A comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbe, Baujat, bubble, and GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted. An introduction to the package can be found in Viechtbauer (2010)

The `metafor`

package is a comprehensive collection of functions for conducting meta-analyses in R. The package includes functions to calculate various effect sizes or outcome measures, fit fixed-, random-, and mixed-effects models to such data, carry out moderator and meta-regression analyses, and create various types of meta-analytical plots (e.g., forest, funnel, radial, L'Abbé, Baujat, GOSH plots). For meta-analyses of binomial and person-time data, the package also provides functions that implement specialized methods, including the Mantel-Haenszel method, Peto's method, and a variety of suitable generalized linear (mixed-effects) models (i.e., mixed-effects logistic and Poisson regression models). Finally, the package provides functionality for fitting meta-analytic multivariate/multilevel models that account for non-independent sampling errors and/or true effects (e.g., due to the inclusion of multiple treatment studies, multiple endpoints, or other forms of clustering). Network meta-analyses and meta-analyses accounting for known correlation structures (e.g., due to phylogenetic relatedness) can also be conducted.

The `metafor`

package website can be found at http://www.metafor-project.org. On the website, you can find:

- some news concerning the package and/or its development,
- a more detailed description of the package features,
- a log of the package updates that have been made over the years,
- a to-do list and a description of planned features to be implemented in the future,
- information on how to download and install the package,
- information on how to obtain documentation and help with using the package,
- some analysis examples that illustrate various models, methods, and techniques,
- a little showcase of plots and figures that can be created with the package,
- some tips and notes that may be useful when working with the package,
- a list of people that have in some shape or form contributed to the development of the package,
- an (incomplete) list of articles that have used the package as part of the analyses,
- a frequently asked questions section, and
- some links to other websites related to software for meta-analysis.

The current official (i.e., CRAN) release can be installed directly within R with:

`install.packages("metafor")`

After installing the devtools package with `install.packages("devtools")`

, the development version of the metafor package can be installed with:

`library("devtools")install_github("wviechtb/metafor")`

This approach builds the package from source based on the development branch on GitHub.

The metafor package was written by Wolfgang Viechtbauer. It is licensed under the GNU General Public License Version 2. For citation info, type `citation(package='metafor')`

in R. To report any issues or bugs, please go here.

o added simulate() method for 'rma' objects; added MASS to 'Suggests' (since simulating for 'rma.mv' objects requires mvrnorm() from MASS)

o cooks.distance.rma.mv() now works properly even when there are missing values in the data

o residuals() gains 'type' argument and can compute Pearson residuals

o 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

o added ranef.rma.mv() for extracting the BLUPs of the random effects for 'rma.mv' models

o all functions that repeatedly refit models now have the option to show a progress bar

o added ranktest.default(), so user can now pass the outcomes and corresponding sampling variances directly to the function

o added regtest.default(), so user can now pass the outcomes and corresponding sampling variances directly to the function

o funnel.default() gains 'subset' argument

o funnel.default() and funnel.rma() gain 'col' and 'bg' arguments

o plot.profile.rma() gains 'ylab' argument

o more consistent handling of 'robust.rma' objects

o added location-scale model

o added a print method for 'rma.gosh' objects

o the (log) relative risk is now called the (log) risk ratio in all help files, plots, code, and comments

o escalc() can now compute outcome measures based on paired binary data ("MPRR", "MPOR", "MPRD", "MPORC", and "MPPETO")

o escalc() can now compute (semi-)partial correlation coefficients ("PCOR", "ZPCOR", "SPCOR")

o 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

o escalc() can now compute the sampling variance for measure="PHI" for studies using stratified sampling (vtpye="ST")

o 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

o added rbind() method for 'escalc' objects

o added as.data.frame() method for 'list.rma' objects

o added a new dataset (dat.pagliaro1992) for another illustration of a network meta-analysis

o added a new dataset (dat.laopaiboon2015) on the effectiveness of azithromycin for treating lower respiratory tract infections

o rma.uni() and rma.mv() now check if the ratio of the largest to smallest sampling variance is extreme large; results may not be stable then (and very large ratios typically indicate wrongly coded data)

o model fitting functions now check if extra/superfluous arguments are specified via ... and issues are warning if so

o instead of defining own generic ranef(), import ranef() from 'nlme'

o improved output formatting

o added more tests (but disabled a few tests on CRAN to avoid some issues when R is compiled with --disable-long-double)

o some general code cleanup

o renamed diagram_metafor.pdf vignette to just diagram.pdf

o minor updates in the documentation

o started to use git as version control system, GitHub to host the repository (https://github.com/wviechtb/metafor) for the development version of the package, Travis CI as continuous integration service (https://travis-ci.org/wviechtb/metafor), and Codecov for automated code coverage reporting (https://codecov.io/github/wviechtb/metafor)

