(source cleanup:) remove trailing whitespace at the end of a line

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: lavaan
 Title: Latent Variable Analysis
-Version: 0.6-2.1265
+Version: 0.6-2.1268
 Authors@R: c(person(given = "Yves", family = "Rosseel",
                     role  = c("aut", "cre"),
                     email = "Yves.Rosseel@UGent.be"),

R/00class.R

@@ -1,5 +1,5 @@
 # class definitions
-# 
+#
 # initial version: YR 25/03/2009
 # added ModelSyntax: YR 02/08/2010
 # deleted ModelSyntax: YR 01/11/2010 (using flattened model syntax now)
@@ -50,7 +50,7 @@ setClass("lavSampleStats",         # sample moments
         res.var="list",            # residual variances
         res.th="list",             # residual thresholds
         res.th.nox="list",         # residual thresholds ignoring x
-        res.slopes="list",         # slopes exo (if conditional.x) 
+        res.slopes="list",         # slopes exo (if conditional.x)
         res.int="list",            # intercepts (if conditional.x)
 
         mean.x="list",             # mean exo
@@ -191,7 +191,7 @@ setClass("lavaan",
         SampleStats = "lavSampleStats",  # sample statistics
         Model       = "lavModel",        # internal matrix representation
         Cache       = "list",            # housekeeping stuff
-        Fit         = "Fit",             # fitted results 
+        Fit         = "Fit",             # fitted results
         boot        = "list",            # bootstrap results
         optim       = "list",            # optimizer results
         loglik      = "list",            # loglik values and info
@@ -201,7 +201,7 @@ setClass("lavaan",
         h1          = "list",            # unrestricted model results
         baseline    = "list",            # baseline model results
         external    = "list"             # optional slot, for add-on packages
-    ) 
+    )
 )
 
 setClass("lavaanList",
@@ -234,4 +234,4 @@ setClass("lavaanList",
 
 
 
-                      
+

R/00generic.R

@@ -1,9 +1,9 @@
 # for blavaan
-setGeneric("fitMeasures", 
-    function(object, fit.measures = "all", baseline.model = NULL) 
+setGeneric("fitMeasures",
+    function(object, fit.measures = "all", baseline.model = NULL)
     standardGeneric("fitMeasures"))
 setGeneric("fitmeasures",
-    function(object, fit.measures = "all", baseline.model = NULL) 
+    function(object, fit.measures = "all", baseline.model = NULL)
     standardGeneric("fitmeasures"))
 
 

R/01RefClass_00lavRefModel.R

@@ -1,7 +1,7 @@
 # generic statistical model -- YR 10 july 2012
 
 
-# super class -- virtual statistical model 
+# super class -- virtual statistical model
 lavRefModel <- setRefClass("lavRefModel",
 
 # fields

R/01RefClass_01lavOptim.R

@@ -32,15 +32,15 @@ minGradient = function(x) {
 minHessian = function(x) {
     cat("this is dummy a function [minHessian]\n")
     return(matrix(as.numeric(NA), npar, npar))
-}, 
+},
 
 optimize = function(method = "nlminb", control = list(), verbose = FALSE,
                     start.values = NULL) {
     method <- tolower(method)
     hessian <- FALSE
     if( method == "none" ) {
         .self$optim.method <- "none"
-    } else if( method %in% c("nlminb", "quasi-newton", "quasi.newton", 
+    } else if( method %in% c("nlminb", "quasi-newton", "quasi.newton",
                       "nlminb.hessian") ) {
         .self$optim.method <- "nlminb"
         if(verbose)
@@ -59,7 +59,7 @@ optimize = function(method = "nlminb", control = list(), verbose = FALSE,
         .self$optim.control <- control.nlminb[c("eval.max", "iter.max", "trace",
                                            "abs.tol", "rel.tol", "x.tol",
                                            "step.min")]
-        
+
     } else if( method %in% c("newton", "newton-raphson", "newton.raphson") ) {
         .self$optim.method <- "newton"
         if(verbose)
@@ -76,7 +76,7 @@ optimize = function(method = "nlminb", control = list(), verbose = FALSE,
     }
 
