CRAN Package Check Results for Package metafor

Last updated on 2020-02-27 15:48:54 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 2.1-0 48.69 286.15 334.84 ERROR
r-devel-linux-x86_64-debian-gcc 2.1-0 42.46 219.30 261.76 ERROR
r-devel-linux-x86_64-fedora-clang 2.1-0 403.42 OK
r-devel-linux-x86_64-fedora-gcc 2.1-0 390.80 OK
r-devel-windows-ix86+x86_64 2.1-0 60.00 266.00 326.00 OK
r-devel-windows-ix86+x86_64-gcc8 2.1-0 65.00 271.00 336.00 OK
r-patched-linux-x86_64 2.1-0 37.86 258.52 296.38 OK
r-patched-solaris-x86 2.1-0 455.30 OK
r-release-linux-x86_64 2.1-0 43.71 256.67 300.38 OK
r-release-windows-ix86+x86_64 2.1-0 62.00 233.00 295.00 OK
r-release-osx-x86_64 2.1-0 OK
r-oldrel-windows-ix86+x86_64 2.1-0 46.00 236.00 282.00 OK
r-oldrel-osx-x86_64 2.1-0 OK

Check Details

Version: 2.1-0
Check: tests
Result: ERROR
     Running 'testthat.R' [40s/46s]
    Running the tests in 'tests/testthat.R' failed.
    Complete output:
     > ### to also run skip_on_cran() tests, uncomment:
     > #Sys.setenv(NOT_CRAN="true")
     >
     > library(testthat)
     > library(metafor)
     Loading required package: Matrix
     Loading 'metafor' package (version 2.1-0). For an overview
     and introduction to the package please type: help(metafor).
     > test_check("metafor", reporter="summary")
     Checking analysis example: berkey1995: ...........
     Checking analysis example: berkey1998: ..............
     Checking analysis example: dersimonian2007: S
     Checking analysis example: gleser2009: .......................
     Checking analysis example: henmi2010: .......
     Checking analysis example: ishak2007: .......................
     Checking analysis example: jackson2014: SS
     Checking analysis example: konstantopoulos2011: .............................S......SSS
     Checking analysis example: law2016: SS
     Checking analysis example: lipsey2001: .........................
     Checking analysis example: miller1978: ...........S
     Checking analysis example: morris2008: ..............
     Checking analysis example: normand1999: ..............................
     Checking analysis example: raudenbush1985: ..........S.............S
     Checking analysis example: raudenbush2009: ..................
     Checking analysis example: rothman2008: .1...........................S.2...................S.3............S
     Checking analysis example: stijnen2010: ............S.......SS............S......S
     Checking analysis example: vanhouwelingen1993: SSS
     Checking analysis example: vanhouwelingen2002: ............S.S....S.....................
     Checking analysis example: viechtbauer2005: ........
     Checking analysis example: viechtbauer2007a: .....S...SS
     Checking analysis example: viechtbauer2007b: ............S
     Checking analysis example: yusuf1985: S.....
     Checking misc: anova() function: ...........
     Checking misc: confint() function: ......
     Checking misc: model diagnostic functions for rma.mv(): SS
     Checking misc: escalc() function: ...............................................................................................
     Checking misc: computations of fit statistics: .......................
     Checking misc: fsn() function: .........
     Checking misc: funnel() functions: .S
     Checking misc: handling of NAs: ...............................................................................
     Checking misc: handling of edge cases due to zeros: .......S.......S
     Checking misc: influence() and related functions: .......................
     Checking misc: head.list.rma() and tail.list.rma() functions: ....
     Checking misc: rma.mh() against metan with 'dat.bcg': .....................
     Checking misc: rma.peto() against metan with 'dat.bcg': ........
     Checking misc: rma.uni() against metan with 'dat.bcg': .............................................
     Checking misc: pdfs of various measures: .....
     Checking misc: permutest() function: SSS
     Checking misc: plot() function: ...
     Checking misc: predict() function: ................
     Checking misc: regtest() and ranktest() functions: ........
     Checking misc: residuals() function: .....................S
     Checking misc: proper handling of errors in rma(): ......
     Checking misc: rma.glmm() function: ............SS
     Checking misc: proper handling of missing values: S
     Checking misc: rma.mv() function: ..........................
     Checking misc: rma() function: ..............
     Checking misc: rma() function with location-scale models: ...............
     Checking misc: rma.uni() against direct computations: .....
     Checking tip: rma() results match up with those from lm(): ........
     Checking misc: robust() function: ......
     Checking misc: .setlab() function: .
     Checking misc: to.long() function: ..............
     Checking misc: transformation functions: .......................
     Checking misc: update() function: ....S
     Checking misc: vcov() function: ........
     Checking misc: weights() function: ..........................
     Checking plots example: Baujat plot: .S
     Checking plots example: Caterpillar plot: .S
     Checking plots example: contour-enhanced funnel plot: .S
     Checking plots example: cumulative forest plot: .S.S.S
     Checking plots example: forest plot with subgroups: .S
     Checking plots example: funnel plot variations: .S
     Checking plots example: funnel plot with trim and fill: .S
     Checking plots example: GOSH plot: .S
     Checking plots example: L'Abbe plot: .S
     Checking plots example: Likelihood plot: .S
     Checking plots example: meta-analytic scatterplot: .S
     Checking plots example: normal QQ plots: .S.S.S.
     Checking plots example: plot of cumulative results: .S
     Checking plots example: plot of influence diagnostics: .S
     Checking plots example: radial (Galbraith) plot: .S
     Checking tip: rma() results match up with those from lm(): ............
     Checking tip: rma() results match up with those from lm() and lme(): ..........
    
