methods_. R package version 1.6-2, <https://Matrix.R-forge.R-project.org>.
#> R | 4.3.2 | R Core Team (2023). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.
#> Rcpp | 1.0.11 | Eddelbuettel D, Francois R, Allaire J, Ushey K, Kou Q, Russell N, Ucar I, Bates D, Chambers J (2023). _Rcpp: Seamless R and C++ Integration_. R package version 1.0.11, 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 | 0.13.1.7 | 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 | 2.20.4 | 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>.
#> dplyr | 1.1.3 | Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. R package version 1.1.3, https://github.com/tidyverse/dplyr, <https://dplyr.tidyverse.org>.
#> lavaan | 0.6.16 | 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 | 1.1.35.1 | 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 | 0.8.6 | 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 | 0.10.8.1 | 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 | 0.5.7.13 | 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
#> ----------------------
#> Matrix | 1.6.2
#> R | 4.3.2
#> Rcpp | 1.0.11
#> bayestestR | 0.13.1.7
#> brms | 2.20.4
#> dplyr | 1.1.3
#> lavaan | 0.6.16
#> lme4 | 1.1.35.1
#> modelbased | 0.8.6
#> performance | 0.10.8.1
#> report | 0.5.7.13
# Convenience functions
report_packages(include_R = FALSE)
#> - Matrix (version 1.6.2; Bates D et al., 2023)
#> - lme4 (version 1.1.35.1; Bates D et al., 2015)
#> - brms (version 2.20.4; Bürkner P, 2017)
#> - Rcpp (version 1.0.11; Eddelbuettel D et al., 2023)
#> - performance (version 0.10.8.1; Lüdecke D et al., 2021)
#> - bayestestR (version 0.13.1.7; Makowski D et al., 2019)
#> - modelbased (version 0.8.6; Makowski D et al., 2020)
#> - report (version 0.5.7.13; Makowski D et al., 2023)
#> - lavaan (version 0.6.16; Rosseel Y, 2012)
#> - dplyr (version 1.1.3; Wickham H et al., 2023)
cite_packages(prefix = "> ")
#> > Bates D, Maechler M, Jagan M (2023). _Matrix: Sparse and Dense Matrix Classes and Methods_. R package version 1.6-2, <https://Matrix.R-forge.R-project.org>.
#> > 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 (2023). _Rcpp: Seamless R and C++ Integration_. R package version 1.0.11, 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/>.
#> > R Core Team (2023). _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 1.1.3, https://github.com/tidyverse/dplyr, <https://dplyr.tidyverse.org>.
report_system()
#> Analyses were conducted using the R Statistical language (version 4.3.2; R Core
#> Team, 2023) on Ubuntu 22.04.3 LTS