CRAN Package Check Results for Package metafor

Last updated on 2024-11-02 22:51:40 CET.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 4.6-0 67.28 425.59 492.87 NOTE
r-devel-linux-x86_64-debian-gcc 4.6-0 42.17 250.58 292.75 NOTE
r-devel-linux-x86_64-fedora-clang 4.6-0 806.83 OK
r-devel-linux-x86_64-fedora-gcc 4.6-0 799.59 OK
r-devel-windows-x86_64 4.6-0 67.00 370.00 437.00 NOTE
r-patched-linux-x86_64 4.6-0 73.12 388.31 461.43 OK
r-release-linux-x86_64 4.6-0 67.46 373.18 440.64 ERROR
r-release-macos-arm64 4.6-0 159.00 NOTE
r-release-macos-x86_64 4.6-0 280.00 NOTE
r-release-windows-x86_64 4.6-0 65.00 380.00 445.00 NOTE
r-oldrel-macos-arm64 4.6-0 166.00 NOTE
r-oldrel-macos-x86_64 4.6-0 498.00 NOTE
r-oldrel-windows-x86_64 4.6-0 87.00 499.00 586.00 NOTE

Check Details

Version: 4.6-0
Check: Rd cross-references
Result: NOTE Found the following Rd file(s) with Rd \link{} targets missing package anchors: misc-options.Rd: dat.moura2021 Please provide package anchors for all Rd \link{} targets not in the package itself and the base packages. Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-windows-x86_64

Version: 4.6-0
Check: package dependencies
Result: NOTE Packages suggested but not available for checking: 'lme4', 'minqa', 'lbfgsb3c', 'Epi', 'glmmTMB', 'ape' Flavor: r-release-linux-x86_64

Version: 4.6-0
Check: examples
Result: ERROR Running examples in ‘metafor-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: update.rma > ### Title: Model Updating for 'rma' Objects > ### Aliases: update update.rma > ### Keywords: models > > ### ** Examples > > ### calculate log risk ratios and corresponding sampling variances > dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg) > > ### fit random-effects model (method="REML" is default) > res <- rma(yi, vi, data=dat, digits=3) > res Random-Effects Model (k = 13; tau^2 estimator: REML) tau^2 (estimated amount of total heterogeneity): 0.313 (SE = 0.166) tau (square root of estimated tau^2 value): 0.560 I^2 (total heterogeneity / total variability): 92.22% H^2 (total variability / sampling variability): 12.86 Test for Heterogeneity: Q(df = 12) = 152.233, p-val < .001 Model Results: estimate se zval pval ci.lb ci.ub -0.715 0.180 -3.974 <.001 -1.067 -0.362 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ### fit mixed-effects model with two moderators (absolute latitude and publication year) > res <- update(res, ~ ablat + year) > res Mixed-Effects Model (k = 13; tau^2 estimator: REML) tau^2 (estimated amount of residual heterogeneity): 0.111 (SE = 0.084) tau (square root of estimated tau^2 value): 0.333 I^2 (residual heterogeneity / unaccounted variability): 71.98% H^2 (unaccounted variability / sampling variability): 3.57 R^2 (amount of heterogeneity accounted for): 64.63% Test for Residual Heterogeneity: QE(df = 10) = 28.325, p-val = 0.002 Test of Moderators (coefficients 2:3): QM(df = 2) = 12.204, p-val = 0.002 Model Results: estimate se zval pval ci.lb ci.ub intrcpt -3.546 29.096 -0.122 0.903 -60.572 53.481 ablat -0.028 0.010 -2.737 0.006 -0.048 -0.008 ** year 0.002 0.015 0.130 0.897 -0.027 0.031 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ### remove 'year' moderator > res <- update(res, ~ . - year) > res Mixed-Effects Model (k = 13; tau^2 estimator: REML) tau^2 (estimated amount of residual heterogeneity): 0.076 (SE = 0.059) tau (square root of estimated tau^2 value): 0.276 I^2 (residual heterogeneity / unaccounted variability): 68.39% H^2 (unaccounted variability / sampling variability): 3.16 R^2 (amount of heterogeneity accounted for): 75.62% Test for Residual Heterogeneity: QE(df = 11) = 30.733, p-val = 0.001 Test of Moderators (coefficient 2): QM(df = 1) = 16.357, p-val < .001 Model Results: estimate se zval pval ci.lb ci.ub intrcpt 0.251 0.249 1.009 0.313 -0.237 0.740 ablat -0.029 0.007 -4.044 <.001 -0.043 -0.015 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ### fit model with ML estimation > update(res, method="ML") Mixed-Effects Model (k = 13; tau^2 estimator: ML) tau^2 (estimated