     == Skipped =====================================================================
     1. results are correct for the CLASP example. (@test_analysis_example_dersimonian2007.r#17) - Reason: On CRAN
    
     2. confint() gives correct results for example 1 in Jackson et al. (2014). (@test_analysis_example_jackson2014.r#9) - Reason: On CRAN
    
     3. confint() gives correct results for example 2 in Jackson et al. (2014). (@test_analysis_example_jackson2014.r#49) - Reason: On CRAN
    
     4. profiling works for the three-level random-effects model (multilevel parameterization). (@test_analysis_example_konstantopoulos2011.r#113) - Reason: On CRAN
    
     5. profiling works for the three-level random-effects model (multivariate parameterization). (@test_analysis_example_konstantopoulos2011.r#146) - Reason: On CRAN
    
     6. BLUPs are calculated correctly for the three-level random-effects model (multilevel parameterization). (@test_analysis_example_konstantopoulos2011.r#162) - Reason: On CRAN
    
     7. results are correct when allowing for different tau^2 per district. (@test_analysis_example_konstantopoulos2011.r#178) - Reason: On CRAN
    
     8. results are correct for example 1. (@test_analysis_example_law2016.r#22) - Reason: On CRAN
    
     9. results are correct for example 2. (@test_analysis_example_law2016.r#95) - Reason: On CRAN
    
     10. back-transformations work as intended for individual studies and the model estimate. (@test_analysis_example_miller1978.r#80) - Reason: On CRAN
    
     11. results are correct for the random-effects model. (@test_analysis_example_raudenbush1985.r#40) - Reason: On CRAN
    
     12. results are correct for the mixed-effects model. (@test_analysis_example_raudenbush1985.r#96) - Reason: On CRAN
    
     13. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#134) - Reason: On CRAN
    
     14. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#269) - Reason: On CRAN
    
     15. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#362) - Reason: On CRAN
    
     16. results for the binomial-normal normal are correct (measure=='PLO') (@test_analysis_example_stijnen2010.r#40) - Reason: On CRAN
    
     17. results for the conditional logistic model with exact likelihood are correct (measure=='OR') (@test_analysis_example_stijnen2010.r#83) - Reason: On CRAN
    
     18. results for the conditional logistic model with approximate likelihood are correct (measure=='OR') (@test_analysis_example_stijnen2010.r#101) - Reason: On CRAN
    
     19. results for the Poisson-normal model are correct (measure=='IRLN') (@test_analysis_example_stijnen2010.r#153) - Reason: On CRAN
    
     20. results for the Poisson-normal model are correct (measure=='IRR') (@test_analysis_example_stijnen2010.r#196) - Reason: On CRAN
    
