Changelog

some general changes to the various forest() functions: argument header is now TRUE by default, the y-axis is now created with yaxs="i", and the y-axis limits have been tweaked slightly in accordance
forest.rma() and the various addpoly() functions now provides multiple styles for drawing the prediction interval via the predstyle argument
forest.rma() and addpoly.rma() now write out the default label (instead of an abbreviation) for the model results; as before, the label can be changed via the mlab argument
added an ilab.lab argument to the various forest() functions for adding header labels to the plot for the additional study information columns
all plot functions that create multi-panel plots now behave in a consistent manner, setting par(mfrow) automatically when no plotting device is open or when the number of panels in an open plotting device is too small for the number of panels to be plotted; all multi-panel plots also set par(mfrow)=c(1L,1L) upon exit; argument layout has been deprecated from plot.permutest.rma.uni(), plot.vif.rma(), and plot.infl.rma.uni()
the predict.rma() and predict.rma.ls() functions now also accept a matrix as input that includes a column for the intercept term (in which case the intercept argument is ignored and the first column of the matrix controls whether the intercept term is included in calculating the predicted value(s))
added extractor function se() for extracting standard errors from model objects
added function pairmat() to construct a matrix of pairwise contrasts
added function deltamethod() to apply the (multivariate) delta method to a set of estimates
anova() and predict() gain an adjust argument for adjusting p-values / interval bounds for multiple testing
fixed predict() ignoring the level argument for robust.rma objects obtained with clubSandwich=TRUE
print.anova.rma() and print.list.anova.rma() now also print significance stars for some tests (unless getOption("show.signif.stars") is FALSE)
added a collapse argument to the various cumul() functions (to specify whether studies with the same value of the order variable should be added simultaneously)
rma.mv() now counts the number of levels of a random effect more appropriately; this may trigger more often the check that the number of levels is equal to 1, in which case the corresponding variance component is automatically fixed to 0; this check can be omitted with control=list(check.k.gtr.1=FALSE)
made optimizers Rcgmin and Rvmmin available again via the optimx package
when unspecified, argument shade in funnel() now automatically uses a color gradient for the regions when multiple level values are specified
added lim, ci, pi, legend, and flip arguments to labbe()
fsn(..., type="General") now computes the final estimates after rounding the fail-safe N value (not before)
permutest.rma.uni() gains a btt argument and permutest.rma.ls() gains btt and att arguments
selmodel() gains a subset argument (to specify a subset of studies to which the selection function should apply); for the beta selection model, one can now also specify two steps values to fit a truncated beta selection model
nobs() now just returns the number of estimates, not the effective number of observations
some tweaks were made to vcalc() to speed up the calculations (by James Pustejovsky)
added measures "PRZ", "CLES", "AUC", "CLESN", "AUCN", "CLESCN", "AUCCN", "R2F", and "ZR2F" to escalc()
escalc() gains a flip argument
escalc() gains a correct argument (to specify whether a bias correction should be applied)
added transformation function transf.dtoovl() (for transforming standardized mean differences to overlapping coefficient values) and `transf.dtocliffd() (for transforming standardized mean differences to Cliff’s delta values)
qqnorm.rma.uni() now shades the pseudo confidence region; all qqnorm() functions gain a grid argument
better handling of outlist="minimal"
added more tests

the steps argument in the various profile() functions can now also be a numeric vector to specify for which parameter values the likelihood should be evaluated
a few minor fixes to the dynamic theming of plots based on the foreground and background colors of the plotting device
slightly improved flexibility for setting package options
new measures added to escalc(): "SMN" for the single-group standardized mean / single-group standardized mean difference, "SMCRP" for the standardized mean change using raw score standardization with pooled standard deviations, and "SMCRPH" for the standardized mean change using raw score standardization with pooled standard deviations and heteroscedastic population variances at the two measurement occasions
calculation of the sampling variances for measures "SMDH", "SMD1H", and "SMCRH" was slightly adjusted for consistency
in plot.gosh.rma(), can also set het="tau" (to plot the square root of tau^2 as the measure of heterogeneity)
in the various forest() functions, argument ylim can now only be a single value to specify the lower bound (while the upper bound is still set automatically)
in forest() and regplot(), observation limits set via olim are now properly applied to all elements
various internal improvements to selmodel()
selmodel() no longer stops with an error when one or more intervals defined by the steps argument do not contain any observed p-values (instead a warning is issued and model fitting proceeds, but may fail)
added decreasing argument to selmodel() for enforcing that the delta estimates must be a monotonically decreasing function of the p-values in the step function model
added the undocumented argument pval to selmodel() for passing p-values directly to the function (doing this is highly experimental)
some internal refactoring of the code
improved the documentation a bit

