Changes in Version 1.8-0 (2013-04-11)
=====================================

   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 Kösters
	 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 estimates 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 estimate is also
     now provided when tau^2 is prespecified

   o rma.uni() and rma.glmm() now use a better method to check whether the
     design 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 some improvements to the documentation


Changes in Version 1.7-0 (2013-02-05)
=====================================

   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 addded 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 design 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 design matrix (which may be much smaller
     than the total number of permutations), making the exact permutation
     test feasible in a larger set of circumstances (thanks to John Hodgson
     for making me aware of this issue and to Hans-Jörg Viechtbauer for
     coming up with a recursive algorithm for finding the unique
     permutations)

   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


Changes in Version 1.6-0 (2011-04-12)
=====================================

   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


Changes in Version 1.5-0 (2010-12-15)
=====================================

   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


Changes in Version 1.4-0 (2010-07-30)
=====================================

   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


Changes in Version 1.3-0 (2010-06-25)
=====================================

   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


Changes in Version 1.2-0 (2010-05-18)
=====================================

   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())


Changes in Version 1.1-0 (2010-04-28)
=====================================

   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


Changes in Version 1.0-1 (2010-01-28)
=====================================

   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 design 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


Changes in Version 0.5-7 (2009-12-06)
=====================================

   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)


Changes in Version 0.5-6 (2009-10-19)
=====================================

   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)


Changes in Version 0.5-5 (2009-10-08)
=====================================

   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


Changes in Version 0.5-4 (2009-09-18)
=====================================

   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


Changes in Version 0.5-3 (2009-07-31)
=====================================

   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


Changes in Version 0.5-2 (2009-07-06)
=====================================

   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


Changes in Version 0.5-1 (2009-06-14)
=====================================

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


Changes in Version 0.5-0 (2009-06-14)
=====================================

   o first version released on CRAN