amount of residual heterogeneity): 0.034 (SE = 0.028) tau (square root of estimated tau^2 value): 0.185 I^2 (residual heterogeneity / unaccounted variability): 49.33% H^2 (unaccounted variability / sampling variability): 1.97 R^2 (amount of heterogeneity accounted for): 87.73% Test for Residual Heterogeneity: QE(df = 11) = 30.733, p-val = 0.001 Test of Moderators (coefficient 2): QM(df = 1) = 28.911, p-val < .001 Model Results: estimate se zval pval ci.lb ci.ub intrcpt 0.282 0.187 1.507 0.132 -0.085 0.649 ablat -0.030 0.005 -5.377 <.001 -0.040 -0.019 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 > > ### example with rma.glmm() > res <- rma.glmm(measure="OR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg, digits=3) Error in rma.glmm(measure = "OR", ai = tpos, bi = tneg, ci = cpos, di = cneg, : Please install the 'lme4' package to fit this model. Execution halted Flavor: r-release-linux-x86_64

Version: 4.6-0
Check: tests
Result: ERROR Running ‘testthat.R’ [44s/61s] 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 required package: metadat Loading required package: numDeriv Loading the 'metafor' package (version 4.6-0). For an introduction to the package please type: help(metafor) > test_check("metafor", reporter="summary") analysis_example_berkey1995: Checking analysis example: berkey1995: ............ analysis_example_berkey1998: Checking analysis example: berkey1998: .............. analysis_example_dersimonian2007: Checking analysis example: dersimonian2007: S analysis_example_gleser2009: Checking analysis example: gleser2009: ....................... analysis_example_henmi2010: Checking analysis example: henmi2010: ....... analysis_example_ishak2007: Checking analysis example: ishak2007: ....................... analysis_example_jackson2014: Checking analysis example: jackson2014: SS analysis_example_konstantopoulos2011: Checking analysis example: konstantopoulos2011: ..............................S......SSSS analysis_example_law2016: Checking analysis example: law2016: SS analysis_example_lipsey2001: Checking analysis example: lipsey2001: .......................... analysis_example_miller1978: Checking analysis example: miller1978: ...........S analysis_example_morris2008: Checking analysis example: morris2008: .............. analysis_example_normand1999: Checking analysis example: normand1999: .............................. analysis_example_raudenbush1985: Checking analysis example: raudenbush1985: ..........S.............S analysis_example_raudenbush2009: Checking analysis example: raudenbush2009: .................. analysis_example_rothman2008: Checking analysis example: rothman2008: .............................S.....................S..............S analysis_example_stijnen2010: Checking analysis example: stijnen2010: ............S.......SS............S......S analysis_example_vanhouwelingen1993: Checking analysis example: vanhouwelingen1993: SSS analysis_example_vanhouwelingen2002: Checking analysis example: vanhouwelingen2002: ..............S.S....S......................... analysis_example_viechtbauer2005: Checking analysis example: viechtbauer2005: ........ analysis_example_viechtbauer2007a: Checking analysis example: viechtbauer2007a: .......S...SS analysis_example_viechtbauer2007b: Checking analysis example: viechtbauer2007b: ............S analysis_example_yusuf1985: Checking analysis example: yusuf1985: S..... misc_aggregate: Checking misc: aggregate() function: .............. misc_anova: Checking misc: anova() function: .......................... misc_calc_q: Checking misc: computation of Q-test: .......... misc_confint: Checking misc: confint() function: ....... misc_dfround: Checking misc: dfround() function: .. misc_diagnostics_rma.mv: Checking misc: model diagnostic functions for rma.mv(): SS misc_emmprep: Checking misc: emmprep() function: SSS misc_escalc: Checking misc: escalc() function: ............................................................................................................. misc_fitstats: Checking misc: computations of fit statistics: ....................... misc_formula: Checking misc: formula() function: ....... misc_fsn: Checking misc: fsn() function: .....S....S....S misc_funnel: Checking misc: funnel() functions: .S misc_handling_nas: Checking misc: handling of NAs: ...................................................................................... misc_handling_of_edge_cases_due_to_zeros: Checking misc: handling of edge cases due to zeros: .......S.......S misc_influence: Checking misc: influence() and related functions: ......................... misc_list_rma: Checking misc: head.list.rma() and tail.list.rma() functions: .... misc_matreg: Checking misc: matreg() function: ......... misc_metan_vs_rma.mh_with_dat.bcg: Checking misc: rma.mh() against metan with 'dat.bcg': ..................... misc_metan_vs_rma.peto_with_dat.bcg: Checking misc: rma.peto() against metan with 'dat.bcg': ........ misc_metan_vs_rma.uni_with_dat.bcg: Checking misc: rma.uni() against metan with 'dat.bcg': ............................................. misc_pdfs: Checking misc: pdfs of various measures: ..... misc_permutest: Checking misc: permutest() function: SSS misc_plot_rma: Checking misc: plot() function: .S.S.S misc_predict: Checking misc: predict() function: ................... misc_pub_bias: Checking misc: regtest() and ranktest() functions: ............ misc_replmiss: Checking misc: replmiss() function: ... misc_reporter: Checking misc: reporter() function: .S misc_residuals: Checking misc: residuals() function: .....................S misc_rma_error_handling: Checking misc: proper handling of errors in rma(): ...... misc_rma_glmm: Checking misc: rma.glmm() function: ...12SS misc_rma_handling_nas: Checking misc: proper handling of missing values: S misc_rma_ls: Checking misc: location-scale models: ...........SSSSSSSS misc_rma_mv: Checking misc: rma.mv() function: ..................3.. misc_rma_uni: Checking misc: rma() function: .............. misc_rma_uni_ls: Checking misc: rma() function with location-scale models: ............... misc_rma_vs_direct_computation: Checking misc: rma.uni() against direct computations: ..... misc_rma_vs_lm: Checking tip: rma() results match up with those from lm(): ........ misc_robust: Checking misc: robust() function: .......................................................................... misc_selmodel: Checking misc: selmodel() function: .S.S.S.S.S misc_setlab: Checking misc: .setlab() function: .S misc_tes: Checking misc: tes() function: ......S misc_to_long_table_wide: Checking misc: to.long() function: ...................... misc_transf: Checking misc: transformation functions: ....................... misc_update: Checking misc: update() function: ....S misc_vcalc: Checking misc: vcalc() function: ...... misc_vcov: Checking misc: vcov() function: ........ misc_vec2mat: Checking misc: vec2mat() function: .... misc_vif: Checking misc: vif() function: ... misc_weights: Checking misc: weights() function: .......................... plots_baujat_plot: Checking plots example: Baujat plot: .S plots_caterpillar_plot: Checking plots example: caterpillar plot: .S plots_contour-enhanced_funnel_plot: Checking plots example: contour-enhanced funnel plot: .S plots_cumulative_forest_plot: Checking plots example: cumulative forest plot: .S.S.S plots_forest_plot_with_subgroups: Checking plots example: forest plot with subgroups: .S plots_funnel_plot_variations: Checking plots example: funnel plot variations: .S plots_funnel_plot_with_trim_and_fill: Checking plots example: funnel plot with trim and fill: .S plots_gosh: Checking plots example: GOSH plot: .S plots_labbe_plot: Checking plots example: L'Abbe plot: .S plots_llplot: Checking plots example: likelihood plot: .S plots_meta-analytic_scatterplot: Checking plots example: meta-analytic scatterplot: .S plots_normal_qq_plots: Checking plots example: normal QQ plots: .S.S.S. plots_plot_of_cumulative_results: Checking plots example: plot of cumulative results: .S