o 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

o 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)

o 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 R�ver for the suggestion)

o escalc() can now also be used to convert a regular data frame to an 'escalc' object

o 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

o 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

o fixed an issue with robust.rma.mv() throwing errors when the model was fitted with sparse=TRUE

o fixed an error with robust.rma.mv() when the model was fitted with user-defined weights (or a user-defined weight matrix)

o added ranef() for extracting the BLUPs of the random effects (only for 'rma.uni' objects at the moment)

o 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)

o 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)

o rma.glmm() continues to work even when the saturated model cannot be fitted (although the tests for heterogeneity are not available then)

o 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"

o 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)

o 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

o the annotations created by the various forest() and addpoly() functions are now a bit more compact by default

o more flexible 'efac' argument in the various forest() functions

o 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

o 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

o added plot.profile.rma(), a plot method for objects returned by the profile.rma.uni() and profile.rma.mv() functions

o 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'

o baujat.rma() gains argument 'symbol' for more control over the plotting symbol

o labbe() gains a 'grid' argument

o more logical placement of labels in qqnorm.rma.uni(), qqnorm.rma.mh(), and qqnorm.rma.peto() functions (and more control thereof)

o qqnorm.rma.uni() gains 'lty' argument

o added gosh.rma() and plot.gosh.rma() for creating GOSH (i.e., graphical display of study heterogeneity) plots based on Olkin et al. (2012)

o 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

o updated datasets so those containing precomputed effect size estimates or observed outcomes are already declared to be 'escalc' objects

o added new datasets (dat.egger2001 and dat.li2007) on the effectiveness of intravenous magnesium in acute myocardial infarction

o 'methods' package is now under 'Depends' (in addition to 'Matrix'), so that rma.mv(..., sparse=TRUE) always works, even under Rscript

o some general code cleanup

o added more tests (and used a more consistent naming scheme for tests)

o 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

o added graphics, grDevices, and methods to Imports (due to recent change in how CRAN checks packages)

o 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

o 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)

o 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

o 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)

o 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

o multiple model objects can now be passed to the fitstats(), AIC(), and BIC() functions

o check for duplicates in the 'slab' argument is now done *after* any
subsetting is done (as suggested by Michael Dewey)

o 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

o 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

o 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)

o for rma.glmm(), BIC and AICc values were switched around; corrected

o 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)

o some (overdue) updates to the documentation

o 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")

o 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))

o changed a few is_identical_to() comparisons to expect_equivalent() ones (that failed on Sparc Solaris)

o funnel() now works again for 'rma.glmm' objects (note to self: quit breaking things that work!)

o 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)

o 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

o 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"))

o 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)

o 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

o 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

o 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

o 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

o the 'efac' argument for forest.rma() now also allows two values, the first for the arrows and CI limits, the second for summary estimates

o corrected some axis labels in various plots when measure="PLO"

o axes in labbe() plots now have "(Group 1)" and "(Group 2)" added by default

o anova.rma() gains argument 'L' for specifying linear combinations of the coefficients in the model that should be tested to be zero

o 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

o 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)

o 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)

o 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

o 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

o 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)

o element 'fill' returned by trimfill.rma.uni() is now a logical vector (instead of a 0/1 dummy variable)

o print.list.rma() now also returns the printed results invisibly as a data frame

o added a new dataset (dat.senn2013) as another illustration of a network meta-analysis

o metafor now depends on at least version 3.1.0 of R

o 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'

o added update.rma() function (for updating/refitting a model); model objects also now store and keep the call

o 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')

o 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)'

o 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

o anova.rma.uni() function changed to anova.rma() that works now for both 'rma.uni' and 'rma.mv' objects

o 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

o 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)

o 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

o weights.rma.uni() now shows the correct weights when weighted=FALSE

o argument 'showweight' renamed to 'showweights' in the forest.default() and forest.rma() functions (more consistent with the naming of the various weights() functions)

o added model.matrix.rma() function (to extract the model matrix from objects of class 'rma')

o 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)

o 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

o corrected some missing/redundant row/column labels in some output

o most require() calls replaced with requireNamespace() to avoid altering the search path (hopefully this won't break stuff ...)

o some non-visible changes including more use of some (non-exported) helper functions for common tasks

o dataset dat.collins91985a updated (including all reported outcomes and some more information about the various trials)

o oh, and guess what? I updated the documentation ...