     21. the log likelihood plot can be created. (@test_analysis_example_vanhouwelingen1993.r#14) - Reason: On CRAN
    
     22. results of the fixed-effects conditional logistic model are correct. (@test_analysis_example_vanhouwelingen1993.r#25) - Reason: On CRAN
    
     23. results of the random-effects conditional logistic model are correct. (@test_analysis_example_vanhouwelingen1993.r#50) - Reason: On CRAN
    
     24. profile plot for tau^2 can be drawn. (@test_analysis_example_vanhouwelingen2002.r#60) - Reason: On CRAN
    
     25. forest plot of observed log(OR)s and corresponding BLUPs can be drawn. (@test_analysis_example_vanhouwelingen2002.r#76) - Reason: On CRAN
    
     26. L'Abbe plot can be drawn. (@test_analysis_example_vanhouwelingen2002.r#117) - Reason: On CRAN
    
     27. CI is correct for the profile likelihood method. (@test_analysis_example_viechtbauer2007a.r#75) - Reason: On CRAN
    
     28. CI is correct for the parametric bootstrap method. (@test_analysis_example_viechtbauer2007a.r#112) - Reason: On CRAN
    
     29. CI is correct for the non-parametric bootstrap method. (@test_analysis_example_viechtbauer2007a.r#150) - Reason: On CRAN
    
     30. results are correct for the mixed-effects model. (@test_analysis_example_viechtbauer2007b.r#78) - Reason: On CRAN
    
     31. log likelihood plot can be drawn. (@test_analysis_example_yusuf1985.r#15) - Reason: On CRAN
    
     32. model diagnostic functions work with 'na.omit'. (@test_misc_diagnostics_rma.mv.r#29) - Reason: On CRAN
    
     33. model diagnostic functions work with 'na.pass'. (@test_misc_diagnostics_rma.mv.r#160) - Reason: On CRAN
    
     34. funnel() works correctly. (@test_misc_funnel.r#11) - Reason: On CRAN
    
     35. rma.peto(), rma.mh(), and rma.glmm() handle outcome1 never occurring properly. (@test_misc_handling_of_edge_cases_due_to_zeros.r#23) - Reason: On CRAN
    
     36. rma.peto(), rma.mh(), and rma.glmm() handle outcome2 never occurring properly. (@test_misc_handling_of_edge_cases_due_to_zeros.r#45) - Reason: On CRAN
    
     37. permutest() gives correct results for a random-effects model. (@test_misc_permutest.r#15) - Reason: On CRAN
    
     38. permutest() gives correct results for a mixed-effects model. (@test_misc_permutest.r#58) - Reason: On CRAN
    
     39. permutest() gives correct results for example in Follmann & Proschan (1999). (@test_misc_permutest.r#95) - Reason: On CRAN
    
     40. residuals are correct for rma.glmm(). (@test_misc_residuals.r#86) - Reason: On CRAN
    
     41. rma.glmm() works correctly when using 'clogit' or 'clogistic'. (@test_misc_rma_glmm.r#36) - Reason: On CRAN
    
     42. rma.glmm() works correctly when using 'nlminb' or 'minqa'. (@test_misc_rma_glmm.r#54) - Reason: On CRAN
    
     43. rma.glmm() handles NAs correctly. (@test_misc_rma_handling_nas.r#9) - Reason: On CRAN
    
     44. update() works for rma.glmm(). (@test_misc_update.r#39) - Reason: On CRAN
    
     45. plot can be drawn. (@test_plots_baujat_plot.r#11) - Reason: On CRAN
    
     46. plot can be drawn. (@test_plots_caterpillar_plot.r#11) - Reason: On CRAN
    
     47. plot can be drawn. (@test_plots_contour-enhanced_funnel_plot.r#11) - Reason: On CRAN
    
     48. plot can be drawn for 'rma.uni' object. (@test_plots_cumulative_forest_plot.r#11) - Reason: On CRAN
    
     49. plot can be drawn for 'rma.mh' object. (@test_plots_cumulative_forest_plot.r#49) - Reason: On CRAN
    