added getmfopt() and setmfopt() functions for getting and setting package options and made some of the options more flexible
removed argument weighted from fsn() (whether weighted or unweighted averages are used in Orwin’s method is now simply determined by whether sampling variances are specified or not); added type="General" to fsn() as a generalization of the Orwin and Rosenberg methods (that allows for a fail-safe N calculation based on a random-effects model); can now pass an rma object to the fsn() function
further improved the theming of all plots based on the foreground and background colors; within RStudio, plot colors can also be automatically chosen based on the theme (with setmfopt(theme="auto"))
added additional/optional argument tabfig to the various forest() functions, for easily setting the annosym argument to an appropriate vector for exactly aligning numbers (when using a matching font)
added (for now undocumented) vccon argument to rma.mv() for setting equality constraints on variance/correlation components
replace argument in conv.2x2(), conv.delta(), conv.fivenum(), and conv.wald() can now also be a logical
added summary.matreg() and print.summary.matreg() methods for including additional statistics in the output (R^2 and the omnibus test) and added coef.matreg() and vcov.matreg() extractor functions
formatting functions fmtp(), fmtx(), and fmtt() gain a quote argument, which is set to FALSE by default
for measures "PCOR", "ZPCOR", "SPCOR", and "ZSPCOR", argument mi in escalc() now refers to the total number of predictors in the regression models (i.e., also counting the focal predictor of interest)
added measures "R2" and "ZR2" to escalc()
addpoly.default() and addpoly.rma.predict() gain a constarea argument (for the option to draw the polygons with a constant area)
plot.rma.uni.selmodel() gains a shade argument (for shading the confidence interval region)
plot.permutest.rma.uni() gains a legend argument
vcalc() gains a sparse argument
aggregate.escalc gains var.names argument
made the legend argument more flexible in funnel()
made the append argument more flexible in to.long()
added a few more transformation functions
small bug fixes
added automated visual comparison tests of plots
improved the documentation a bit

improved the various plotting functions so they respect par("fg"); as a result, one can now create plots with a dark background and light plotting colors
also allow two or three values for xlab in the various forest() functions (for adding labels at the ends of the x-axis limits)
better default choices for xlim in the various forest() functions; also, argument ilab.xpos is now optional when using the ilab argument
added shade and colshade arguments to the various forest() functions
the various forest() functions no longer enforce that xlim must be at least as wide as alim
added link argument to rma.glmm()
rma.glmm() with measure="OR", model="CM.EL", method="ML" now treats tau^2 values below 1e-04 effectively as zero before computing the standard errors of the fixed effects; this helps to avoid numerical problems in approximating the Hessian; similarly, selmodel() now treats tau^2 values below 1e-04 or min(vi/10) effectively as zero before computing the standard errors
for measure SMCC, can now specify d-values, t-test statistics, and p-values via arguments di, ti, and pi
functions that issue a warning when omitting studies due to NAs now indicate how many were omitted
properly documented the level argument
added a few more transformation functions
small bug fixes
improved the documentation a bit

