Read news file of the metafor-package.

metafor.news()

Details

The function is just a wrapper for news(package="metafor") which parses and displays the NEWS file of the package.

Author

Wolfgang Viechtbauer wvb@metafor-project.org https://www.metafor-project.org

References

Viechtbauer, W. (2010). Conducting meta-analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1--48. https://doi.org/10.18637/jss.v036.i03

Examples

# \dontrun{ metafor.news()# }
#> Changes in version 3.1-14 (2021-09-01) #> #> - elements of rho and phi in rma.mv() are now based on the lower #> triangular part of the respective correlation matrix (instead of the #> upper triangular part) for consistency with other functions; note #> that this is in principle a backwards incompatible change, although #> this should only be a concern in very special circumstances #> #> - rma.mv() gains cvvc argument (for calculating the var-cov matrix of #> the variance/correlation components) #> #> - added measure "MPORM" to escalc() for computing marginal log odds #> ratios based on marginal 2x2 tables directly (which requires #> specification of the correlation coefficients in the paired tables #> for the calculation of the sampling variances) #> #> - aggregate.escalc() gains checkpd argument #> #> - rma.glmm() now has entire array of optimizers available for #> model="CM.EL" and measure="OR" #> #> - rma.uni() now also provides R^2 for fixed-effects models #> #> - matreg() can now also analyze a covariance matrix with a #> corresponding 'V' matrix #> #> - renamed argument nearPD to nearpd in matreg() (but nearPD continues #> to work) #> #> - plot.profile.rma() gains refline argument #> #> - addpoly.default() and addpoly.rma() gain lty argument #> #> - datasets moved to the metadat package #> (https://cran.r-project.org/package=metadat) #> #> - improved the documentation a bit #> #> Changes in version 3.0-2 (2021-06-09) #> #> - the metafor package now makes use of the mathjaxr package to nicely #> render equations shown in the HTML help pages #> #> - rma() can now also fit location-scale models #> #> - added selmodel() for fitting a wide variety of selection models (and #> added the corresponding plot.rma.uni.selmodel() function for drawing #> the estimated selection function) #> #> - rma.mv() gains dfs argument and now provides an often better way for #> calculating the (denominator) degrees of freedom for approximate t- #> and F-tests when dfs="contain" #> #> - added tes() function for the test of excess significance #> #> - added regplot() function for drawing scatter plots / bubble plots #> based on meta-regression models #> #> - added rcalc() for calculating the variance-covariance matrix of #> correlation coefficients and matreg() for fitting regression models #> based on correlation/covariance matrices #> #> - added convenience functions dfround() and vec2mat() #> #> - added aggregate.escalc() function to aggregate multiple effect sizes #> or outcomes within studies/clusters #> #> - regtest() now shows the 'limit estimate' of the (average) true #> effect when using sei, vi, ninv, or sqrtninv as predictors (and the #> model does not contain any other moderators) #> #> - vif() gains btt argument and can now also compute generalized #> variance inflation factors; a proper print.vif.rma() function was #> also added #> #> - anova.rma() argument L renamed to X (the former still works, but is #> no longer documented) #> #> - argument order in cumul() should now just be a variable, not the #> order of the variable, to be used for ordering the studies and must #> be of the same length as the original dataset that was used in the #> model fitting #> #> - similarly, vector arguments in various plotting functions such as #> forest.rma() must now be of the same length as the original dataset #> that was used in the model fitting (any subsetting and removal of #> NAs is automatically applied) #> #> - the various leave1out() and cumul() functions now provide I^2 and #> H^2 also for fixed-effects models; accordingly, plot.cumul.rma() now #> also works with such models #> #> - fixed level not getting passed down to the various cumul() functions #> #> - plot.cumul.rma() argument addgrid renamed to grid (the former still #> works, but is no longer documented) #> #> - forest.default(), forest.rma(), and labbe() gain plim argument and #> now provide more flexibility in terms of the scaling of the points #> #> - forest.rma() gains colout argument (to adjust the color of the #> observed effect sizes or outcomes) #> #> - in the various forest() functions, the right header is now #> suppressed when annotate=FALSE and header=TRUE #> #> - funnel.default() and funnel.rma() gain label and offset arguments #> #> - funnel.default() and funnel.rma() gain lty argument; the reference #> line is now drawn by default as a dotted line (like the line for the #> pseudo confidence region) #> #> - the forest and funnel arguments of reporter.rma.uni() can now also #> be logicals to suppress the drawing of these plots #> #> - added weighted argument to fsn() (for Orwin's method) #> #> - added some more transformation functions #> #> - bldiag() now properly handles ?x0 or 0x? matrices #> #> - p-values are still given to 2 digits even when digits=1 #> #> - summary.escalc() also provides the p-values (of the Wald-type #> tests); but when using the transf argument, the sampling variances, #> standard errors, test statistics, and p-values are no longer shown #> #> - rma.uni() no longer constrains a fixed tau^2 value to 0 when k=1 #> #> - slight speedup in functions that repeatedly fit rma.uni() models by #> skipping the computation of the pseudo R^2 statistic #> #> - started using the pbapply package for showing progress bars, also #> when using parallel processing #> #> - to avoid potential confusion, all references to 'credibility #> intervals' have been removed from the documentation; these intervals #> are now exclusively referred to as 'prediction intervals'; in the #> output, the bounds are therefore indicated now as pi.lb and pi.ub #> (instead of cr.lb and cr.ub); the corresponding argument names were #> changed in addpoly.default(); argument addcred was changed to #> addpred in addpoly.rma() and forest.rma(); however, code using the #> old arguments names should continue to work #> #> - one can now use weights(..., type="rowsum") for intercept-only #> rma.mv models (to obtain 'row-sum weights') #> #> - simulate.rma() gains olim argument; renamed the clim argument in #> summary.escalc() and the various forest() functions to olim for #> consistency (the old clim argument should continue to work) #> #> - show nicer network graphs for dat.hasselblad1998 and dat.senn2013 in #> the help files #> #> - added 24 datasets (dat.anand1999, dat.assink2016, #> dat.baskerville2012, dat.bornmann2007, dat.cannon2006, #> dat.cohen1981, dat.craft2003, dat.crede2010, dat.dagostino1998, #> dat.damico2009, dat.dorn2007, dat.hahn2001, dat.kalaian1996, #> dat.kearon1998, dat.knapp2017, dat.landenberger2005, dat.lau1992, #> dat.lim2014, dat.lopez2019, dat.maire2019, , dat.moura2021 #> dat.obrien2003, dat.vanhowe1999, dat.viechtbauer2021) #> #> - the package now runs a version check on startup in interactive #> sessions; setting the environment variable METAFOR_VERSION_CHECK to #> FALSE disables this #> #> - refactored various functions (for cleaner/simpler code) #> #> - improved the documentation a bit #> #> Changes in version 2.4-0 (2020-03-19) #> #> - version jump to 2.4-0 for CRAN release (from now on, even minor #> numbers for CRAN releases, odd numbers for development versions) #> #> - the various forest() functions gain header argument #> #> - escalc() gains include argument #> #> - setting verbose=3 in model fitting functions sets options(warn=1) #> #> - forest.rma() and forest.default() now throw informative errors when #> misusing order and subset arguments #> #> - fixed failing tests due to the stringsAsFactors=FALSE change in the #> upcoming version of R #> #> - print.infl.rma.uni() gains infonly argument, to only show the #> influential studies #> #> - removed MASS from Suggests (no longer needed) #> #> - argument btt can now also take a string to grep for #> #> - added optimParallel as possible optimizer in rma.mv() #> #> - added (for now undocumented) option to fit models in rma.glmm() via #> the GLMMadaptive package (instead of lme4); to try this, use: #> control=list(package="GLMMadaptive") #> #> - started to use numbering scheme for devel version (the number after #> the dash indicates the devel version) #> #> - added contrmat() function (for creating a matrix that indicates #> which groups have been compared against each other in each row of a #> dataset) #> #> - added to.wide() function (for restructuring long format datasets #> into the wide format needed for contrast-based analyses) #> #> - I^2 and H^2 are also shown in output for fixed-effects models #> #> - argument grid in baujat() can now also be a color name #> #> - added (for now undocumented) time argument to more functions that #> are computationally expensive #> #> - added (for now undocumented) textpos argument to the various forest #> functions #> #> - added a new dataset (dat.graves2010) #> #> - added more tests #> #> Changes in version 2.1-0 (2019-05-13) #> #> - added formula() method for objects of class rma #> #> - llplot() now also allows for measure="GEN"; also, the documentation #> and y-axis label have been corrected to indicate that the function #> plots likelihoods (not log likelihoods) #> #> - confint.rma.mv() now returns an object of class list.confint.rma #> when obtaining CIs for all variance and correlation components of #> the model; added corresponding print.list.confint.rma() function #> #> - moved tol argument in permutest() to control and renamed to comptol #> #> - added PMM and GENQM estimators in rma.uni() #> #> - added