     50. plot can be drawn for 'rma.peto' object. (@test_plots_cumulative_forest_plot.r#84) - Reason: On CRAN
    
     51. plot can be drawn. (@test_plots_forest_plot_with_subgroups.r#11) - Reason: On CRAN
    
     52. plot can be drawn. (@test_plots_funnel_plot_variations.r#11) - Reason: On CRAN
    
     53. plot can be drawn. (@test_plots_funnel_plot_with_trim_and_fill.r#11) - Reason: On CRAN
    
     54. plot can be drawn. (@test_plots_gosh.r#11) - Reason: On CRAN
    
     55. plot can be drawn. (@test_plots_labbe_plot.r#11) - Reason: On CRAN
    
     56. plot can be drawn. (@test_plots_llplot.r#11) - Reason: On CRAN
    
     57. plot can be drawn. (@test_plots_meta-analytic_scatterplot.r#11) - Reason: On CRAN
    
     58. plot can be drawn for 'rma.uni' object. (@test_plots_normal_qq_plots.r#11) - Reason: On CRAN
    
     59. plot can be drawn for 'rma.mh' object. (@test_plots_normal_qq_plots.r#49) - Reason: On CRAN
    
     60. plot can be drawn for 'rma.peto' object. (@test_plots_normal_qq_plots.r#64) - Reason: On CRAN
    
     61. plot can be drawn. (@test_plots_plot_of_cumulative_results.r#11) - Reason: On CRAN
    
     62. plot can be drawn. (@test_plots_plot_of_influence_diagnostics.r#11) - Reason: On CRAN
    
     63. plot can be drawn. (@test_plots_radial_plot.r#11) - Reason: On CRAN
    
     == Failed ======================================================================
     -- 1. Failure: the to.long() function works. (@test_analysis_example_rothman2008
     `tmp` not equivalent to `expected`.
     Component "age": target is character, current is factor
     Component "out1": target is character, current is factor
     Component "out2": target is character, current is factor
    
     -- 2. Failure: the to.long() function works. (@test_analysis_example_rothman2008
     `tmp` not equivalent to `expected`.
     Component "age": target is character, current is factor
     Component "events": target is character, current is factor
     Component "ptime": target is character, current is factor
    
     -- 3. Failure: the to.long() function works. (@test_analysis_example_rothman2008
     `tmp` not equivalent to `expected`.
     Component "age": target is character, current is factor
     Component "out1": target is character, current is factor
     Component "out2": target is character, current is factor
    
     == DONE ========================================================================
     No one gets it right on their first try
     Error: Test failures
     Execution halted
Flavor: r-devel-linux-x86_64-debian-clang