added conv.2x2() function for reconstructing the cell frequencies in 2x2 tables based on other summary statistics
added conv.wald() function for converting Wald-type confidence intervals and test statistics to sampling variances
added conv.fivenum() function for estimating means and standard deviations from five-number summary values
added conv.delta() function for transforming observed effect sizes or outcomes and their sampling variances using the delta method
added emmprep() function to create a reference grid for use with the emmeans() function from the package of the same name
exposed formatter functions fmtp(), fmtx(), and fmtt()
package numDeriv moved from Suggests to Depends
model.matrix.rma() gains asdf argument
corrected bug in vcalc() (values for obs and type were taken directly as indices instead of using them as identifiers)
improved efficiency of vif() when sim=TRUE by reshuffling only the data needed in the model matrix; due to some edge cases, the simulation approach cannot be used when some redundant predictors were dropped from the original model; and when redundancies occur after reshuffling the data, the simulated (G)VIF value(s) are now set to Inf instead of NA
selmodel() gains type='trunc' and type='truncest' models (the latter should be considered experimental)
added exact="i" option in permutest() (to just return the number of iterations required for an exact permutation test)
escalc() now provides more informative error messages when not specifying all required arguments to compute a particular measure
added measures "ZPHI", "ZTET", "ZPB", "ZBIS", and "ZSPCOR" to escalc() (but note that Fisher’s r-to-z transformation is not a variance-stabilizing transformation for these measures)
the variance of measure ZPCOR is now calculated with 1/(ni-mi-3) (instead of 1/(ni-mi-1)), which provides a better approximation in small samples (and analogous to how the variance of ZCOR is calculated with 1/(ni-3))
as with measure="SMD", one can now also use arguments di and ti to specify d-values and t-test statistics for measures RPB, RBIS, D2ORN, and D2ORL in escalc()
for measures COR, UCOR, and ZCOR, can now use argument ti to specify t-test statistics in escalc()
can also specify (two-sided) p-values (of the respective t-tests) for these measures (and for measures PCOR, ZPCOR, SPCOR, and ZSPCOR) via argument pi (the sign of the p-value is taken to be the sign of the measure)
can also specify (semi-)partial correlations directly via argument ri for measures PCOR, ZPCOR, SPCOR, and ZSPCOR
when passing a correlation marix to rcalc(), it now orders the elements (columnwise) based on the lower triangular part of the matrix, not the upper one (which is more consistent with what matreg() expects as input when using the V argument)
optimizers Rcgmin and Rvmmin are now available in rma.uni(), rma.mv(), rma.glmm(), and selmodel()
improved the documentation a bit

funnel.default(), funnel.rma(), and regplot.rma() gain slab argument
vif() was completely refactored and gains reestimate, sim, and parallel arguments; added as.data.frame.vif.rma() and plot.vif.rma() methods
plot.permutest.rma.uni() function sets the y-axis limits automatically and in a smarter way when also drawing the reference/null distribution and the density estimate
added possibility to specify a list for btt in anova.rma(); added print.list.anova.rma() to print the resulting object
added as.data.frame.anova.rma() and as.data.frame.list.anova.rma() methods
documented the possibility to use an identity link (with link="identity") in rma.uni() when fitting location-scale models (although this will often lead to estimation problems); added solnp() as an additional optimizer for this case
optimizers nloptr and constrOptim.nl (the latter from the alabama package) are now available in rma.uni() for location-scale models when using an identity link
added measure SMD1H to escalc()
for measure="SMD", escalc() now also allows the user to specify d-values and t-test statistics via arguments di and ti, respectively
aggregate.escalc() gains addk argument
added (experimental!) support for measures "RR", "RD", "PLN", and "PR" to rma.glmm() (but using these measures will often lead to estimation problems)
replmiss() gains data argument
cumul() functions also store data, so that arguments ilab, col, pch, and psize in the forest.cumul.rma() function can look for variables therein
fixed issue with rendering Rmarkdown documents with metafor output due to the use of a zero-width space