vif() function to get variance inflation factors #> #> - added .glmulti object for making the interaction with glmulti easier #> #> - added reporter() and reporter.rma.uni() for dynamically generating #> analysis reports for objects of class rma.uni #> #> - output is now styled/colored when crayon package is loaded (this #> only works on a 'proper' terminal with color support; also works in #> RStudio) #> #> - overhauled plot.gosh.rma(); when out is specified, it now shows two #> distributions, one for the values when the outlier is included and #> one for the values when for outlier is excluded; dropped the hcol #> argument and added border argument #> #> - refactored influence.rma.uni() to be more consistent internally with #> other functions; print.infl.rma.uni() and plot.infl.rma.uni() #> adjusted accordingly; functions cooks.distance.rma.uni(), #> dfbetas.rma.uni(), and rstudent.rma.uni() now call #> influence.rma.uni() for the computations #> #> - rstudent.rma.uni() now computes the SE of the deleted residuals in #> such a way that it will yield identical results to a mean shift #> outlier model even when that model is fitted with test="knha" #> #> - rstandard.rma.uni() gains type argument, and can now also compute #> conditional residuals (it still computes marginal residuals by #> default) #> #> - cooks.distance.rma.mv() gains cluster argument, so that the Cook's #> distances can be computed for groups of estimates #> #> - cooks.distance.rma.mv() gains parallel, ncpus, and cl arguments and #> can now make use of parallel processing #> #> - cooks.distance.rma.mv() should be faster by using the estimates from #> the full model as starting values when fitting the models with the #> ith study/cluster deleted from the dataset #> #> - cooks.distance.rma.mv() gains reestimate argument; when set to #> FALSE, variance/correlation components are not reestimated #> #> - rstandard.rma.mv() gains cluster argument for computing #> cluster-level multivariate standardized residuals #> #> - added rstudent.rma.mv() and dfbetas.rma.mv() #> #> - smarter matching of elements in newmods (when using a named vector) #> in predict() that also works for models with interactions (thanks to #> Nicole Erler for pointing out the problem) #> #> - rma.uni() and rma.mv() no longer issue (obvious) warnings when user #> constrains vi or V to 0 (i.e., vi=0 or V=0, respectively) #> #> - rma.mv() does more intelligent filtering based on NAs in V matrix #> #> - rma.mv() now ensures strict symmetry of any (var-cov or correlation) #> matrices specified via the R argument #> #> - fixed rma.mv() so checks on R argument run as intended; also fixed #> an issue when multiple formulas with slashes are specified via #> random (thanks to Andrew Loignon for pointing out the problem) #> #> - suppressed showing calls on some warnings/errors in rma.mv() #> #> - rma.mv() now allows for a continuous-time autoregressive random #> effects structure (struct="CAR") and various spatial correlation #> structures (struct="SPEXP", "SPGAU", "SPLIN", "SPRAT", and "SPSPH") #> #> - rma.mv() now allows for struct="GEN" which models correlated random #> effects for any number of predictors, including continuous ones #> (i.e., this allows for 'random slopes') #> #> - in the various forest() functions, when options(na.action="na.pass") #> or options(na.action="na.exclude") and an annotation contains NA, #> this is now shown as a blank (instead of NA [NA, NA]) #> #> - the various forest() and addpoly() functions gain a fonts argument #> #> - the various forest() functions gain a top argument #> #> - the various forest() functions now show correct point sizes when the #> weights of the studies are exactly the same #> #> - forest.cumul.rma() gains a col argument #> #> - funnel.default() and funnel.rma() can now take vectors as input for #> the col and bg arguments (and also for pch); both functions also #> gain a legend argument #> #> - addpoly() functions can now also show prediction interval bounds #> #> - removed 'formula interface' from escalc(); until this actually adds #> some kind of extra functionality, this just makes escalc() more #> confusing to use #> #> - escalc() can now compute the coefficient of variation ratio and the #> variability ratio for pre-post or matched designs ("CVRC", "VRC") #> #> - escalc() does a bit more housekeeping #> #> - added (currently undocumented) arguments onlyo1, addyi, and addvi to #> escalc() that allow for more flexibility when computing certain bias #> corrections and when computing sampling variances for measures that #> make use of the add and to arguments #> #> - escalc() now sets add=0 for measures where the use of such a bias #> correction makes little sense; this applies to the following #> measures: "AS", "PHI", "RTET", "IRSD", "PAS", "PFT", "IRS", and #> "IRFT"; one can still force the use of the bias correction by #> explicitly setting the add argument to some non-zero value #> #> - added clim argument to summary.escalc() #> #> - added ilim argument to trimfill() #> #> - labbe() gains lty argument #> #> - labbe() now (invisibly) returns a data frame with the coordinates of #> the points that were drawn (which may be useful for manual labeling #> of points in the plot) #> #> - added a print method for profile.rma objects #> #> - profile.rma.mv() now check whether any of the profiled #> log-likelihood values is larger than the log-likelihood of the #> fitted model (using numerical tolerance given by lltol) and issues a #> warning if so #> #> - profile.rma.uni(), profile.rma.mv(), and plot.profile.rma() gain #> cline argument; plot.profile.rma() gains xlim, ylab, and main #> arguments #> #> - fixed an issue with robust.rma.mv() when the model was fitted with #> sparse=TRUE (thanks to Roger Martineau for noting the problem) #> #> - various method functions (fitted(), resid(), predict(), etc.) behave #> in a more consistent manner when model omitted studies with missings #> #> - predict.rma() gains vcov argument; when set to TRUE, the #> variance-covariance matrix of the predicted values is also returned #> #> - vcov.rma() can now also return the variance-covariance matrix of the #> fitted values (type="fitted") and the residuals (type="resid") #> #> - added $<- and as.matrix() methods for list.rma objects #> #> - fixed error in simulate.rma() that would generate too many samples #> for rma.mv models #> #> - added undocumented argument time to all model fitting functions; if #> set to TRUE, the model fitting time is printed #> #> - added more tests (also for parallel operations); also, all tests #> updated to use proper tolerances instead of rounding #> #> - reorganized the documentation a bit #> #> Changes in version 2.0-0 (2017-06-22) #> #> - added simulate() method for rma objects; added MASS to Suggests #> (since simulating for rma.mv objects requires mvrnorm() from MASS) #> #> - cooks.distance.rma.mv() now works properly even when there are #> missing values in the data #> #> - residuals() gains type argument and can compute Pearson residuals #> #> - the newmods argument in predict() can now be a named vector or a #> matrix/data frame with column names that get properly matched up #> with the variables in the model #> #> - added ranef.rma.mv() for extracting the BLUPs of the random effects #> for rma.mv models #> #> - all functions that repeatedly refit models now have the option to #> show a progress bar #> #> - added ranktest.default(), so user can now pass the outcomes and #> corresponding sampling variances directly to the function #> #> - added regtest.default(), so user can now pass the outcomes and #> corresponding sampling variances directly to the function #> #> - funnel.default() gains subset argument #> #> - funnel.default() and funnel.rma() gain col and bg arguments #> #> - plot.profile.rma() gains ylab argument #> #> - more consistent handling of robust.rma objects #> #> - added a print method for rma.gosh objects #> #> - the (log) relative risk is now called the (log) risk ratio in all #> help files, plots, code, and comments #> #> - escalc() can now compute outcome measures based on paired binary #> data ("MPRR", "MPOR", "MPRD", "MPORC", and "MPPETO") #> #> - escalc() can now compute (semi-)partial correlation coefficients #> ("PCOR", "ZPCOR", "SPCOR") #> #> - escalc() can now compute measures of variability for single groups #> ("CVLN", "SDLN") and for the difference in variability between two #> groups ("CVR", "VR"); also the log transformed mean ("MNLN") has #> been added for consistency #> #> - escalc() can now compute the sampling variance for measure="PHI" for #> studies using stratified sampling (vtpye="ST") #> #> - the [ method for escalc objects now properly handles the ni and slab #> attributes and does a better job of cleaning out superfluous #> variable name information #> #> - added rbind() method for escalc objects #> #> - added as.data.frame() method for list.rma objects #> #> - added a new dataset (dat.pagliaro1992) for another illustration of a #> network meta-analysis #> #> - added a new dataset (dat.laopaiboon2015) on the effectiveness of #> azithromycin for treating lower respiratory tract infections #> #> - rma.uni() and rma.mv() now check if the ratio of the largest to #> smallest sampling variance is very large; results may not be stable #> then (and very large ratios typically indicate wrongly coded data) #> #> - model fitting functions now check if extra/superfluous arguments are #> specified via ... and issues are warning if so #> #> - instead of defining own generic ranef(), import ranef() from nlme #> #> - improved output formatting #> #> - added more tests (but disabled a few tests on CRAN to avoid some #> issues when R is compiled with --disable-long-double) #> #> - some general code cleanup #> #> - renamed diagram_metafor.pdf vignette to just diagram.pdf #> #> - minor