o 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)

o 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

o slight improvements in how the rma.uni() function handles non-positive sampling variances

o 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

o 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

o 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

o added "sqrtni" and "sqrtninv" as possible options for the 'predictor' argument of regtest()

o 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)

o 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 credibility/prediction intervals are not computed)

o 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)

o 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

o 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)

o added two new datasets (dat.bourassa1996, dat.riley2003)

o added function replmiss() (just a useful helper function)

o package now uses LazyData: TRUE

o some improvements to the documentation (do I still need to mention this every time?)

o some minor tweaks to rma.uni() that should be user transparent

o 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

o better handling of mismatched length of yi and ni vectors in rma.uni() and rma.mv() functions

o 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

o some general tweaks to rma.mv() that should make it a bit faster

o 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

o rma.mv() now has a new argument 'W' allowing the user to specify arbitrary user-defined weights or an arbitrary weight matrix

o 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)

o 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)

o 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)

o 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)

o 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)

o 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)

o 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

o 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

o 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 credibility/prediction intervals)

o 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 credibility interval

o 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

o 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)

o 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

o the various forest() functions now have a new argument, clim, to set limits for the confidence/credibility/prediction interval bounds

o cumul.mh() and cumul.peto() now get the order of the studies right when there are missing values in the data

o 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

o 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

o 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

o slight improvement to profile.rma.mv() function, which would throw an error if the last model fit did not converge

o added a new dataset (dat.linde2005) for replication of the analyses in Viechtbauer (2007)

o added a new dataset (dat.molloy2014) for illustrating the meta-analysis of (r-to-z transformed) correlation coefficients

o added a new dataset (dat.gibson2002) to illustrate the combined analysis of standardized mean differences and probit transformed risk differences

o computations in weights.mh() slightly changed to prevent integer overflows for large counts

o unnecessary warnings in transf.ipft.hm() are now suppressed (cases that raised those warnings were already handled correctly)

o in predict(), blup(), cumul(), and leave1out(), when using the 'transf' argument, the standard errors (which are NA) are no longer shown in the output

o argument 'slab' in various functions will now also accept non-unique study labels; make.unique() is used as needed to make them unique

o 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)

o 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)

o summary.escalc() has a new argument 'H0' to specify the value of the outcome under the null hypothesis for computing the test statistics

o 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

o 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

o 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)

o 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)

o rma.glmm() should not throw an error anymore when including only a single moderator/predictor in the model

o predict.rma() now returns an object of class 'list.rma' (therefore, function print.predict.rma() has been removed)

o for 'rma.list' objects, added `[`

, head(), and tail() methods

o automated testing using the 'testthat' package (still many more tests to add, but finally made a start on this)

o encoding changed to UTF-8 (to use 'foreign characters' in the docs and to make the HTML help files look a bit nicer)

o 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)

o 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

o added the AICc to the information criteria computed by the various model fitting functions

o 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)

o rma.uni(), rma.glmm(), and rma.mv() now use a more stringent check whether the model matrix is of full rank

o 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)

o 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)

o 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

o 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)

o 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)

o rma.mh() and rma.peto() also now have a 'verbose' argument (not really needed, but added for sake of consistency across functions)

o 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!)

o a bit of code reorganization (should be user transparent)

o vignettes ("metafor" and "metafor_diagram") are now just 'other files' in the doc directory (as these were not true vignettes to begin with)

o some improvements to the documentation (as always)

o rma.mh() now also implements the Mantel-Haenszel method for incidence rate differences (measure="IRD")

o when analyzing incidence rate ratios (measure="IRR") with the rma.mh() function, the Mantel-Haenszel test for person-time data is now also provided

o 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

o renamed elements 'CMH' and 'CMHp' (for the Cochran-Mantel-Haenszel test statistic and corresponding p-value) to 'MH' and 'MHp'

o added function baujat() to create Baujat plots

o added a new dataset (dat.pignon2000) to illustrate the use of the baujat() function

o added function to.table() to convert data from vector format into the corresponding table format

o added function to.long() to convert data from vector format into the corresponding long format

o rma.glmm() now even runs when k=1 (yielding trivial results)

o 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

o in rma.glmm(), default number of quadrature points (nAGQ) is now 7 (setting this to 100 was a bit overkill)

o a few more error checks here and there for misspecified arguments

o some improvements to the documentation

o vignette renamed to 'metafor' so vignette("metafor") works now

o 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")

o 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

o 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)

o component 'VAF' is now called 'R2' in anova.rma.uni() function

o 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

o added new dataset (dat.lee2004), which was used in the article by Henmi and Copas (2010) to illustrate their method

