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--- tips [2020/05/27 21:05] – Wolfgang Viechtbauer
+++ tips [2021/12/20 15:22] – Wolfgang Viechtbauer
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 The links below point to pages illustrating various tips and notes that may be useful when working with the metafor package. In addition, some features of the package that may not be readily apparent from the documentation are explained in more detail.
+**Note:** If an example does not work properly, try installing the development version of the metafor package as described [[installation#development_version|here]].
   * [[tips:handling_missing_data|Handling Missing Data in Output/Figures]]: An illustration/discussion of how to show studies in figures and output that were actually excluded from model fitting due to missing data.
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   * [[tips:two_stage_analysis|Two-Stage Analysis versus Linear Mixed-Effects Models for Longitudinal Data]]: An illustration of two different approaches to analyzing longitudinal data: A two-stage analysis (which the ''rma.mv()'' function can be used for) and linear mixed-effects models (e.g., using the ''lme()'' function).
-  * [[tips:rma.uni_vs_rma.mv|A Comparison of the rma.uni() and rma.mv() Functions]]: A comparison of the ''rma.uni()'' and ''rma.mv()'' functions for fitting fixed- and random-effects models.
+  * [[tips:rma.uni_vs_rma.mv|A Comparison of the rma.uni() and rma.mv() Functions]]: A comparison of the ''rma.uni()'' and ''rma.mv()'' functions for fitting equal- and random-effects models.
   * [[tips:rma_vs_lm_lme_lmer|A Comparison of the rma() and the lm(), lme(), and lmer() Functions]]: An illustration of the difference between the models fitted by the ''rma()'' function and the models fitted by the ''lm()'', ''lme()'', and ''lmer()'' functions (or: why the ''lm()'', ''lme()'', and ''lmer()'' functions cannot be used to fit meta-analytic models).
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   * [[tips:clogit_paired_binary_data|Conditional Logistic Regression for Paired Binary Data]]: An illustration of how to fit the conditional logistic regression model for paired binary data.
-  * [[tips:i2_multilevel_multivariate|I^2 for Multilevel and Multivariate Models]]: A discussion of how one can compute $I^2$-type statistics in multilevel and multivariate models.
+  * [[tips:i2_multilevel_multivariate|$I^2$ for Multilevel and Multivariate Models]]: A discussion of how one can compute $I^2$-type statistics in multilevel and multivariate models.
   * [[tips:hunter_schmidt_method|Hunter and Schmidt Method]]: A discussion of how one can conduct meta-analyses according to the Hunter & Schmidt method.
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   * [[tips:computing_adjusted_effects|Computing Adjusted Effects Based on Meta-Regression Models]]: A discussion of how to compute 'adjusted effects' based on meta-regression models.
+  * [[tips:weights_in_rma.mv_models|Weights in Models Fitted with the rma.mv() Function]]: A discussion of how weighting works in more complex models, such as those that can be fitted with the ''rma.mv()'' function.