Release to CRAN 1.4

DESCRIPTION

@@ -2,7 +2,7 @@ Package: metaSEM
 Type: Package
 Title: Meta-Analysis using Structural Equation Modeling
 Version: 1.4.0
-Date: 2024-04-08
+Date: 2024-05-15
 Depends: R (>= 3.4.0), OpenMx
 Imports: Matrix, MASS, ellipse, graphics, stats, utils, mvtnorm, numDeriv, lavaan
 Suggests: metafor, semPlot, R.rsp, testthat, matrixcalc

NEWS

@@ -1,10 +1,12 @@
-Release 1.4.0 (Apr 8, 2024)
+Release 1.4.0 (May 15, 2024)
 ====================================
+* Release to CRAN.
 * Revise create.mxModel().
 * Replace dontrun with donttest.
 
 Release 1.3.1 (Aug 8, 2023)
 ====================================
+* Release to CRAN.
 * Lower the tolerance in testing asyCov(), which returns an error in latest R.
 
 Release 1.3.0 (Jan 7, 2023)

R/create.mxModel.R

@@ -173,22 +173,25 @@ create.mxModel <- function(model.name="mxModel", RAM=NULL, data=NULL,
  }
  }
 
- if (run==FALSE) return(mx.model)
+ if (run) {
 
- ## Default is z
- mx.fit <- tryCatch(mxRun(mx.model, intervals=(intervals.type=="LB"),
- suppressWarnings=TRUE, silent=TRUE, ...),
- error=function(e) e)
-
- ## Check if any errors
- if (inherits(mx.fit, "error")) {
- mx.fit <- mxTryHard(mx.model, extraTries=50, intervals=FALSE, silent=TRUE) 
- mx.fit <- tryCatch(mxRun(mx.fit, intervals=(intervals.type=="LB"),
+ ## Default is z
+ mx.fit <- tryCatch(mxRun(mx.model, intervals=(intervals.type=="LB"),
  suppressWarnings=TRUE, silent=TRUE, ...),
  error=function(e) e)
- if (inherits(mx.fit, "error")) { 
- warning("Error in running mxModel.\n")
+
+ ## Check if any errors
+ if (inherits(mx.fit, "error")) {
+ mx.fit <- mxTryHard(mx.model, extraTries=50, intervals=FALSE, silent=TRUE) 
+ mx.fit <- tryCatch(mxRun(mx.fit, intervals=(intervals.type=="LB"),
+ suppressWarnings=TRUE, silent=TRUE, ...),
+ error=function(e) e)
+ if (inherits(mx.fit, "error")) { 
+ warning("Error in running mxModel.\n")
+ }
  }
+ } else {
+ mx.fit <- mx.model
  }
 
  out <- list(mx.fit=mx.fit, RAM=RAM, data=data, mxalgebras=mxalgebras.ci,

R/meta2semPlot.R

@@ -161,8 +161,8 @@ plot.osmasem <- function(x, manNames=NULL, latNames=NULL, labels=c("labels", "RA
 
  labels <- match.arg(labels)
  if (labels=="labels") {
- A.labels <- x$mx.model$A0$labels
- S.labels <- x$mx.model$S0$labels
+ A.labels <- x$mx.fit$A0$labels
+ S.labels <- x$mx.fit$S0$labels
  labels <- c(c(A.labels), c(S.labels))
  row.pars <- as.numeric(row.names(out@Pars))
  out@Pars$label <- labels[row.pars]

R/osmasem.R

@@ -512,14 +512,13 @@ osmasem <- function(model.name="osmasem", RAM=NULL, Mmatrix=NULL,
  ## Assign same starting values, e.g., A and S matrices are different from those in provided in Jmatrix
  mx.model <- omxAssignFirstParameters(mx.model)
 
- ## Return mx model without running the analysis
- if (run==FALSE) {
- return(mx.model)
- } else { 
+ if (run) { 
  mx.fit <- tryCatch(do.call(mxRun, c(list(mx.model, intervals=intervals,
  suppressWarnings=suppressWarnings,
  silent=silent),
  mxRun.Args)), error = function(e) e)
+ } else {
+ mx.fit <- mx.model
  }
 
  # try to run it with error message as output
@@ -538,7 +537,8 @@ osmasem <- function(model.name="osmasem", RAM=NULL, Mmatrix=NULL,
  mxRun.Args=mxRun.Args,
  subset.variables=subset.variables,
  subset.rows=subset.rows,
- mx.model=mx.model, mx.fit=mx.fit)
+ #mx.model=mx.model,
+ mx.fit=mx.fit)
  class(out) <- 'osmasem'
  out
 }

man/Cheung09.Rd

@@ -16,8 +16,6 @@ A list of data with the following structure:
  \item{n}{A vector of sample sizes}
 }
 }
-\source{Cheung, M. W.-L. (2009). TSSEM: A LISREL syntax generator for two-stage structural equation modeling (Version 1.11) [Computer software]. Retrieved from \url{http://courses.nus.edu.sg/course/psycwlm/internet/tssem.zip}.
-}
 \references{
  Cheung, M. W.-L., & Chan, W. (2005). Meta-analytic structural equation modeling: A two-stage approach. \emph{Psychological Methods}, \bold{10}, 40-64.
 

man/metaSEM-package.Rd

@@ -15,7 +15,7 @@
 Package: \tab metaSEM\cr
 Type: \tab Package\cr
 Version: \tab 1.4.0\cr
-Date: \tab 2024-04-08\cr
+Date: \tab 2024-05-15\cr
 License: \tab GPL (>=2)\cr
 LazyLoad: \tab yes\cr
 }

man/tssem1.Rd

@@ -41,8 +41,8 @@ tssem1REM(Cov, n, cor.analysis=TRUE, RE.type=c("Diag", "Symm", "Zero","User"),
  }
  \item{RE.type}{Either \code{"Diag"}, \code{"Symm"}, 
  \code{"Zero"} or \code{"User"}. If it is
- \code{"Diag"}, a diagonal matrix is used for the random effects
- meaning that the random effects are independent. If it is \code{"Symm"} (default if missing), a
+ \code{"Diag"} (default if missing), a diagonal matrix is used for the random effects
+ meaning that the random effects are independent. If it is \code{"Symm"}, a
  symmetric matrix is used for the random effects on the covariances
  among the correlation (or covariance) vectors. If it is
  \code{"Zero"}, there is no random effects which is similar to the