o fixed missing x-axis labels in the forest() functions

o 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

o trimfill.rma.uni() now also implements the "Q0" estimator (although the "L0" and "R0" estimators are generally to be preferred)

o 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

o added new dataset (dat.bangertdrowns2004)

o 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)

o summary.escalc() now computes confidence intervals correctly when using the 'transf' argument

o computation of Cochran-Mantel-Haenszel statistic in rma.mh() changed slightly to avoid integer overflow with very big counts

o some internal improvements with respect to object attributes that were getting discarded when subsetting

o some general code cleanup

o some improvements to the documentation

o 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 K�sters for bringing this to my attention)

o sampling variance for the biserial correlation coefficient ("RBIS") is now calculated in a slightly more accurate way

o llplot() now properly scales the log-likelihoods

o 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

o labeling of the axes in labbe() plots is now correct for odds ratios (and transformations thereof)

o added two new datasets (dat.nielweise2007 and dat.nielweise2008) to illustrate some methods/models from the rma.glmm() function

o added a new dataset (dat.yusuf1985) to illustrate the use of rma.peto()

o test for heterogeneity is now conducted by the rma.peto() function exactly as described by Yusuf et al. (1985)

o 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)

o 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

o rma.uni() and rma.glmm() now use a better method to check whether the model matrix is of full rank

o 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

o 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

o slight changes in wording in the output produced by the print.rma.uni() and print.rma.glmm() functions

o some improvements to the documentation

o 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)

o added several methods functions for objects of class 'rma.glmm' (not all methods yet implemented; to be completed in the future)

o 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)

o 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)

o 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)

o rma.uni() now has a 'verbose' argument

o 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

o the escalc() function now does some more extensive error checking for misspecified data and some unusual cases

o 'append' argument is now TRUE by default in the escalc() function

o objects generated by the escalc() function now have their own class

o added print() and summary() methods for objects of class 'escalc'

o added `[`

and cbind() methods for objects of class 'escalc'

o added a few additional arguments to the escalc() function (i.e., slab, subset, var.names, replace, digits)

o added 'drop00' argument to the escalc(), rma.uni(), rma.mh(), and rma.peto() functions

o 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

o 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

o 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)

o 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

o added "PBIT" and "OR2D" measures to the escalc() and rma.uni() functions to compute the standardized mean difference based on 2x2 table data

o added the "D2OR" measure to the escalc() and rma.uni() functions to compute the log odds ratio based on the standardized mean difference

o added "SMDH" measure to the escalc() and rma.uni() functions to compute the standardized mean difference without assuming equal population variances

o 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)

o corrected a small mistake in the equation used to compute the sampling variance of the phi coefficient (measure="PHI") in the escalc() function

o 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-J�rg Viechtbauer for coming up with a recursive algorithm for finding the unique permutations)

o credibility 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

o 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

o 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()

o added switch so that regtest() can also provide the full results from the fitted model (thanks to Michael Dewey for the suggestion)

o weights.rma.mh() now shows the weights in % as intended (thanks to Gavin Stewart for pointing out this error)

o more flexible handling of the 'digits' argument in the various forest functions

o 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)

o 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)

o 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

o better handling of missing values in some unusual situations

o 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)

o 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

o added proper print() method for objects generated by the confint.rma.uni(), confint.mh(), and confint.peto() functions

o when 'transf' or 'atransf' argument was a monotonically *decreasing*
function, then confidence, prediction, and credibility interval bounds
were in reversed order; various functions now check for this and order
the bounds correctly

o trimfill.rma.uni() now only prints information about the number of imputed studies when actually printing the model object

o 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

o 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

o the restricted maximum likelihood (REML) is now calculated using the full likelihood equation (without leaving out additive constants)

o 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)

o 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

o but dat.bcg has been kept as an alias for dat.colditz1994, as it has been referenced under that name in some publications

o added new dataset (dat.pritz1997) to illustrate the meta-analysis of proportions (raw values and transformations thereof)

o added new dataset (dat.bonett2010) to illustrate the meta-analysis of Cronbach's alpha values (raw values and transformations thereof)

o added new datasets (dat.hackshaw1998, dat.raudenbush1985)

o (approximate) standard error of the tau^2 estimate is now computed and shown for most of the (residual) heterogeneity estimators

o added nobs() and df.residual() methods for objects of class 'rma'

o metafor.news() is now simply a wrapper for news(package="metafor")

o the package code is now byte-compiled, which yields some modest increases in execution speed

o some general code cleanup

o the 'metafor' package no longer depends on the 'nlme' package

o some improvements to the documentation

o trimfill.rma.uni() now returns a proper object even when the number of missing studies is estimated to be zero