Version: 2.1-0
Check: tests
Result: ERROR
     Running ‘testthat.R’ [29s/42s]
    Running the tests in ‘tests/testthat.R’ failed.
    Complete output:
     > ### to also run skip_on_cran() tests, uncomment:
     > #Sys.setenv(NOT_CRAN="true")
     >
     > library(testthat)
     > library(metafor)
     Loading required package: Matrix
     Loading 'metafor' package (version 2.1-0). For an overview
     and introduction to the package please type: help(metafor).
     > test_check("metafor", reporter="summary")
     Checking analysis example: berkey1995: ...........
     Checking analysis example: berkey1998: ..............
     Checking analysis example: dersimonian2007: S
     Checking analysis example: gleser2009: .......................
     Checking analysis example: henmi2010: .......
     Checking analysis example: ishak2007: .......................
     Checking analysis example: jackson2014: SS
     Checking analysis example: konstantopoulos2011: .............................S......SSS
     Checking analysis example: law2016: SS
     Checking analysis example: lipsey2001: .........................
     Checking analysis example: miller1978: ...........S
     Checking analysis example: morris2008: ..............
     Checking analysis example: normand1999: ..............................
     Checking analysis example: raudenbush1985: ..........S.............S
     Checking analysis example: raudenbush2009: ..................
     Checking analysis example: rothman2008: .1...........................S.2...................S.3............S
     Checking analysis example: stijnen2010: ............S.......SS............S......S
     Checking analysis example: vanhouwelingen1993: SSS
     Checking analysis example: vanhouwelingen2002: ............S.S....S.....................
     Checking analysis example: viechtbauer2005: ........
     Checking analysis example: viechtbauer2007a: .....S...SS
     Checking analysis example: viechtbauer2007b: ............S
     Checking analysis example: yusuf1985: S.....
     Checking misc: anova() function: ...........
     Checking misc: confint() function: ......
     Checking misc: model diagnostic functions for rma.mv(): SS
     Checking misc: escalc() function: ...............................................................................................
     Checking misc: computations of fit statistics: .......................
     Checking misc: fsn() function: .........
     Checking misc: funnel() functions: .S
     Checking misc: handling of NAs: ...............................................................................
     Checking misc: handling of edge cases due to zeros: .......S.......S
     Checking misc: influence() and related functions: .......................
     Checking misc: head.list.rma() and tail.list.rma() functions: ....
     Checking misc: rma.mh() against metan with 'dat.bcg': .....................
     Checking misc: rma.peto() against metan with 'dat.bcg': ........
     Checking misc: rma.uni() against metan with 'dat.bcg': .............................................
     Checking misc: pdfs of various measures: .....
     Checking misc: permutest() function: SSS
     Checking misc: plot() function: ...
     Checking misc: predict() function: ................
     Checking misc: regtest() and ranktest() functions: ........
     Checking misc: residuals() function: .....................S
     Checking misc: proper handling of errors in rma(): ......
     Checking misc: rma.glmm() function: ............SS
     Checking misc: proper handling of missing values: S
     Checking misc: rma.mv() function: ..........................
     Checking misc: rma() function: ..............
     Checking misc: rma() function with location-scale models: ...............
     Checking misc: rma.uni() against direct computations: .....
     Checking tip: rma() results match up with those from lm(): ........
     Checking misc: robust() function: ......
     Checking misc: .setlab() function: .
     Checking misc: to.long() function: ..............
     Checking misc: transformation functions: .......................
     Checking misc: update() function: ....S
     Checking misc: vcov() function: ........
     Checking misc: weights() function: ..........................
     Checking plots example: Baujat plot: .S
     Checking plots example: Caterpillar plot: .S
     Checking plots example: contour-enhanced funnel plot: .S
     Checking plots example: cumulative forest plot: .S.S.S
     Checking plots example: forest plot with subgroups: .S
     Checking plots example: funnel plot variations: .S
     Checking plots example: funnel plot with trim and fill: .S
     Checking plots example: GOSH plot: .S
     Checking plots example: L'Abbe plot: .S
     Checking plots example: Likelihood plot: .S
     Checking plots example: meta-analytic scatterplot: .S
     Checking plots example: normal QQ plots: .S.S.S.
     Checking plots example: plot of cumulative results: .S
     Checking plots example: plot of influence diagnostics: .S
     Checking plots example: radial (Galbraith) plot: .S
     Checking tip: rma() results match up with those from lm(): ............
     Checking tip: rma() results match up with those from lm() and lme(): ..........
    
     ══ Skipped ═════════════════════════════════════════════════════════════════════
     1. results are correct for the CLASP example. (@test_analysis_example_dersimonian2007.r#17) - Reason: On CRAN
    
     2. confint() gives correct results for example 1 in Jackson et al. (2014). (@test_analysis_example_jackson2014.r#9) - Reason: On CRAN
    
     3. confint() gives correct results for example 2 in Jackson et al. (2014). (@test_analysis_example_jackson2014.r#49) - Reason: On CRAN
    
     4. profiling works for the three-level random-effects model (multilevel parameterization). (@test_analysis_example_konstantopoulos2011.r#113) - Reason: On CRAN
    
     5. profiling works for the three-level random-effects model (multivariate parameterization). (@test_analysis_example_konstantopoulos2011.r#146) - Reason: On CRAN
    
     6. BLUPs are calculated correctly for the three-level random-effects model (multilevel parameterization). (@test_analysis_example_konstantopoulos2011.r#162) - Reason: On CRAN
    
     7. results are correct when allowing for different tau^2 per district. (@test_analysis_example_konstantopoulos2011.r#178) - Reason: On CRAN
    