updates in the documentation #> #> Changes in version 1.9-9 (2016-09-25) #> #> - started to use git as version control system, GitHub to host the #> repository (https://github.com/wviechtb/metafor) for the development #> version of the package, Travis CI as continuous integration service #> (https://travis-ci.org/wviechtb/metafor), and Codecov for automated #> code coverage reporting (https://codecov.io/github/wviechtb/metafor) #> #> - argument knha in rma.uni() and argument tdist in rma.glmm() and #> rma.mv() are now superseded by argument test in all three functions; #> for backwards compatibility, the knha and tdist arguments still #> work, but are no longer documented #> #> - rma(yi, vi, weights=1, test="knha") now yields the same results as #> rma(yi, vi, weighted=FALSE, test="knha") (but use of the Knapp and #> Hartung method in the context of an unweighted analysis remains an #> experimental feature) #> #> - one can now pass an escalc object directly to rma.uni(), which then #> tries to automatically determine the yi and vi variables in the data #> frame (thanks to Christian Roever for the suggestion) #> #> - escalc() can now also be used to convert a regular data frame to an #> escalc object #> #> - for measure="UCOR", the exact bias-correction is now used (instead #> of the approximation); when vtype="UB", the exact equation is now #> used to compute the unbiased estimate of the variance of the #> bias-corrected correlation coefficient; hence gsl is now a suggested #> package (needed to compute the hypergeometric function) and is #> loaded when required #> #> - cooks.distance() now also works with rma.mv objects; and since model #> fitting can take some time, an option to show a progress bar has #> been added #> #> - fixed an issue with robust.rma.mv() throwing errors when the model #> was fitted with sparse=TRUE #> #> - fixed an error with robust.rma.mv() when the model was fitted with #> user-defined weights (or a user-defined weight matrix) #> #> - added ranef() for extracting the BLUPs of the random effects (only #> for rma.uni objects at the moment) #> #> - reverted back to the pre-1.1-0 way of computing p-values for #> individual coefficients in permutest.rma.uni(), that is, the p-value #> is computed with mean(abs(z_perm) >= abs(z_obs) - tol) (where tol is #> a numerical tolerance) #> #> - permutest.rma.uni() gains permci argument, which can be used to #> obtain permutation-based CIs of the model coefficients (note that #> this is computationally very demanding and may take a long time to #> complete) #> #> - rma.glmm() continues to work even when the saturated model cannot be #> fitted (although the tests for heterogeneity are not available then) #> #> - rma.glmm() now allows control over the arguments used for #> method.args (via control=list(hessianCtrl=list(...))) passed to #> hessian() (from the numDeriv package) when using model="CM.EL" and #> measure="OR" #> #> - in rma.glmm(), default method.args value for r passed to hessian() #> has been increased to 16 (while this slows things down a bit, this #> appears to improve the accuracy of the numerical approximation to #> the Hessian, especially when tau^2 is close to 0) #> #> - the various forest() and addpoly() functions now have a new argument #> called width, which provides manual control over the width of the #> annotation columns; this is useful when creating complex forest #> plots with a monospaced font and we want to ensure that all #> annotations are properly lined up at the decimal point #> #> - the annotations created by the various forest() and addpoly() #> functions are now a bit more compact by default #> #> - more flexible efac argument in the various forest() functions #> #> - trailing zeros in the axis labels are now dropped in forest and #> funnel plots by default; but trailing zeros can be retained by #> specifying a numeric (and not an integer) value for the digits #> argument #> #> - added funnel.default(), which directly takes as input a vector with #> the observed effect sizes or outcomes and the corresponding sampling #> variances, standard errors, and/or sample sizes #> #> - added plot.profile.rma(), a plot method for objects returned by the #> profile.rma.uni() and profile.rma.mv() functions #> #> - simplified baujat.rma.uni(), baujat.rma.mh(), and baujat.rma.peto() #> to baujat.rma(), which now handles objects of class rma.uni, rma.mh, #> and rma.peto #> #> - baujat.rma() gains argument symbol for more control over the #> plotting symbol #> #> - labbe() gains a grid argument #> #> - more logical placement of labels in qqnorm.rma.uni(), #> qqnorm.rma.mh(), and qqnorm.rma.peto() functions (and more control #> thereof) #> #> - qqnorm.rma.uni() gains lty argument #> #> - added gosh.rma() and plot.gosh.rma() for creating GOSH (i.e., #> graphical display of study heterogeneity) plots based on Olkin et #> al. (2012) #> #> - in the (rare) case where all observed outcomes are exactly equal to #> each other, test="knha" (i.e., knha=TRUE) in rma() now leads to more #> appropriate results #> #> - updated datasets so those containing precomputed effect size #> estimates or observed outcomes are already declared to be escalc #> objects #> #> - added new datasets (dat.egger2001 and dat.li2007) on the #> effectiveness of intravenous magnesium in acute myocardial #> infarction #> #> - methods package is now under Depends (in addition to Matrix), so #> that rma.mv(..., sparse=TRUE) always works, even under Rscript #> #> - some general code cleanup #> #> - added more tests (and used a more consistent naming scheme for #> tests) #> #> Changes in version 1.9-8 (2015-09-28) #> #> - due to more stringent package testing, it is increasingly difficult #> to ensure that the package passes all checks on older versions of R; #> from now on, the package will therefore require, and be checked #> under, only the current (and the development) version of R #> #> - added graphics, grDevices, and methods to Imports (due to recent #> change in how CRAN checks packages) #> #> - the struct argument for rma.mv() now also allows for "ID" and #> "DIAG", which are identical to the "CS" and "HCS" structures, but #> with the correlation parameter fixed to 0 #> #> - added robust() for (cluster) robust tests and confidence intervals #> for rma.uni and rma.mv models (this uses a robust sandwich-type #> estimator of the variance-covariance matrix of the fixed effects #> along the lines of the Eicker-Huber-White method) #> #> - confint() now works for models fitted with the rma.mv() function; #> for variance and correlation parameters, the function provides #> profile likelihood confidence intervals; the output generated by the #> confint() function has been adjusted in general to make the #> formatting more consistent across the different model types #> #> - for objects of class rma.mv, profile() now provides profile plots #> for all (non-fixed) variance and correlation components of the model #> when no component is specified by the user (via the sigma2, tau2, #> rho, gamma2, or phi arguments) #> #> - for measure="MD" and measure="ROM", one can now choose between #> vtype="LS" (the default) and vtype="HO"; the former computes the #> sampling variances without assuming homoscedasticity, while the #> latter assumes homoscedasticity #> #> - multiple model objects can now be passed to the fitstats(), AIC(), #> and BIC() functions #> #> - check for duplicates in the slab argument is now done after any #> subsetting is done (as suggested by Michael Dewey) #> #> - rma.glmm() now again works when using add=0, in which case some of #> the observed outcomes (e.g., log odds or log odds ratios) may be NA #> #> - when using rma.glmm() with model="CM.EL", the saturated model (used #> to compute the Wald-type and likelihood ratio tests for the presence #> of (residual) heterogeneity) often fails to converge; the function #> now continues to run (instead of stopping with an error) and simply #> omits the test results from the output #> #> - when using rma.glmm() with model="CM.EL" and inversion of the #> Hessian fails via the Choleski factorization, the function now makes #> another attempt via the QR decomposition (even when this works, a #> warning is issued) #> #> - for rma.glmm(), BIC and AICc values were switched around; corrected #> #> - more use of suppressWarnings() is made when functions repeatedly #> need to fit the same model, such as cumul(), influence(), and #> profile(); that way, one does not get inundated with the same #> warning(s) #> #> - some (overdue) updates to the documentation #> #> Changes in version 1.9-7 (2015-05-22) #> #> - default optimizer for rma.mv() changed to nlminb() (instead of #> optim() with "Nelder-Mead"); extensive testing indicated that #> nlminb() (and also optim() with "BFGS") is typically quicker and #> more robust; note that this is in principle a non-backwards #> compatible change, but really a necessary one; and you can always #> revert to the old behavior with control=list(optimizer="optim", #> optmethod="Nelder-Mead") #> #> - all tests have been updated in accordance with the recommended #> syntax of the testthat package; for example, expect_equivalent(x,y) #> is used instead of test_that(x, is_equivalent_to(y)) #> #> - changed a few is_identical_to() comparisons to expect_equivalent() #> ones (that failed on Sparc Solaris) #> #> Changes in version 1.9-6 (2015-05-07) #> #> - funnel() now works again for rma.glmm objects (note to self: quit #> breaking things that work!) #> #> - rma.glmm() will now only issue a warning (and not an error) when the #> Hessian for the saturated model cannot be inverted (which is needed #> to compute the Wald-type test for heterogeneity, so the test #> statistic is then simply set to NA) #> #> - rma.mv() now allows for two terms of the form ~ inner | outer; the #> variance components corresponding to such a