plots_plot_of_influence_diagnostics: Checking plots example: plot of influence diagnostics: .S plots_radial_plot: Checking plots example: radial (Galbraith) plot: .S plots_regplot: Checking plots example: scatter/bubble plot: .S tips_regression_with_rma: Checking tip: rma() results match up with those from lm(): ........... tips_rma_vs_lm_and_lme: 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:17:4') - Reason: On CRAN 2. confint() gives correct results for example 1 in Jackson et al. (2014). ('test_analysis_example_jackson2014.r:9:4') - Reason: On CRAN 3. confint() gives correct results for example 2 in Jackson et al. (2014). ('test_analysis_example_jackson2014.r:49:4') - Reason: On CRAN 4. profiling works for the three-level random-effects model (multilevel parameterization). ('test_analysis_example_konstantopoulos2011.r:119:4') - Reason: On CRAN 5. profiling works for the three-level random-effects model (multivariate parameterization). ('test_analysis_example_konstantopoulos2011.r:156:4') - Reason: On CRAN 6. BLUPs are calculated correctly for the three-level random-effects model (multilevel parameterization). ('test_analysis_example_konstantopoulos2011.r:174:4') - Reason: On CRAN 7. restarting with 'restart=TRUE' works. ('test_analysis_example_konstantopoulos2011.r:190:4') - Reason: On CRAN 8. results are correct when allowing for different tau^2 per district. ('test_analysis_example_konstantopoulos2011.r:204:4') - Reason: On CRAN 9. results are correct for example 1. ('test_analysis_example_law2016.r:9:4') - Reason: On CRAN 10. results are correct for example 2. ('test_analysis_example_law2016.r:86:4') - Reason: On CRAN 11. back-transformations work as intended for individual studies and the model estimate. ('test_analysis_example_miller1978.r:80:4') - Reason: On CRAN 12. results are correct for the random-effects model. ('test_analysis_example_raudenbush1985.r:40:4') - Reason: On CRAN 13. results are correct for the mixed-effects model. ('test_analysis_example_raudenbush1985.r:102:4') - Reason: On CRAN 14. results are correct for Mantel-Haenszel method. ('test_analysis_example_rothman2008.r:133:4') - Reason: On CRAN 15. results are correct for Mantel-Haenszel method. ('test_analysis_example_rothman2008.r:269:4') - Reason: On CRAN 16. results are correct for Mantel-Haenszel method. ('test_analysis_example_rothman2008.r:363:4') - Reason: On CRAN 17. results for the binomial-normal normal are correct (measure=='PLO') ('test_analysis_example_stijnen2010.r:40:4') - Reason: On CRAN 18. results for the conditional logistic model with exact likelihood are correct (measure=='OR') ('test_analysis_example_stijnen2010.r:83:4') - Reason: On CRAN 19. results for the conditional logistic model with approximate likelihood are correct (measure=='OR') ('test_analysis_example_stijnen2010.r:101:4') - Reason: On CRAN 20. results for the Poisson-normal model are correct (measure=='IRLN') ('test_analysis_example_stijnen2010.r:153:4') - Reason: On CRAN 21. results for the Poisson-normal model are correct (measure=='IRR') ('test_analysis_example_stijnen2010.r:196:4') - Reason: On CRAN 22. the log likelihood plot can be created. ('test_analysis_example_vanhouwelingen1993.r:14:4') - Reason: On CRAN 23. results of the equal-effects conditional logistic model are correct. ('test_analysis_example_vanhouwelingen1993.r:28:4') - Reason: On CRAN 24. results of the random-effects conditional logistic model are correct. ('test_analysis_example_vanhouwelingen1993.r:53:4') - Reason: On CRAN 25. profile plot for tau^2 can be drawn. ('test_analysis_example_vanhouwelingen2002.r:65:4') - Reason: On CRAN 26. forest plot of observed log(OR)s and corresponding BLUPs can be drawn. ('test_analysis_example_vanhouwelingen2002.r:82:4') - Reason: On CRAN 27. L'Abbe plot can be drawn. ('test_analysis_example_vanhouwelingen2002.r:123:4') - Reason: On CRAN 28. CI is correct for the profile likelihood method. ('test_analysis_example_viechtbauer2007a.r:79:4') - Reason: On CRAN 29. CI is correct for the parametric