updates

### Table of Contents

## Package Updates

On this page, you can find a description of the (recent) updates to the metafor package. Older updates are archived here.

### Version 3.8-1 (2022-08-26)

`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

### Version 3.4-0 (2022-04-21)

- 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

### Version 3.0-2 (2021-06-02)

- 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 23 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 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

### 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 the argument 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

### 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

### 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://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 Röver 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)

### Version 1.9-8 (2015-05-28)

- 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

### Older Versions

Older updates are archived here.

updates.txt · Last modified: 2022/08/27 16:13 by Wolfgang Viechtbauer