structure are called #> gamma2 and correlations are called phi; other functions that work #> with objects of class rma.mv have been updated accordingly #> #> - rma.mv() now provides (even) more optimizer choices: nlm() from the #> stats package, hjk() and nmk() from the dfoptim package, and #> ucminf() from the ucminf package; choose the desired optimizer via #> the control argument (e.g., control=list(optimizer="nlm")) #> #> - profile.rma.uni() and profile.rma.mv() now can do parallel #> processing (which is especially relevant for rma.mv objects, where #> profiling is crucial and model fitting can be slow) #> #> - the various confint() functions now have a transf argument (to apply #> some kind of transformation to the model coefficients and confidence #> interval bounds); coefficients and bounds for objects of class #> rma.mh and rma.peto are no longer automatically transformed #> #> - the various forest() functions no longer enforce that the actual #> x-axis limits (alim) encompass the observed outcomes to be plotted; #> also, outcomes below or above the actual x-axis limits are no longer #> shown #> #> - the various forest() functions now provide control over the #> horizontal lines (at the top/bottom) that are automatically added to #> the plot via the lty argument (this also allows for removing them); #> also, the vertical reference line is now placed behind the #> points/CIs #> #> - forest.default() now has argument col which can be used to specify #> the color(s) to be used for drawing the study labels, points, CIs, #> and annotations #> #> - the efac argument for forest.rma() now also allows two values, the #> first for the arrows and CI limits, the second for summary estimates #> #> - corrected some axis labels in various plots when measure="PLO" #> #> - axes in labbe() plots now have "(Group 1)" and "(Group 2)" added by #> default #> #> - anova.rma() gains argument L for specifying linear combinations of #> the coefficients in the model that should be tested to be zero #> #> - in case removal of a row of data would lead to one or more #> inestimable model coefficients, baujat(), cooks.distance(), #> dfbetas(), influence(), and rstudent() could fail for rma.uni #> objects; such cases are now handled properly #> #> - for models with moderators, the predict() function now shows the #> study labels when they have been specified by the user (and newmods #> is not used) #> #> - if there is only one fixed effect (model coefficient) in the model, #> the print.infl.rma.uni() function now shows the DFBETAS values with #> the other case diagnostics in a single table (for easier #> inspection); if there is more than one fixed effect, a separate #> table is still used for the DFBETAS values (with one column for each #> coefficient) #> #> - added measure="SMCRH" to the escalc() function for the standardized #> mean change using raw score standardization with heteroscedastic #> population variances at the two measurement occasions #> #> - added measure="ROMC" to the escalc() function for the (log #> transformed) ratio of means (response ratio) when the means reflect #> two measurement occasions (e.g., for a single group of people) and #> hence are correlated #> #> - added own function for computing/estimating the tetrachoric #> correlation coefficient (for measure="RTET"); package therefore no #> longer suggests polycor but now suggest mvtnorm (which is loaded as #> needed) #> #> - element fill returned by trimfill.rma.uni() is now a logical vector #> (instead of a 0/1 dummy variable) #> #> - print.list.rma() now also returns the printed results invisibly as a #> data frame #> #> - added a new dataset (dat.senn2013) as another illustration of a #> network meta-analysis #> #> - metafor now depends on at least version 3.1.0 of R #> #> Changes in version 1.9-5 (2014-11-24) #> #> - moved the stats and Matrix packages from Depends to Imports; as a #> result, had to add utils to Imports; moved the Formula package from #> Depends to Suggests #> #> - added update.rma() function (for updating/refitting a model); model #> objects also now store and keep the call #> #> - the vcov() function now also extracts the marginal #> variance-covariance matrix of the observed effect sizes or outcomes #> from a fitted model (of class rma.uni or rma.mv) #> #> - rma.mv() now makes use of the Cholesky decomposition when there is a #> random = ~ inner | outer formula and struct="UN"; this is #> numerically more stable than the old approach that avoided #> non-positive definite solutions by forcing the log-likelihood to be #> -Inf in those cases; the old behavior can be restored with control = #> list(cholesky=FALSE) #> #> - rma.mv() now requires the inner variable in an ~ inner | outer #> formula to be a factor or character variable (except when struct is #> "AR" or "HAR"); use ~ factor(inner) | outer in case it isn't #> #> - anova.rma.uni() function changed to anova.rma() that works now for #> both rma.uni and rma.mv objects #> #> - the profile.rma.mv() function now omits the number of the variance #> or correlation component from the plot title and x-axis label when #> the model only includes one of the respective parameters #> #> - profile() functions now pass on the ... argument also to the title() #> function used to create the figure titles (esp. relevant when using #> the cex.main argument) #> #> - the drop00 argument of the rma.mh() and rma.peto() functions now #> also accepts a vector with two logicals, the first applies when #> calculating the observed outcomes, the second when applying the #> Mantel-Haenszel or Peto's method #> #> - weights.rma.uni() now shows the correct weights when weighted=FALSE #> #> - argument showweight renamed to showweights in the forest.default() #> and forest.rma() functions (more consistent with the naming of the #> various weights() functions) #> #> - added model.matrix.rma() function (to extract the model matrix from #> objects of class rma) #> #> - funnel() and radial() now (invisibly) return data frames with the #> coordinates of the points that were drawn (may be useful for manual #> labeling of points in the plots) #> #> - permutest.rma.uni() function now uses a numerical tolerance when #> making comparisons (>= or <=) between an observed test statistic and #> the test statistic under the permuted data; when using random #> permutations, the function now ensures that the very first #> permutation correspond to the original data #> #> - corrected some missing/redundant row/column labels in some output #> #> - most require() calls replaced with requireNamespace() to avoid #> altering the search path (hopefully this won't break stuff ...) #> #> - some non-visible changes including more use of some (non-exported) #> helper functions for common tasks #> #> - dataset dat.collins91985a updated (including all reported outcomes #> and some more information about the various trials) #> #> - oh, and guess what? I updated the documentation ... #> #> Changes in version 1.9-4 (2014-07-30) #> #> - added method="GENQ" to rma.uni() for the generalized Q-statistic #> estimator of tau^2, which allows for used-defined weights (note: the #> DL and HE estimators are just special cases of this method) #> #> - when the model was fitted with method="GENQ", then confint() will #> now use the generalized Q-statistic method to construct the #> corresponding confidence interval for tau^2 (thanks to Dan Jackson #> for the code); the iterative method used to obtain the CI makes use #> of Farebrother's algorithm as implemented in the CompQuadForm #> package #> #> - slight improvements in how the rma.uni() function handles #> non-positive sampling variances #> #> - rma.uni(), rma.mv(), and rma.glmm() now try to detect and remove any #> redundant predictors before the model fitting; therefore, if there #> are exact linear relationships among the predictor variables (i.e., #> perfect multicollinearity), terms are removed to obtain a set of #> predictors that is no longer perfectly multicollinear (a warning is #> issued when this happens); note that the order of how the variables #> are specified in the model formula can influence which terms are #> removed #> #> - the last update introduced an error in how hat values were computed #> when the model was fitted with the rma() function using the Knapp & #> Hartung method (i.e., when knha=TRUE); this has been fixed #> #> - regtest() no longer works (for now) with rma.mv objects (it wasn't #> meant to in the first place); if you want to run something along the #> same lines, just consider adding some measure of the precision of #> the observed outcomes (e.g., their standard errors) as a predictor #> to the model #> #> - added "sqrtni" and "sqrtninv" as possible options for the predictor #> argument of regtest() #> #> - more optimizers are now available for the rma.mv() function via the #> nloptr package by setting control = list(optimizer="nloptr"); when #> using this optimizer, the default is to use the BOBYQA #> implementation from that package with a relative convergence #> criterion of 1e-8 on the function value (see documentation on how to #> change these defaults) #> #> - predict.rma() function now works for rma.mv objects with multiple #> tau^2 values even if the user specifies the newmods argument but not #> the tau2.levels argument (but a warning is issued and the prediction #> intervals are not computed) #> #> - argument var.names now works properly in escalc() when the user has #> not made use of the data argument (thanks to Jarrett Byrnes for #> bringing this to my attention) #> #> - added plot() function for cumulative random-effects models results #> as obtained with the cumul.rma.uni() function; the plot shows the #> model estimate on the x-axis and the corresponding tau^2 estimate on #> the y-axis in the cumulative order of the results #> #> - fixed the omitted offset term in