bootstrap method. ('test_analysis_example_viechtbauer2007a.r:121:4') - Reason: On CRAN 30. CI is correct for the non-parametric bootstrap method. ('test_analysis_example_viechtbauer2007a.r:159:4') - Reason: On CRAN 31. results are correct for the mixed-effects model. ('test_analysis_example_viechtbauer2007b.r:74:4') - Reason: On CRAN 32. log likelihood plot can be drawn. ('test_analysis_example_yusuf1985.r:15:4') - Reason: On CRAN 33. model diagnostic functions work with 'na.omit'. ('test_misc_diagnostics_rma.mv.r:29:4') - Reason: On CRAN 34. model diagnostic functions work with 'na.pass'. ('test_misc_diagnostics_rma.mv.r:160:4') - Reason: On CRAN 35. emmprep() gives correct results for an intercept-only model. ('test_misc_emmprep.r:16:4') - Reason: On CRAN 36. emmprep() gives correct results for a meta-regression model. ('test_misc_emmprep.r:36:4') - Reason: On CRAN 37. emmprep() gives correct results for the r-to-z transformation. ('test_misc_emmprep.r:63:4') - Reason: On CRAN 38. confint() gives correct results for the 'expectancy data' in Becker (2005). ('test_misc_fsn.r:33:4') - Reason: On CRAN 39. confint() gives correct results for the 'passive smoking data' in Becker (2005). ('test_misc_fsn.r:65:4') - Reason: On CRAN 40. confint() gives correct results for the 'interview data' in Becker (2005). ('test_misc_fsn.r:93:4') - Reason: On CRAN 41. funnel() works correctly. ('test_misc_funnel.r:11:4') - Reason: On CRAN 42. rma.peto(), rma.mh(), and rma.glmm() handle outcome1 never occurring properly. ('test_misc_handling_of_edge_cases_due_to_zeros.r:23:4') - Reason: On CRAN 43. rma.peto(), rma.mh(), and rma.glmm() handle outcome2 never occurring properly. ('test_misc_handling_of_edge_cases_due_to_zeros.r:45:4') - Reason: On CRAN 44. permutest() gives correct results for a random-effects model. ('test_misc_permutest.r:15:4') - Reason: On CRAN 45. permutest() gives correct results for a mixed-effects model. ('test_misc_permutest.r:45:4') - Reason: On CRAN 46. permutest() gives correct results for example in Follmann & Proschan (1999). ('test_misc_permutest.r:69:4') - Reason: On CRAN 47. plot can be drawn for rma(). ('test_misc_plot_rma.r:11:4') - Reason: On CRAN 48. plot can be drawn for rma.mh(). ('test_misc_plot_rma.r:36:4') - Reason: On CRAN 49. plot can be drawn for rma.peto(). ('test_misc_plot_rma.r:52:4') - Reason: On CRAN 50. reporter() works correctly for 'rma.uni' objects. ('test_misc_reporter.r:12:4') - Reason: On CRAN 51. residuals are correct for rma.glmm(). ('test_misc_residuals.r:81:4') - Reason: On CRAN 52. rma.glmm() works correctly when using 'clogit' or 'clogistic'. ('test_misc_rma_glmm.r:89:4') - Reason: On CRAN 53. rma.glmm() works correctly for 'CM.EL' model. ('test_misc_rma_glmm.r:107:4') - Reason: On CRAN 54. rma.glmm() handles NAs correctly. ('test_misc_rma_handling_nas.r:9:4') - Reason: On CRAN 55. location-scale model works correctly for two subgroups with different tau^2 values ('test_misc_rma_ls.r:38:4') - Reason: On CRAN 56. profile() and confint() work correctly for location-scale models ('test_misc_rma_ls.r:49:4') - Reason: On CRAN 57. location-scale model works correctly for a continuous predictor ('test_misc_rma_ls.r:92:4') - Reason: On CRAN 58. location-scale model works correctly for multiple predictors ('test_misc_rma_ls.r:155:4') - Reason: On CRAN 59. permutation tests work correctly for a location-scale model ('test_misc_rma_ls.r:196:4') - Reason: On CRAN 60. predict() works correctly for location-scale models ('test_misc_rma_ls.r:218:4') - Reason: On CRAN 61. anova() works correctly for location-scale models ('test_misc_rma_ls.r:259:4') - Reason: On CRAN 62. vif() works correctly for location-scale models ('test_misc_rma_ls.r:296:4') - Reason: On CRAN 63. results are correct for a step function model. ('test_misc_selmodel.r:11:4') - Reason: On CRAN 64. results are correct for the beta function model. ('test_misc_selmodel.r:55:4') - Reason: On CRAN 65. results are correct for the various exponential function