     8. results are correct for example 1. (@test_analysis_example_law2016.r#22) - Reason: On CRAN
    
     9. results are correct for example 2. (@test_analysis_example_law2016.r#95) - Reason: On CRAN
    
     10. back-transformations work as intended for individual studies and the model estimate. (@test_analysis_example_miller1978.r#80) - Reason: On CRAN
    
     11. results are correct for the random-effects model. (@test_analysis_example_raudenbush1985.r#40) - Reason: On CRAN
    
     12. results are correct for the mixed-effects model. (@test_analysis_example_raudenbush1985.r#96) - Reason: On CRAN
    
     13. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#134) - Reason: On CRAN
    
     14. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#269) - Reason: On CRAN
    
     15. results are correct for Mantel-Haenszel method. (@test_analysis_example_rothman2008.r#362) - Reason: On CRAN
    
     16. results for the binomial-normal normal are correct (measure=='PLO') (@test_analysis_example_stijnen2010.r#40) - Reason: On CRAN
    
     17. results for the conditional logistic model with exact likelihood are correct (measure=='OR') (@test_analysis_example_stijnen2010.r#83) - Reason: On CRAN
    
     18. results for the conditional logistic model with approximate likelihood are correct (measure=='OR') (@test_analysis_example_stijnen2010.r#101) - Reason: On CRAN
    
     19. results for the Poisson-normal model are correct (measure=='IRLN') (@test_analysis_example_stijnen2010.r#153) - Reason: On CRAN
    
     20. results for the Poisson-normal model are correct (measure=='IRR') (@test_analysis_example_stijnen2010.r#196) - Reason: On CRAN
    
     21. the log likelihood plot can be created. (@test_analysis_example_vanhouwelingen1993.r#14) - Reason: On CRAN
    
     22. results of the fixed-effects conditional logistic model are correct. (@test_analysis_example_vanhouwelingen1993.r#25) - Reason: On CRAN
    
     23. results of the random-effects conditional logistic model are correct. (@test_analysis_example_vanhouwelingen1993.r#50) - Reason: On CRAN
    
     24. profile plot for tau^2 can be drawn. (@test_analysis_example_vanhouwelingen2002.r#60) - Reason: On CRAN
    
     25. forest plot of observed log(OR)s and corresponding BLUPs can be drawn. (@test_analysis_example_vanhouwelingen2002.r#76) - Reason: On CRAN
    
     26. L'Abbe plot can be drawn. (@test_analysis_example_vanhouwelingen2002.r#117) - Reason: On CRAN
    
     27. CI is correct for the profile likelihood method. (@test_analysis_example_viechtbauer2007a.r#75) - Reason: On CRAN
    
     28. CI is correct for the parametric bootstrap method. (@test_analysis_example_viechtbauer2007a.r#112) - Reason: On CRAN
    
     29. CI is correct for the non-parametric bootstrap method. (@test_analysis_example_viechtbauer2007a.r#150) - Reason: On CRAN
    
     30. results are correct for the mixed-effects model. (@test_analysis_example_viechtbauer2007b.r#78) - Reason: On CRAN
    
     31. log likelihood plot can be drawn. (@test_analysis_example_yusuf1985.r#15) - Reason: On CRAN
    
     32. model diagnostic functions work with 'na.omit'. (@test_misc_diagnostics_rma.mv.r#29) - Reason: On CRAN
    
     33. model diagnostic functions work with 'na.pass'. (@test_misc_diagnostics_rma.mv.r#160) - Reason: On CRAN
    
     34. funnel() works correctly. (@test_misc_funnel.r#11) - Reason: On CRAN
    
     35. rma.peto(), rma.mh(), and rma.glmm() handle outcome1 never occurring properly. (@test_misc_handling_of_edge_cases_due_to_zeros.r#23) - Reason: On CRAN
    
     36. rma.peto(), rma.mh(), and rma.glmm() handle outcome2 never occurring properly. (@test_misc_handling_of_edge_cases_due_to_zeros.r#45) - Reason: On CRAN
    
     37. permutest() gives correct results for a random-effects model. (@test_misc_permutest.r#15) - Reason: On CRAN
    