the underlying model fitted by the #> rma.glmm() function when method="ML", measure="IRR", and #> model="UM.FS", that is, when fitting a mixed-effects Poisson #> regression model with fixed study effects to two-group event count #> data (thanks to Peter Konings for pointing out this error) #> #> - added two new datasets (dat.bourassa1996, dat.riley2003) #> #> - added function replmiss() (just a useful helper function) #> #> - package now uses LazyData: TRUE #> #> - some improvements to the documentation (do I still need to mention #> this every time?) #> #> Changes in version 1.9-3 (2014-05-05) #> #> - some minor tweaks to rma.uni() that should be user transparent #> #> - rma.uni() now has a weights argument, allowing the user to specify #> arbitrary user-defined weights; all functions affected by this have #> been updated accordingly #> #> - better handling of mismatched length of yi and ni vectors in #> rma.uni() and rma.mv() functions #> #> - subsetting is now handled as early as possible within functions with #> subsetting capabilities; this avoids some (rare) cases where studies #> ultimately excluded by the subsetting could still affect the results #> #> - some general tweaks to rma.mv() that should make it a bit faster #> #> - argument V of rma.mv() now also accepts a list of var-cov matrices #> for the observed effects or outcomes; from the list elements, the #> full (block diagonal) var-cov matrix V is then automatically #> constructed #> #> - rma.mv() now has a new argument W allowing the user to specify #> arbitrary user-defined weights or an arbitrary weight matrix #> #> - rma.mv() now has a new argument sparse; by setting this to TRUE, the #> function uses sparse matrix objects to the extent possible; this can #> speed up model fitting substantially for certain models (hence, the #> metafor package now depends on the Matrix package) #> #> - rma.mv() now allows for struct="AR" and struct="HAR", to fit models #> with (heteroscedastic) autoregressive (AR1) structures among the #> true effects (useful for meta-analyses of studies reporting outcomes #> at multiple time points) #> #> - rma.mv() now has a new argument Rscale which can be used to control #> how matrices specified via the R argument are scaled (see docs for #> more details) #> #> - rma.mv() now only checks for missing values in the rows of the lower #> triangular part of the V matrix (including the diagonal); this way, #> if Vi = matrix(c(.5,NA,NA,NA), nrow=2, ncol=2) is the var-cov matrix #> of the sampling errors for a particular study with two outcomes, #> then only the second row/column needs to be removed before the model #> fitting (and not the entire study) #> #> - added five new datasets (dat.begg1989, dat.ishak2007, dat.fine1993, #> dat.konstantopoulos2011, and dat.hasselblad1998) to provide further #> illustrations of the use of the rma.mv() function (for meta-analyses #> combining controlled and uncontrolled studies, for meta-analyses of #> longitudinal studies, for multilevel meta-analyses, and for network #> meta-analyses / mixed treatment comparison meta-analyses) #> #> - added rstandard.rma.mv() function to compute standardized residuals #> for models fitted with the rma.mv() function (rstudent.rma.mv() to #> be added at a later point); also added hatvalues.rma.mv() for #> computing the hat values and weights.rma.uni() for computing the #> weights (i.e., the diagonal elements of the weight matrix) #> #> - the various weights() functions now have a new argument type to #> indicate whether only the diagonal elements of the weight matrix #> (default) or the entire weight matrix should be returned #> #> - the various hatvalues() functions now have a new argument type to #> indicate whether only the diagonal elements of the hat matrix #> (default) or the entire hat matrix should be returned #> #> - predict.rma() function now works properly for rma.mv objects (also #> has a new argument tau2.levels to specify, where applicable, the #> levels of the inner factor when computing prediction intervals) #> #> - forest.rma() function now provides a bit more control over the color #> of the summary polygon and is now compatible with rma.mv objects; #> also, has a new argument lty, which provides more control over the #> line type for the individual CIs and the prediction interval #> #> - addpoly.default() and addpoly.rma() now have a border argument (for #> consistency with the forest.rma() function); addpoly.rma() now #> yields the correct CI bounds when the model was fitted with #> knha=TRUE #> #> - forest.cumul.rma() now provides the correct CI bounds when the #> models were fitted with the Knapp & Hartung method (i.e., when #> knha=TRUE in the original rma() function call) #> #> - the various forest() functions now return information about the #> chosen values for arguments xlim, alim, at, ylim, rows, cex, #> cex.lab, and cex.axis invisibly (useful for tweaking the default #> values); thanks to Michael Dewey for the suggestion #> #> - the various forest() functions now have a new argument, clim, to set #> limits for the confidence/prediction interval bounds #> #> - cumul.mh() and cumul.peto() now get the order of the studies right #> when there are missing values in the data #> #> - the transf argument of leave1out.rma.mh(), leave1out.rma.peto(), #> cumul.rma.mh(), and cumul.rma.peto() should now be used to specify #> the actual function for the transformation (the former behavior of #> setting this argument to TRUE to exponentiate log RRs, log ORs, or #> log IRRs still works for back-compatibility); this is more #> consistent with how the cumul.rma.uni() and leave1out.rma.uni() #> functions work and is also more flexible #> #> - added bldiag() function to construct a block diagonal matrix from (a #> list of) matrices (may be needed to construct the V matrix when #> using the rma.mv() function); bdiag() function from the Matrix #> package does the same thing, but creates sparse matrix objects #> #> - profile.rma.mv() now has a startmethod argument; by setting this to #> "prev", successive model fits are started at the parameter estimates #> from the previous model fit; this may speed things up a bit; also, #> the method for automatically choosing the xlim values has been #> changed #> #> - slight improvement to profile.rma.mv() function, which would throw #> an error if the last model fit did not converge #> #> - added a new dataset (dat.linde2005) for replication of the analyses #> in Viechtbauer (2007) #> #> - added a new dataset (dat.molloy2014) for illustrating the #> meta-analysis of (r-to-z transformed) correlation coefficients #> #> - added a new dataset (dat.gibson2002) to illustrate the combined #> analysis of standardized mean differences and probit transformed #> risk differences #> #> - computations in weights.mh() slightly changed to prevent integer #> overflows for large counts #> #> - unnecessary warnings in transf.ipft.hm() are now suppressed (cases #> that raised those warnings were already handled correctly) #> #> - in predict(), blup(), cumul(), and leave1out(), when using the #> transf argument, the standard errors (which are NA) are no longer #> shown in the output #> #> - argument slab in various functions will now also accept non-unique #> study labels; make.unique() is used as needed to make them unique #> #> - vignettes("metafor") and vignettes("metafor_diagram") work again #> (yes, I know they are not true vignettes in the strict sense, but I #> think they should show up on the CRAN website for the package and #> using a minimal valid Sweave document that is recognized by the R #> build system makes that happen) #> #> - escalc() and its summary() method now keep better track when the #> data frame contains multiple columns with outcome or effect size #> values (and corresponding sampling variances) for print formatting; #> also simplified the class structure a bit (and hence, #> print.summary.escalc() removed) #> #> - summary.escalc() has a new argument H0 to specify the value of the #> outcome under the null hypothesis for computing the test statistics #> #> - added measures "OR2DN" and "D2ORN" to escalc() for transforming log #> odds ratios to standardized mean differences and vice-versa, based #> on the method of Cox & Snell (1989), which assumes normally #> distributed response variables within the two groups before the #> dichotomization #> #> - permutest.rma.uni() function now catches an error when the number of #> permutations requested is too large (for R to even create the #> objects to store the results in) and produces a proper error message #> #> - funnel.rma() function now allows the yaxis argument to be set to #> "wi" so that the actual weights (in %) are placed on the y-axis #> (useful when arbitrary user-defined have been specified) #> #> - for rma.glmm(), the control argument optCtrl is now used for passing #> control arguments to all of the optimizers (hence, control arguments #> nlminbCtrl and minqaCtrl are now defunct) #> #> - rma.glmm() should not throw an error anymore when including only a #> single moderator/predictor in the model #> #> - predict.rma() now returns an object of class list.rma (therefore, #> function print.predict.rma() has been removed) #> #> - for rma.list objects, added [, head(), and tail() methods #> #> - automated testing using the testthat package (still many more tests #> to add, but finally made a start on this) #> #> - encoding changed to UTF-8 (to use 'foreign characters' in the docs #> and to make the HTML help files look a bit nicer) #> #> - guess what? some improvements to the documentation! (also combined #> some of the help files to reduce the size of the manual a bit; and #> yes, it's still way too big) #> #> Changes in version 1.9-2 (2013-10-07) #> #> - added function rma.mv() to fit multivariate/multilevel meta-analytic #> models via appropriate linear (mixed-effects) models; this function #> allows for modeling of non-independent sampling errors and/or true #> effects and can be used for network meta-analyses, meta-analyses #> accounting for phylogenetic relatedness, and other complicated #> meta-analytic data structures #> #> - added the AICc to the information criteria computed by the various #> model fitting functions #> #> - if the value of tau^2 is fixed by the user via the corresponding #> argument in rma.uni(), then tau^2 is no longer counted as an #> additional parameter for the computation of the information criteria #> (i.e., AIC, BIC, and AICc) #> #> - rma.uni(), rma.glmm(), and rma.mv() now use a more stringent check #> whether the model matrix is of full rank #> #> - added profile() method functions for objects of class rma.uni and #> rma.mv (can be used to obtain a plot of the profiled log-likelihood #> as a function of a specific variance component or correlation #> parameter of the model) #> #> - predict.rma() function now has an intercept argument that allows the #> user to decide whether the intercept term should be included when #> calculating the predicted values (rare that this should be changed #> from the default) #> #> - for rma.uni(), rma.glmm(), and rma.mv(), the control argument can #> now also accept an integer value; values > 1 generate more verbose #> output about the progress inside of the function #> #> - rma.glmm() has been updated to work with lme4 1.0.x for fitting #> various models; as a result, model="UM.RS" can only use nAGQ=1 at #> the moment (hopefully this will change in the future) #> #> - the control argument of rma.glmm() can now be used to pass all #> desired control arguments to the various functions and optimizers #> used for the model fitting (admittedly the use of lists within this #> argument is a bit unwieldy, but much more flexible) #> #> - rma.mh() and rma.peto() also now have a verbose argument (not really #> needed, but added for sake of consistency across functions) #> #> - fixed (silly) error that would prevent rma.glmm() from running for #> measures "IRR", "PLO", and "IRLN" when there are missing values in #> the data (lesson: add some missing values to datasets for the unit #> tests!) #> #> - a bit of code reorganization (should be user transparent) #> #> - vignettes ("metafor" and "metafor_diagram") are now just 'other #> files' in the doc directory (as these were not true vignettes to #> begin with) #> #> - some improvements to the documentation (as always) #> #> Changes in version 1.9-1 (2013-07-20) #> #> - rma.mh() now also implements the Mantel-Haenszel method for #> incidence rate differences (measure="IRD") #> #> - when analyzing incidence rate ratios (measure="IRR") with the #> rma.mh() function, the Mantel-Haenszel test for person-time data is #> now also provided #> #> - rma.mh() has a new argument correct (default is TRUE) to indicate #> whether the continuity correction should be applied when computing #> the (Cochran-)Mantel-Haenszel test statistic #> #> - renamed elements CMH and CMHp (for the Cochran-Mantel-Haenszel test #> statistic and corresponding p-value) to MH and MHp #> #> - added function baujat() to create Baujat plots #> #> - added a new dataset (dat.pignon2000) to illustrate the use of the #> baujat() function #> #> - added function to.table() to convert data from vector format into #> the corresponding table format #> #> - added function to.long() to convert data from vector format into the #> corresponding long format #> #> - rma.glmm() now even runs when k=1 (yielding trivial results) #> #> - for models with an intercept and moderators, rma.glmm() now #> internally rescales (non-dummy) variables to z-scores during the #> model fitting (this improves the stability of the model fitting, #> especially when model="CM.EL"); results are given after #> back-scaling, so this should be transparent to the user #> #> - in rma.glmm(), default number of quadrature points (nAGQ) is now 7 #> (setting this to 100 was a bit overkill) #> #> - a few more error checks here and there for misspecified arguments #> #> - some improvements to the documentation #> #> Changes in version 1.9-0 (2013-06-21) #> #> - vignette renamed to metafor so vignette("metafor") works now #> #> - added a diagram to the documentation, showing the various functions #> in the metafor package (and how they relate to each other); can be #> loaded with vignette("metafor_diagram") #> #> - anova.rma.uni() function can now also be used to test (sub)sets of #> model coefficients with a Wald-type test when a single model is #> passed to the function #> #> - the pseudo R^2 statistic is now automatically calculated by the #> rma.uni() function and supplied in the output (only for #> mixed-effects models and when the model includes an intercept, so #> that the random- effects model is clearly nested within the #> mixed-effects model) #> #> - component VAF is now called R2 in anova.rma.uni() function #> #> - added function hc() that carries out a random-effects model analysis #> using the method by Henmi and Copas (2010); thanks to Michael Dewey #> for the suggestion and providing the code #> #> - added new dataset (dat.lee2004), which was used in the article by #> Henmi and Copas (2010) to illustrate their method #> #> - fixed missing x-axis labels in the forest() functions #> #> - rma.glmm() now computes Hessian matrices via the numDeriv package #> when model="CM.EL" and measure="OR" (i.e., for the conditional #> logistic model with exact likelihood); so numDeriv is now a #> suggested package and is loaded within rma.glmm() when required #> #> - trimfill.rma.uni() now also implements the "Q0" estimator (although #> the "L0" and "R0" estimators are generally to be preferred) #> #> - trimfill.rma.uni() now also calculates the SE of the estimated #> number of missing studies and, for estimator "R0", provides a formal #> test of the null hypothesis that the number of missing studies on a #> given side is zero #> #> - added new dataset (dat.bangertdrowns2004) #> #> - the level argument in various functions now either accepts a value #> representing a percentage or a proportion (values greater than 1 are #> assumed to be a percentage) #> #> - summary.escalc() now computes confidence intervals correctly when #> using the transf argument #> #> - computation of Cochran-Mantel-Haenszel statistic in rma.mh() changed #> slightly to avoid integer overflow with very big counts #> #> - some internal improvements with respect to object attributes that #> were getting discarded when subsetting #> #> - some general code cleanup #> #> - some improvements to the documentation #> #> Changes in version 1.8-0 (2013-04-11) #> #> - added additional clarifications about the change score outcome #> measures ("MC", "SMCC", and "SMCR") to the help file for the #> escalc() function and changed the code so that "SMCR" no longer #> expects argument sd2i to be specified (which is not needed anyways) #> (thanks to Markus Kösters for bringing this to my attention) #> #> - sampling variance for the biserial correlation coefficient ("RBIS") #> is now calculated in a slightly more accurate way #> #> - llplot() now properly scales the log-likelihoods #> #> - argument which in the plot.infl.rma.uni() function has been replaced #> with argument plotinf which can now also be set to FALSE to suppress #> plotting of the various case diagnostics altogether #> #> - labeling of the axes in labbe() plots is now correct for odds ratios #> (and transformations thereof) #> #> - added two new datasets (dat.nielweise2007 and dat.nielweise2008) to #> illustrate some methods/models from the rma.glmm() function #> #> - added a new dataset (dat.yusuf1985) to illustrate the use of #> rma.peto() #> #> - test for heterogeneity is now conducted by the rma.peto() function #> exactly as described by Yusuf et al. (1985) #> #> - in rma.glmm(), default number of quadrature points (nAGQ) is now 100 #> (which is quite a bit slower, but should provide more than #> sufficient accuracy in most cases) #> #> - the standard errors of the HS and DL estimators of tau^2 are now #> correctly computed when tau^2 is prespecified by the user in the #> rma() function; in addition, the standard error of the SJ estimator #> is also now provided when tau^2 is prespecified #> #> - rma.uni() and rma.glmm() now use a better method to check whether #> the model matrix is of full rank #> #> - I^2 and H^2 statistics are now also calculated for mixed-effects #> models by the rma.uni() and rma.glmm() function; confint.rma.uni() #> provides the corresponding confidence intervals for rma.uni models #> #> - various print() methods now have a new argument called signif.stars, #> which defaults to getOption("show.signif.stars") (which by default #> is TRUE) to determine whether the infamous 'significance stars' #> should be printed #> #> - slight changes in wording in the output produced by the #> print.rma.uni() and print.rma.glmm() functions #> #> - some improvements to the documentation #> #> Changes in version 1.7-0 (2013-02-06) #> #> - added rma.glmm() function for fitting of appropriate generalized #> linear (mixed-effects) models when analyzing odds ratios, incidence #> rate ratios, proportions, or rates; the function makes use of the #> lme4 and BiasedUrn packages; these are now suggested packages and #> loaded within rma.glmm() only when required (this makes for faster #> loading of the metafor package) #> #> - added several method functions for objects of class rma.glmm (not #> all methods yet implemented; to be completed in the future) #> #> - rma.uni() now allows the user to specify a formula for the yi #> argument, so instead of rma(yi, vi, mods=~mod1+mod2), one can #> specify the same model with rma(yi~mod1+mod2, vi) #> #> - rma.uni() now has a weights argument to specify the inverse of the #> sampling variances (instead