added misc-models, misc-recs, and misc-options help pages
added as.data.frame.confint.rma() and as.data.frame.list.confint.rma methods
permutest() can now also do permutation tests for location-scale models; it also always returns the permutation distributions; hence, argument retpermdist was removed
added plot.permutest.rma.uni() function to plot the permutation distributions
simplified regtest(), ranktest(), and tes() to single functions instead of using generics and methods; this way, a data argument could be added
added vcalc() and blsplit() functions
robust() gains clubSandwich argument; if set to TRUE, the methods from the clubSandwich package (https://cran.r-project.org/package=clubSandwich) are used to obtain the cluster-robust results; anova.rma() and predict.rma() updated to work appropriately in this case
results from robust() are no longer printed with print.robust.rma() but with the print methods print.rma.uni() and print.rma.mv()
anova.rma() now gives a warning when running LRTs not based on ML/REML estimation and gains rhs argument; it also now has a refit argument (to refit REML fits with ML in case the fixed effects of the models differ)
setting dfs="contain" in rma.mv() automatically sets test="t" for convenience
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/covariance 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 via the ri argument)
added measure "REH" to escalc() for computing the (log transformed) relative excess heterozygosity (to assess deviations from the Hardy-Weinberg equilibrium)
aggregate.escalc() gains checkpd argument and struct="CS+CAR"
rma.glmm() now has entire array of optimizers available for model="CM.EL" and measure="OR"; switched the default from optim() with method BFGS to nlminb() for consistency with rma.mv(), rma.uni(), and selmodel.rma.uni()
rma.glmm() gains coding and cor arguments and hence more flexibility how the group variable should be coded in the random effects structure and whether the random study effects should be allowed to be correlated with the random group effects
rma.uni() now also provides R^2 for fixed-effects models
matreg() can now also analyze a covariance matrix with a corresponding V matrix; can also specify variable names (instead of indices) for arguments x and y
renamed argument nearPD to nearpd in matreg() (but nearPD continues to work)
plot.profile.rma() gains refline argument
added addpoly.rma.predict() method
addpoly.default() and addpoly.rma() gain lty and annosym arguments; if unspecified, arguments annotate, digits, width, transf, atransf, targs, efac, fonts, cex, and annosym are now automatically set equal to the same values that were used when creating the forest plot
documented textpos and rowadj arguments for the various forest functions and moved the top and annosym arguments to ‘additional arguments’
fixed that level argument in addpoly.rma() did not affect the CI width
points.regplot() function now also redraws the labels (if there were any to begin with)
added lbfgsb3c, subplex, and BBoptim as possible optimizer in rma.mv(), rma.glmm(), rma.uni(), and selmodel.rma.uni()
the object returned by model fitting functions now includes the data frame specified via the data argument; various method functions now automatically look for specified variables within this data frame first
datasets moved to the metadat package (https://cran.r-project.org/package=metadat)
improved the documentation a bit

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

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

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

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

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://app.codecov.io/gh/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)

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

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)

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

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 …

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?)

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)

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)

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

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

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

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

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

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

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

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

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())

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

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

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)

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)

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

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

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

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

made some small changes to the documentation (some typos corrected, some confusing points clarified)

first version released on CRAN

metafor 4.7-55 (2024-11-22)

metafor 4.6-0 (2024-03-28)2024-03-28

metafor 4.4-0 (2023-09-27)2023-09-27

metafor 4.2-0 (2023-05-08)2023-05-08

metafor 4.0-0 (2023-03-19)2023-03-19

metafor 3.8-1 (2022-08-26)2022-08-26

metafor 3.4-0 (2022-04-21)2022-04-21

metafor 3.0-2 (2021-06-09)2021-06-09

metafor 2.4-0 (2020-03-19)2020-03-19

metafor 2.1-0 (2019-05-13)2019-05-14

metafor 2.0-0 (2017-06-22)2017-06-22

metafor 1.9-9 (2016-09-25)2016-09-25

metafor 1.9-8 (2015-09-28)2015-09-28

metafor 1.9-7 (2015-05-22)2015-05-22

metafor 1.9-6 (2015-05-07)2015-05-07

metafor 1.9-5 (2014-11-24)2014-11-24

metafor 1.9-4 (2014-07-30)2014-07-30

metafor 1.9-3 (2014-05-05)2014-05-05

metafor 1.9-2 (2013-10-07)2013-10-07

metafor 1.9-1 (2013-07-20)2013-07-20

metafor 1.9-0 (2013-06-21)2013-06-21

metafor 1.8-0 (2013-04-11)2013-04-11

metafor 1.7-0 (2013-02-06)2013-02-06

metafor 1.6-0 (2011-04-13)2011-04-13

metafor 1.5-0 (2010-12-16)2010-12-16

metafor 1.4-0 (2010-07-30)2010-07-30

metafor 1.3-0 (2010-06-25)2010-06-25

metafor 1.2-0 (2010-05-18)

metafor 1.1-0 (2010-04-28)2010-04-28

metafor 1.0-1 (2010-02-02)2010-02-02

metafor 0.5-7 (2009-12-06)2009-12-06

metafor 0.5-6 (2009-10-19)

metafor 0.5-5 (2009-10-08)2009-10-08

metafor 0.5-4 (2009-09-18)2009-09-18

metafor 0.5-3 (2009-07-31)2009-07-31

metafor 0.5-2 (2009-07-06)

metafor 0.5-1 (2009-06-14)

metafor 0.5-0 (2009-06-05)2009-06-05