     38. permutest() gives correct results for a mixed-effects model. (@test_misc_permutest.r#58) - Reason: On CRAN
    
     39. permutest() gives correct results for example in Follmann & Proschan (1999). (@test_misc_permutest.r#95) - Reason: On CRAN
    
     40. residuals are correct for rma.glmm(). (@test_misc_residuals.r#86) - Reason: On CRAN
    
     41. rma.glmm() works correctly when using 'clogit' or 'clogistic'. (@test_misc_rma_glmm.r#36) - Reason: On CRAN
    
     42. rma.glmm() works correctly when using 'nlminb' or 'minqa'. (@test_misc_rma_glmm.r#54) - Reason: On CRAN
    
     43. rma.glmm() handles NAs correctly. (@test_misc_rma_handling_nas.r#9) - Reason: On CRAN
    
     44. update() works for rma.glmm(). (@test_misc_update.r#39) - Reason: On CRAN
    
     45. plot can be drawn. (@test_plots_baujat_plot.r#11) - Reason: On CRAN
    
     46. plot can be drawn. (@test_plots_caterpillar_plot.r#11) - Reason: On CRAN
    
     47. plot can be drawn. (@test_plots_contour-enhanced_funnel_plot.r#11) - Reason: On CRAN
    
     48. plot can be drawn for 'rma.uni' object. (@test_plots_cumulative_forest_plot.r#11) - Reason: On CRAN
    
     49. plot can be drawn for 'rma.mh' object. (@test_plots_cumulative_forest_plot.r#49) - Reason: On CRAN
    
     50. plot can be drawn for 'rma.peto' object. (@test_plots_cumulative_forest_plot.r#84) - Reason: On CRAN
    
     51. plot can be drawn. (@test_plots_forest_plot_with_subgroups.r#11) - Reason: On CRAN
    
     52. plot can be drawn. (@test_plots_funnel_plot_variations.r#11) - Reason: On CRAN
    
     53. plot can be drawn. (@test_plots_funnel_plot_with_trim_and_fill.r#11) - Reason: On CRAN
    
     54. plot can be drawn. (@test_plots_gosh.r#11) - Reason: On CRAN
    
     55. plot can be drawn. (@test_plots_labbe_plot.r#11) - Reason: On CRAN
    
     56. plot can be drawn. (@test_plots_llplot.r#11) - Reason: On CRAN
    
     57. plot can be drawn. (@test_plots_meta-analytic_scatterplot.r#11) - Reason: On CRAN
    
     58. plot can be drawn for 'rma.uni' object. (@test_plots_normal_qq_plots.r#11) - Reason: On CRAN
    
     59. plot can be drawn for 'rma.mh' object. (@test_plots_normal_qq_plots.r#49) - Reason: On CRAN
    
     60. plot can be drawn for 'rma.peto' object. (@test_plots_normal_qq_plots.r#64) - Reason: On CRAN
    
     61. plot can be drawn. (@test_plots_plot_of_cumulative_results.r#11) - Reason: On CRAN
    
     62. plot can be drawn. (@test_plots_plot_of_influence_diagnostics.r#11) - Reason: On CRAN
    
     63. plot can be drawn. (@test_plots_radial_plot.r#11) - Reason: On CRAN
    
     ══ Failed ══════════════════════════════════════════════════════════════════════
     ── 1. Failure: the to.long() function works. (@test_analysis_example_rothman2008
     `tmp` not equivalent to `expected`.
     Component "age": target is character, current is factor
     Component "out1": target is character, current is factor
     Component "out2": target is character, current is factor
    
     ── 2. Failure: the to.long() function works. (@test_analysis_example_rothman2008
     `tmp` not equivalent to `expected`.
     Component "age": target is character, current is factor
     Component "events": target is character, current is factor
     Component "ptime": target is character, current is factor
    
     ── 3. Failure: the to.long() function works. (@test_analysis_example_rothman2008
     `tmp` not equivalent to `expected`.
     Component "age": target is character, current is factor
     Component "out1": target is character, current is factor
     Component "out2": target is character, current is factor
    
     ══ DONE ════════════════════════════════════════════════════════════════════════
     Error: Test failures
     Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc