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--- news:news [2022/08/22 17:40] – Wolfgang Viechtbauer
+++ news:news [2022/09/26 14:55] – Wolfgang Viechtbauer
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 ~~NOTOC~~
+==== 2022-08-27: Version 3.8-1 Released on CRAN ====
+I just sent a new version (3.8-1) of the metafor package to CRAN. This update was prompted due to a small issue in the help pages (related to my use of MathJax to render nice equations in the docs), which was easy to fix. I took the opportunity to incorporate some other updates into the new version, which provide a bit of polish.
+One thing I am kind of excited about is the completely overhauled ''[[https://wviechtb.github.io/metafor/reference/vif.html|vif()]]'' function for computing variance inflation factors. One of the major difficulties with VIFs is their interpretation. Is a particular value 'large'? Commonly used cutoffs like 5 or 10 are quite arbitrary. To make it easier to gauge whether a VIF value is relatively large, one can now simulate the distribution of a VIF under independence, similar to a 'parallel analysis' that is used in factor analysis to determine the number of factors. One can then examine how extreme the actually observed VIF is under this distribution. A [[https://wviechtb.github.io/metafor/reference/plot.vif.rma.html|plot method]] is also available to visualize this.
+There is now some more support for using an identity link when fitting location-scale models with the ''[[https://wviechtb.github.io/metafor/reference/rma.uni.html|rma()]]'' function, although the default log link is typically the better choice and avoids having to use constrained optimization to fit the model.
+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_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]].
 ==== 2022-08-22: Another Multilevel Meta-Analysis Example ====
-My go-to example for illustrating the use of a multilevel meta-analysis is based on the dataset in the paper by [[analyses:konstantopoulos2011|Konstantopoulos (2011)]]. It is a nice example, but slightly unusual, since the multilevel structure in the dataset involves a clustering variable above studies. In practice, one is more likely to encounter cases where studies form the higher level clustering variable, with multiple effects/outcomes reported within at least some of the studies (e.g., for different samples). Such an example is provided by the meta-analysis by [[analyses:crede2010|Credé et al. (2010)]], which I have now added as a case study to the analysis examples.
+My go-to example for illustrating the use of a multilevel meta-analysis is based on the dataset in the paper by [[analyses:konstantopoulos2011|Konstantopoulos (2011)]]. It is a nice example, but slightly unusual, since the multilevel structure in the dataset involves a clustering variable above studies.
+In practice, one is more likely to encounter cases where studies form the higher level clustering variable, with multiple effects/outcomes reported within at least some of the studies (e.g., for different samples). Such an example arises in the meta-analysis by [[analyses:crede2010|Credé et al. (2010)]] on the relationship between class attendance and grades, which I have now added as a case study to the analysis examples.
 ==== 2022-06-19: Difference Between the Omnibus Test and Individual Predictors ====