models. ('test_misc_selmodel.r:104:4') - Reason: On CRAN 66. results are correct for a pirori chosen step function models. ('test_misc_selmodel.r:162:4') - Reason: On CRAN 67. results are correct for a truncated distribution model. ('test_misc_selmodel.r:186:4') - Reason: On CRAN 68. .setlab() works correctly together with forest(). ('test_misc_setlab.r:14:4') - Reason: On CRAN 69. tes() works correctly for 'dat.dorn2007'. ('test_misc_tes.r:25:4') - Reason: On CRAN 70. update() works for rma.glmm(). ('test_misc_update.r:45:4') - Reason: On CRAN 71. plot can be drawn. ('test_plots_baujat_plot.r:13:4') - Reason: On CRAN 72. plot can be drawn. ('test_plots_caterpillar_plot.r:13:4') - Reason: On CRAN 73. plot can be drawn. ('test_plots_contour-enhanced_funnel_plot.r:13:4') - Reason: On CRAN 74. plot can be drawn for 'rma.uni' object. ('test_plots_cumulative_forest_plot.r:13:4') - Reason: On CRAN 75. plot can be drawn for 'rma.mh' object. ('test_plots_cumulative_forest_plot.r:44:4') - Reason: On CRAN 76. plot can be drawn for 'rma.peto' object. ('test_plots_cumulative_forest_plot.r:71:4') - Reason: On CRAN 77. plot can be drawn. ('test_plots_forest_plot_with_subgroups.r:13:4') - Reason: On CRAN 78. plot can be drawn. ('test_plots_funnel_plot_variations.r:13:4') - Reason: On CRAN 79. plot can be drawn. ('test_plots_funnel_plot_with_trim_and_fill.r:13:4') - Reason: On CRAN 80. plot can be drawn. ('test_plots_gosh.r:13:4') - Reason: On CRAN 81. plot can be drawn. ('test_plots_labbe_plot.r:13:4') - Reason: On CRAN 82. plot can be drawn. ('test_plots_llplot.r:11:4') - Reason: On CRAN 83. plot can be drawn. ('test_plots_meta-analytic_scatterplot.r:13:4') - Reason: On CRAN 84. plot can be drawn for 'rma.uni' object. ('test_plots_normal_qq_plots.r:13:4') - Reason: On CRAN 85. plot can be drawn for 'rma.mh' object. ('test_plots_normal_qq_plots.r:54:4') - Reason: On CRAN 86. plot can be drawn for 'rma.peto' object. ('test_plots_normal_qq_plots.r:72:4') - Reason: On CRAN 87. plot can be drawn. ('test_plots_plot_of_cumulative_results.r:13:4') - Reason: On CRAN 88. plot can be drawn. ('test_plots_plot_of_influence_diagnostics.r:13:4') - Reason: On CRAN 89. plot can be drawn. ('test_plots_radial_plot.r:13:4') - Reason: On CRAN 90. plot can be drawn. ('test_plots_regplot.r:13:4') - Reason: On CRAN ══ Failed ══════════════════════════════════════════════════════════════════════ ── 1. Error ('test_misc_rma_glmm.r:17:4'): rma.glmm() works correctly for 'UM.FS Error in `rma.glmm(measure = "OR", ai = ai, n1i = n1i, ci = ci, n2i = n2i, data = dat, model = "UM.FS", test = "t")`: Please install the 'lme4' package to fit this model. Backtrace: ▆ 1. ├─testthat::expect_warning(...) at test_misc_rma_glmm.r:17:4 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. └─metafor::rma.glmm(...) ── 2. Error ('test_misc_rma_glmm.r:48:4'): rma.glmm() works correctly for 'UM.RS Error in `rma.glmm(measure = "OR", ai = ai, n1i = n1i, ci = ci, n2i = n2i, data = dat, model = "UM.RS", method = "EE")`: Please install the 'lme4' package to fit this model. Backtrace: ▆ 1. ├─testthat::expect_warning(...) at test_misc_rma_glmm.r:48:4 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. └─metafor::rma.glmm(...) ── 3. Error ('test_misc_rma_mv.r:102:4'): rma.mv() works correctly with differen Error: Please install the 'minqa' package to use this optimizer. Backtrace: ▆ 1. └─metafor::rma.mv(...) at test_misc_rma_mv.r:102:4 2. └─metafor:::.chkopt(optimizer, optcontrol) ══ DONE ════════════════════════════════════════════════════════════════════════ Error: Test failures Execution halted Flavor: r-release-linux-x86_64

Version: 4.6-0
Check: HTML version of manual
Result: NOTE Skipping checking math rendering: package 'V8' unavailable Flavor: r-release-linux-x86_64

Version: 4.6-0
Check: installed package size
Result: NOTE installed size is 5.2Mb sub-directories of 1Mb or more: R 2.1Mb help 2.2Mb Flavors: r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64, r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64