of using the vi or sei arguments); for #> now, this is all this argument should be used for (in the future, #> this argument may potentially be used to allow the user to define #> alternative weights) #> #> - rma.uni() now checks whether the model matrix is not of full rank #> and issues an error accordingly (instead of the rather cryptic error #> that was issued before) #> #> - rma.uni() now has a verbose argument #> #> - coef.rma() now returns only the model coefficients (this change was #> necessary to make the package compatible with the multcomp package; #> see help(rma) for an example); use coef(summary()) to obtain the #> full table of results #> #> - the escalc() function now does some more extensive error checking #> for misspecified data and some unusual cases #> #> - append argument is now TRUE by default in the escalc() function #> #> - objects generated by the escalc() function now have their own class #> #> - added print() and summary() methods for objects of class escalc #> #> - added [ and cbind() methods for objects of class escalc #> #> - added a few additional arguments to the escalc() function (i.e., #> slab, subset, var.names, replace, digits) #> #> - added drop00 argument to the escalc(), rma.uni(), rma.mh(), and #> rma.peto() functions #> #> - added "MN", "MC", "SMCC", and "SMCR" measures to the escalc() and #> rma.uni() functions for the raw mean, the raw mean change, and the #> standardized mean change (with change score or raw score #> standardization) as possible outcome measures #> #> - the "IRFT" measure in the escalc() and rma.uni() functions is now #> computed with 1/2*(sqrt(xi/ti) + sqrt(xi/ti+1/ti)) which is more #> consistent with the definition of the Freeman-Tukey transformation #> for proportions #> #> - added "RTET" measure to the escalc() and rma.uni() functions to #> compute the tetrachoric correlation coefficient based on 2x2 table #> data (the polycor package is therefore now a suggested package, #> which is loaded within escalc() only when required) #> #> - added "RPB" and "RBIS" measures to the escalc() and rma.uni() #> functions to compute the point-biserial and biserial correlation #> coefficient based on means and standard deviations #> #> - added "PBIT" and "OR2D" measures to the escalc() and rma.uni() #> functions to compute the standardized mean difference based on 2x2 #> table data #> #> - added the "D2OR" measure to the escalc() and rma.uni() functions to #> compute the log odds ratio based on the standardized mean difference #> #> - added "SMDH" measure to the escalc() and rma.uni() functions to #> compute the standardized mean difference without assuming equal #> population variances #> #> - added "ARAW", "AHW", and "ABT" measures to the escalc() and #> rma.uni() functions for the raw value of Cronbach's alpha, the #> transformation suggested by Hakstian & Whalen (1976), and the #> transformation suggested by Bonett (2002) for the meta-analysis of #> reliability coefficients (see help(escalc) for details) #> #> - corrected a small mistake in the equation used to compute the #> sampling variance of the phi coefficient (measure="PHI") in the #> escalc() function #> #> - the permutest.rma.uni() function now uses an algorithm to find only #> the unique permutations of the model matrix (which may be much #> smaller than the total number of permutations), making the exact #> permutation test feasible in a larger set of circumstances (thanks #> to John Hodgson for making me aware of this issue and to Hans-Jörg #> Viechtbauer for coming up with a recursive algorithm for finding the #> unique permutations) #> #> - prediction interval in forest.rma() is now indicated with a dotted #> (instead of a dashed) line; ends of the interval are now marked with #> vertical bars #> #> - completely rewrote the funnel.rma() function which now supports many #> more options for the values to put on the y-axis; trimfill.rma.uni() #> function was adapted accordingly #> #> - removed the ni argument from the regtest.rma() function; instead, #> sample sizes can now be explicitly specified via the ni argument #> when using the rma.uni() function (i.e., when measure="GEN"); the #> escalc() function also now adds information on the ni values to the #> resulting data frame (as an attribute of the yi variable), so, if #> possible, this information is passed on to regtest.rma() #> #> - added switch so that regtest() can also provide the full results #> from the fitted model (thanks to Michael Dewey for the suggestion) #> #> - weights.rma.mh() now shows the weights in % as intended (thanks to #> Gavin Stewart for pointing out this error) #> #> - more flexible handling of the digits argument in the various forest #> functions #> #> - forest functions now use pretty() by default to set the x-axis tick #> locations (alim and at arguments can still be used for complete #> control) #> #> - studies that are considered to be 'influential' are now marked with #> an asterisk when printing the results returned by the #> influence.rma.uni() function (see the documentation of this function #> for details on how such studies are identified) #> #> - added additional extractor functions for some of the influence #> measures (i.e., cooks.distance(), dfbetas()); unfortunately, the #> covratio() and dffits() functions in the stats package are not #> generic; so, to avoid masking, there are currently no extractor #> functions for these measures #> #> - better handling of missing values in some unusual situations #> #> - corrected small bug in fsn() that would not allow the user to #> specify the standard errors instead of the sampling variances #> (thanks to Bernd Weiss for pointing this out) #> #> - plot.infl.rma.uni() function now allows the user to specify which #> plots to draw (and the layout) and adds the option to show study #> labels on the x-axis #> #> - added proper print() method for objects generated by the #> confint.rma.uni(), confint.rma.mh(), and confint.rma.peto() #> functions #> #> - when transf or atransf argument was a monotonically decreasing #> function, then confidence and prediction interval bounds were in #> reversed order; various functions now check for this and order the #> bounds correctly #> #> - trimfill.rma.uni() now only prints information about the number of #> imputed studies when actually printing the model object #> #> - qqnorm.rma.uni(), qqnorm.rma.mh(), and qqnorm.rma.peto() functions #> now have a new argument called label, which allows for labeling of #> points; the functions also now return (invisibly) the x and y #> coordinates of the points drawn #> #> - rma.mh() with measure="RD" now computes the standard error of the #> estimated risk difference based on Sato, Greenland, & Robins (1989), #> which provides a consistent estimate under both large-stratum and #> sparse-data limiting models #> #> - the restricted maximum likelihood (REML) is now calculated using the #> full likelihood equation (without leaving out additive constants) #> #> - the model deviance is now calculated as -2 times the difference #> between the model log-likelihood and the log-likelihood under the #> saturated model (this is a more appropriate definition of the #> deviance than just taking -2 times the model log-likelihood) #> #> - naming scheme of illustrative datasets bundled with the package has #> been changed; now datasets are called <dat.authoryear>; therefore, #> the datasets are now called (old name -> new name): #> #> - dat.bcg -> dat.colditz1994 #> - dat.warfarin -> dat.hart1999 #> - dat.los -> dat.normand1999 #> - dat.co2 -> dat.curtis1998 #> - dat.empint -> dat.mcdaniel1994 #> #> - but dat.bcg has been kept as an alias for dat.colditz1994, as it has #> been referenced under that name in some publications #> #> - added new dataset (dat.pritz1997) to illustrate the meta-analysis of #> proportions (raw values and transformations thereof) #> #> - added new dataset (dat.bonett2010) to illustrate the meta-analysis #> of Cronbach's alpha values (raw values and transformations thereof) #> #> - added new datasets (dat.hackshaw1998, dat.raudenbush1985) #> #> - (approximate) standard error of the tau^2 estimate is now computed #> and shown for most of the (residual) heterogeneity estimators #> #> - added nobs() and df.residual() methods for objects of class rma #> #> - metafor.news() is now simply a wrapper for news(package="metafor") #> #> - the package code is now byte-compiled, which yields some modest #> increases in execution speed #> #> - some general code cleanup #> #> - the metafor package no longer depends on the nlme package #> #> - some improvements to the documentation #> #> Changes in version 1.6-0 (2011-04-13) #> #> - trimfill.rma.uni() now returns a proper object even when the number #> of missing studies is estimated to be zero #> #> - added the (log transformed) ratio of means as a possible outcome #> measure to the escalc() and rma.uni() functions (measure="ROM") #> #> - added new dataset (dat.co2) to illustrate the use of the ratio of #> means outcome measure #> #> - some additional error checking in the various forest functions #> (especially when using the ilab argument) #> #> - in labbe.rma(), the solid and dashed lines are now drawn behind (and #> not on top of) the points #> #> - slight change to transf.ipft.hm() so that missing values in targs$ni #> are ignored #> #> - some improvements to the documentation #> #> Changes in version 1.5-0 (2010-12-16) #> #> - the metafor package now has its own project website at: #> https://www.metafor-project.org/ #> #> - added labbe() function to create L'Abbe plots #> #> - the forest.default() and addpoly.default() functions now allow the #> user to directly specify the lower and upper confidence interval #> bounds (this can be useful when the CI bounds have been calculated #> with other methods/functions) #> #> - added the incidence rate