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--- news:news [2022/08/27 16:19] – Wolfgang Viechtbauer
+++ news:news [2023/03/20 08:23] – Wolfgang Viechtbauer
@@ Line 2: / Line 2: @@
 ~~NOTOC~~
+==== 2023-03-19: Version 4.0-0 Released on CRAN ====
+I am excited to announce the official (i.e., CRAN) release of version 4.0-0 of the metafor package. This will be the 30th update to the package after its initial release in 2009. Since then, the package has grown from a measly 4460 lines of code / 60 functions / 76 pages of documentation to a respectable 36879 lines of code / 330 functions / 347 pages of documentation. Aside from a few improvements related to modeling (e.g., the ''[[https://wviechtb.github.io/metafor/reference/emmprep.html|emmprep()]]'' function provides easier interoperability with the [[https://cran.r-project.org/package=emmeans|emmeans]] package and the ''[[https://wviechtb.github.io/metafor/reference/selmodel.html|selmodel()]]'' function gains a few additional selection models), I would say the focus of this update was on steps that occur prior to modeling, namely the calculation of the chosen effect size measure (or outcome measure as I prefer to call it) and the construction of the dataset in general.
+In particular, the ''[[https://wviechtb.github.io/metafor/reference/escalc.html|escalc()]]'' function now allows the user to also input appropriate test statistics and/or p-values for a number of measures where these can be directly transformed into the corresponding values of the measure. For example, the t-statistic from an independent samples t-test can be easily transformed into a standardized mean difference or the t-statistic from a standard test of a correlation coefficient can be easily transformed into the correlation coefficient or its r-to-z transformed version. Speaking of the latter, essentially all correlation-type measures can now be transformed using the r-to-z transformation, although it should be noted that this is not a proper variance-stabilizing transformation for all measures. This can still be useful though since the r-to-z transformation also has normalizing properties and when combining different types of correlation coefficients in the same analysis (e.g., Pearson product-moment correlations and tetrachoric/biserial correlations).
+Finally, there are now several functions in the package that facilitate the construction of the dataset for a meta-analysis more generally. The ''[[https://wviechtb.github.io/metafor/reference/conv.2x2.html|conv.2x2()]]'' function helps to reconstruct 2x2 tables based on various pieces of information (e.g., odds ratios, chi-square statistics), while the ''[[https://wviechtb.github.io/metafor/reference/conv.fivenum.html|conv.fivenum()]]'' function provides various methods for computing (or more precisely, estimating) means and standard deviations based on five-number summary values (i.e., the minimum, first quartile, median, third quartile, and maximum) and subsets thereof. The ''[[https://wviechtb.github.io/metafor/reference/conv.wald.html|conv.wald()]]'' function converts Wald-type tests and/or confidence intervals to effect sizes and corresponding sampling variances (e.g., to transform a reported odds ratio and its confidence interval to the corresponding log odds ratio and sampling variance). And the ''[[https://wviechtb.github.io/metafor/reference/conv.delta.html|conv.delta()]]'' function transforms effect sizes or outcomes and their sampling variances using the delta method, which can be useful in several data preparations steps. See the documentation of these functions for further details and examples.
+If you come across any issues/bugs, please report them [[https://github.com/wviechtb/metafor/issues|here]]. However, for questions or discussions about these functions (or really anything related to the metafor package or meta-analysis with R in general), please use the [[https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis|R-sig-meta-analysis]] mailing list.
+==== 2022-10-15: Allowing $\tau^2$ to Differ Across Subgroups ====
+A while ago, I wrote up a little discussion on how comparing estimates of independent meta-analyses or subgroups [[tips:comp_two_independent_estimates|here]]. Part of this discussion touched upon subgroup analyses and two possible approaches thereof, one where we assume that the amount of heterogeneity ($\tau^2$) is the same within the various subgroups and one where we relax this assumption and allow $\tau^2$ to differ across groups. I now wrote up a more extensive discussion of this [[tips:different_tau2_across_subgroups|here]] where I also illustrate several different methods/models for conducting a subgroup analysis that allow the amount of heterogeneity to differ across the subgroups.
+==== 2022-10-07: Convergence Problems with the rma.mv() Function ====
+I already had written up a little [[tips:convergence_problems_rma|discussion]] of convergence problems that can arise when using the ''rma()'' (i.e., ''rma.uni()'') function (and some remedies to deal with them) previously. I have now added an analogous discussion for the ''rma.mv()'' function [[tips:convergence_problems_rma_mv|here]].
+==== 2022-09-26: Confidence Intervals for $R^2$ ====
+The (pseudo) $R^2$ statistic that is shown in the output for meta-regression models fitted with the ''rma()'' function provides an estimate of how much of the total amount of heterogeneity is accounted for by the moderator(s) included in the model (Raudenbush, 2009). However, it is important to realize that this statistic can be quite inaccurate, especially when the number of studies ($k$) is small. We may therefore want to construct a confidence interval to get a better sense of how precise the value may be. This can be done using bootstrapping, as illustrated [[tips:ci_for_r2|here]]. I also conducted a small simulation study to examine how well bootstrapping actually works for constructing CIs for $R^2$. The results indicate that the bias-corrected and accelerated (BCa) CI actually works quite well, as long as $k$ is at least 40 and the true value of $R^2$ is not too small.
 ==== 2022-08-27: Version 3.8-1 Released on CRAN ====
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 I also added (experimental!) support for additional measures (e.g., log risk ratios and risk differences) to ''[[https://wviechtb.github.io/metafor/reference/rma.glmm.html|rma.glmm()]]'' (by using log and identity links in the generalized linear mixed-effects model), but using these measures will often lead to estimation problems. For 2x2 table data, the log odds ratio (i.e., using a logit link) is still the preferred choice.
-Another nice feature when computing standardized mean differences with the ''[[https://wviechtb.github.io/metafor/reference/escalc.html|escalc()]]'' function is that one can now specify d-values and t-test statistics directly. This makes it easier to assemble data for a meta-analysis with SMD values, as described [[tips:assembling_data_or|here]].
+Another nice feature when computing standardized mean differences with the ''[[https://wviechtb.github.io/metafor/reference/escalc.html|escalc()]]'' function is that one can now specify d-values and t-test statistics directly. This makes it easier to assemble data for a meta-analysis with SMD values, as described [[tips:assembling_data_smd|here]].
 Aside from this, there were a few smaller improvements. The full changelog can be found [[:updates#version_38-1_2022-08-26|here]].