o added the (log transformed) ratio of means as a possible outcome measure to the escalc() and rma.uni() functions (measure="ROM")

o added new dataset (dat.co2) to illustrate the use of the ratio of means outcome measure

o some additional error checking in the various forest functions (especially when using the 'ilab' argument)

o in labbe.rma(), the solid and dashed lines are now drawn behind (and not on top of) the points

o slight change to transf.ipft.hm() so that missing values in 'targs$ni' are ignored

o some improvements to the documentation

o the 'metafor' package now has its own project website at: http://www.metafor-project.org/

o added labbe() function to create L�Abbe plots

o 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)

o 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")

o added the incidence rate ratio as a possible outcome measure to the rma.mh() function

o added transformation functions related to incidence rates

o added the Freeman-Tukey double arcsine transformation and its inverse to the transformation functions

o added some additional error checking for out-of-range p-values in the permutest.rma.uni() function

o added some additional checking for out-of-range values in several transformation functions

o added confint() methods for 'rma.mh' and 'rma.peto' objects (only for completeness sake; print already provides CIs)

o added new datasets (dat.warfarin, dat.los, dat.empint)

o some improvements to the documentation

o a papar about the package has now been published in the Journal of Statistical Software (http://www.jstatsoft.org/v36/i03/)

o added citation info; see: citation("metafor")

o the 'metafor' package now depends on the 'nlme' package

o added extractor functions for the AIC, BIC, and deviance

o some updates to the documentation

o the 'metafor' package now depends on the 'Formula' package

o made escalc() generic and implemented a default and a formula interface

o added the (inverse) arcsine transformation to the set of transformation functions

o cases where k is very small (e.g., k equal to 1 or 2) are now handled more gracefully

o 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)

o 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

o 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

o 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

o 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

o added 'showweight' argument to the forest.default() and forest.rma() functions

o cumul() functions not showing all of the output columns when using fixed-effects models has been corrected

o weights.rma.uni() function now handles NAs appropriately

o weights.rma.mh() and weights.rma.peto() functions added

o logLik.rma() function now behaves more like other logLik() functions (such as logLik.lm() and logLik.lme())

o cint() generic removed and replaced with confint() method for objects of class 'rma.uni'

o slightly improved the code to set the x-axis title in the forest() and funnel() functions

o added coef() method for 'permutest.rma.uni' objects

o added 'append' argument to escalc() function

o 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)

o 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)

o added 'retpermdist' argument to permutest.rma.uni() to return the permutation distributions of the test statistics

o slight improvements to the various transformation functions to cope better with some extreme cases

o 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)

o changed the default number of iterations for the ML, REML, and EB estimators from 50 to 100

o version jump in conjunction with the upcoming publication of a paper in the Journal of Statistical Software describing the 'metafor' package

o 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)

o permutest() function now allows exact permutation tests (but this is only feasible when k is not too large)

o 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)

o forest() function can now also show the credibility interval as a dashed line for a random-effects model

o 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

o funnel() function now has more arguments (e.g., atransf, at) providing more control over the display of the x-axis

o 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)

o functions now properly handle the na.action "na.pass" (treated essentially like "na.exclude")

o added method for weights() to extract the weights used when fitting models with rma.uni()

o some small improvements to the documentation

o added permutest() function for permutation tests

o 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)

o added some checks for values below machine precision

o a bit of code reorganization (nothing that affects how the functions work)

o 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

o added option to the plot function for objects returned by influence() to allow plotting the covariance ratios on a log scale (now the default)

o slight adjustments to various print() functions (to catch some errors when certain values were NA)

o 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)

o 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

o 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)

o added cumul() function for cumulative meta-analyses (with a corresponding forest() method to plot the cumulative results)

o added leave1out() function for leave-one-out diagnostics

o 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

o some internal changes to the class and methods names

o some small corrections to the documentation

o corrected the trimfill() function

o improvements to various print functions

o added a regtest() function for various regression tests of funnel plot asymmetry (e.g., Egger's regression test)

o made ranktest() generic and added a method for objects of class 'rma' so that the test can be carried out after fitting

o added anova() function for full vs reduced model comparisons via fit statistics and likelihood ratio tests

o added the Orwin and Rosenberg approaches to fsn()

o added H^2 measure to the output for random-effects models

o in escalc(), measure="COR" is now used for the (usual) raw correlation coefficient and measure="UCOR" for the bias corrected correlation coefficients

o some small corrections to the documentation

o small changes to some of the examples

o 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

o added an option in plot.infl.rma.uni() to open a new device for plotting the DFBETAS values

o 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

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

o first version released on CRAN