for a single group and for two groups (and #> transformations thereof) as possible outcome measures to the #> escalc() and rma.uni() functions (measure="IRR", "IRD", "IRSD", #> "IR", "IRLN", "IRS", and "IRFT") #> #> - added the incidence rate ratio as a possible outcome measure to the #> rma.mh() function #> #> - added transformation functions related to incidence rates #> #> - added the Freeman-Tukey double arcsine transformation and its #> inverse to the transformation functions #> #> - added some additional error checking for out-of-range p-values in #> the permutest.rma.uni() function #> #> - added some additional checking for out-of-range values in several #> transformation functions #> #> - added confint() methods for rma.mh and rma.peto objects (only for #> completeness sake; print already provides CIs) #> #> - added new datasets (dat.warfarin, dat.los, dat.empint) #> #> - some improvements to the documentation #> #> Changes in version 1.4-0 (2010-07-30) #> #> - a paper about the package has now been published in the Journal of #> Statistical Software (https://www.jstatsoft.org/v36/i03/) #> #> - added citation info; see: citation("metafor") #> #> - the metafor package now depends on the nlme package #> #> - added extractor functions for the AIC, BIC, and deviance #> #> - some updates to the documentation #> #> Changes in version 1.3-0 (2010-06-25) #> #> - the metafor package now depends on the Formula package #> #> - made escalc() generic and implemented a default and a formula #> interface #> #> - added the (inverse) arcsine transformation to the set of #> transformation functions #> #> Changes in version 1.2-0 (2010-05-18) #> #> - cases where k is very small (e.g., k equal to 1 or 2) are now #> handled more gracefully #> #> - added sanity check for cases where all observed outcomes are equal #> to each other (this led to division by zero when using the Knapp & #> Hartung method) #> #> - the "smarter way to set the number of iterations for permutation #> tests" (see notes for previous version below) now actually works #> like it is supposed to #> #> - the permutest.rma.uni() function now provides more sensible results #> when k is very small; the documentation for the function has also #> been updated with some notes about the use of permutation tests #> under those circumstances #> #> - made some general improvements to the various forest plot functions #> making them more flexible in particular when creating more complex #> displays; most importantly, added a rows argument and removed the #> addrows argument #> #> - some additional examples have been added to the help files for the #> forest and addpoly functions to demonstrate how to create more #> complex displays with these functions #> #> - added showweight argument to the forest.default() and forest.rma() #> functions #> #> - cumul() functions not showing all of the output columns when using #> fixed-effects models has been corrected #> #> - weights.rma.uni() function now handles NAs appropriately #> #> - weights.rma.mh() and weights.rma.peto() functions added #> #> - logLik.rma() function now behaves more like other logLik() functions #> (such as logLik.lm() and logLik.lme()) #> #> Changes in version 1.1-0 (2010-04-28) #> #> - cint() generic removed and replaced with confint() method for #> objects of class rma.uni #> #> - slightly improved the code to set the x-axis title in the forest() #> and funnel() functions #> #> - added coef() method for permutest.rma.uni objects #> #> - added append argument to escalc() function #> #> - implemented a smarter way to set the number of iterations for #> permutation tests (i.e., the permutest.rma.uni() function will now #> switch to an exact test if the number of iterations required for an #> exact test is actually smaller than the requested number of #> iterations for an approximate test) #> #> - changed the way how p-values for individual coefficients are #> calculated in permutest.rma.uni() to 'two times the one-tailed area #> under the permutation distribution' (more consistent with the way we #> typically define two-tailed p-values) #> #> - added retpermdist argument to permutest.rma.uni() to return the #> permutation distributions of the test statistics #> #> - slight improvements to the various transformation functions to cope #> better with some extreme cases #> #> - p-values are now calculated in such a way that very small p-values #> stored in fitted model objects are no longer truncated to 0 (the #> printed results are still truncated depending on the number of #> digits specified) #> #> - changed the default number of iterations for the ML, REML, and EB #> estimators from 50 to 100 #> #> Changes in version 1.0-1 (2010-02-02) #> #> - version jump in conjunction with the upcoming publication of a paper #> in the Journal of Statistical Software describing the metafor #> package #> #> - instead of specifying a model matrix, the user can now specify a #> model formula for the mods argument in the rma() function (e.g., #> like in the lm() function) #> #> - permutest() function now allows exact permutation tests (but this is #> only feasible when k is not too large) #> #> - forest() function now uses the level argument properly to adjust the #> CI level of the summary estimate for models without moderators #> (i.e., for fixed- and random-effets models) #> #> - forest() function can now also show the prediction interval as a #> dashed line for a random-effects model #> #> - information about the measure used is now passed on to the forest() #> and funnel() functions, which try to set an appropriate x-axis title #> accordingly #> #> - funnel() function now has more arguments (e.g., atransf, at) #> providing more control over the display of the x-axis #> #> - predict() function now has its own print() method and has a new #> argument called addx, which adds the values of the moderator #> variables to the returned object (when addx=TRUE) #> #> - functions now properly handle the na.action "na.pass" (treated #> essentially like "na.exclude") #> #> - added method for weights() to extract the weights used when fitting #> models with rma.uni() #> #> - some small improvements to the documentation #> #> Changes in version 0.5-7 (2009-12-06) #> #> - added permutest() function for permutation tests #> #> - added metafor.news() function to display the NEWS file of the #> metafor package within R (based on same idea in the animate package #> by Yihui Xie) #> #> - added some checks for values below machine precision #> #> - a bit of code reorganization (nothing that affects how the functions #> work) #> #> Changes in version 0.5-6 (2009-10-19) #> #> - small changes to the computation of the DFFITS and DFBETAS values in #> the influence() function, so that these statistics are more in line #> with their definitions in regular linear regression models #> #> - added option to the plot function for objects returned by #> influence() to allow plotting the covariance ratios on a log scale #> (now the default) #> #> - slight adjustments to various print() functions (to catch some #> errors when certain values were NA) #> #> - added a control option to rma() to adjust the step length of the #> Fisher scoring algorithm by a constant factor (this may be useful #> when the algorithm does not converge) #> #> Changes in version 0.5-5 (2009-10-08) #> #> - added the phi coefficient (measure="PHI"), Yule's Q ("YUQ"), and #> Yule's Y ("YUY") as additional measures to the escalc() function #> for 2x2 table data #> #> - forest plots now order the studies so that the first study is at the #> top of the plot and the last study at the bottom (the order can #> still be set with the order or subset argument) #> #> - added cumul() function for cumulative meta-analyses (with a #> corresponding forest() method to plot the cumulative results) #> #> - added leave1out() function for leave-one-out diagnostics #> #> - added option to qqnorm.rma.uni() so that the user can choose whether #> to apply the Bonferroni correction to the bounds of the pseudo #> confidence envelope #> #> - some internal changes to the class and methods names #> #> - some small corrections to the documentation #> #> Changes in version 0.5-4 (2009-09-18) #> #> - corrected the trimfill() function #> #> - improvements to various print functions #> #> - added a regtest() function for various regression tests of funnel #> plot asymmetry (e.g., Egger's regression test) #> #> - made ranktest() generic and added a method for objects of class rma #> so that the test can be carried out after fitting #> #> - added anova() function for full vs reduced model comparisons via fit #> statistics and likelihood ratio tests #> #> - added the Orwin and Rosenberg approaches to fsn() #> #> - added H^2 measure to the output for random-effects models #> #> - in escalc(), measure="COR" is now used for the (usual) raw #> correlation coefficient and measure="UCOR" for the bias corrected #> correlation coefficients #> #> - some small corrections to the documentation #> #> Changes in version 0.5-3 (2009-07-31) #> #> - small changes to some of the examples #> #> - added the log transformed proportion (measure="PLN") as another #> measure to the escalc() function; changed "PL" to "PLO" for the #> logit (i.e., log odds) transformation for proportions #> #> Changes in version 0.5-2 (2009-07-06) #> #> - added an option in plot.infl.rma.uni() to open a new device for #> plotting the DFBETAS values #> #> - thanks to Jim Lemon, added a much better method for adjusting the #> size of the labels, annotations, and symbols in the forest() #> function when the number of studies is large #> #> Changes in version 0.5-1 (2009-06-14) #> #> - made some small changes to the documentation (some typos corrected, #> some confusing points clarified) #> #> Changes in version 0.5-0 (2009-06-05) #> #> - first version released on CRAN