     # user provided starting values?
-    if(!is.null(start.values)) { 
+    if(!is.null(start.values)) {
         stopifnot(length(start.values) == npar)
         .self$theta.start <- start.values
     }
@@ -94,14 +94,14 @@ optimize = function(method = "nlminb", control = list(), verbose = FALSE,
                           gradient = .self$minGradient, control = optim.control)
         } else {
             out <- nlminb(start = theta, objective = .self$minObjective,
-                          gradient = .self$minGradient, 
+                          gradient = .self$minGradient,
                           hessian = .self$minHessian,
                           control = optim.control)
         }
         # FIXME: use generic fields
         .self$optim.out <- out
     }
-    # just in case, a last call to objective() 
+    # just in case, a last call to objective()
     tmp <- minObjective()
 
 }
@@ -114,10 +114,10 @@ optimize = function(method = "nlminb", control = list(), verbose = FALSE,
 # - it assumes that the hessian is always positive definite (no check!)
 # - it may do some backstepping, but there is no guarantee that it will
 #   converge
-# this function is NOT for general-purpose optimization, but should only be 
-# used or simple (convex!) problems (eg. estimating polychoric/polyserial 
+# this function is NOT for general-purpose optimization, but should only be
+# used or simple (convex!) problems (eg. estimating polychoric/polyserial
 # correlations, probit regressions, ...)
-# 
+#
 lavOptimNewtonRaphson <- function(object,
                                   control = list(iter.max  = 100L,
                                                  grad.tol  = 1e-6,
@@ -140,8 +140,8 @@ lavOptimNewtonRaphson <- function(object,
     for(i in seq_len(control$iter.max)) {
 
         if(control$verbose) {
-            cat("NR step ", sprintf("%2d", (i-1L)), ": max.grad = ", 
-                sprintf("%12.9f", max.grad), " norm.grad = ", 
+            cat("NR step ", sprintf("%2d", (i-1L)), ": max.grad = ",
+                sprintf("%12.9f", max.grad), " norm.grad = ",
                 sprintf("%12.9f", norm.grad), "\n", sep="")
         }
 

R/01RefClass_02lavML.R

@@ -33,7 +33,7 @@ scores = function(x) {
     if(!missing(x)) .self$theta <- x
     cat("this is dummy function\n")
     return(matrix(as.numeric(NA), nobs, npar))
-}, 
+},
 
 gradient = function(x) {
     SCORES <- scores(x)

R/ctr_estfun.R

@@ -1,15 +1,15 @@
 # contributed by Ed Merkle (17 Jan 2013)
 
 
-# YR 12 Feb 2013: small changes to match the results of lav_model_gradient 
+# YR 12 Feb 2013: small changes to match the results of lav_model_gradient
 #                 in the multiple group case
 # YR 30 May 2014: handle 1-variable case (fixing apply in lines 56, 62, 108)
-# YR 05 Nov 2015: add remove.duplicated = TRUE, to cope with strucchange in 
+# YR 05 Nov 2015: add remove.duplicated = TRUE, to cope with strucchange in
 #                 case of simple equality constraints
 # YR 19 Nov 2015: if constraints have been used, compute case-wise Lagrange
 #                 multipliers, and define the scores as: SC + (t(R) lambda)
 # YR 05 Feb 2016: catch conditional.x = TRUE: no support (for now), until
-#                 we can use the generic 0.6 infrastructure for scores, 
+#                 we can use the generic 0.6 infrastructure for scores,
 #                 including the missing-values case
 # YR 16 Feb 2016: adapt to changed @Mp slot elements; add remove.empty.cases=
 #                 argument
@@ -21,7 +21,7 @@ estfun.lavaan <- lavScores <- function(object, scaling = FALSE,
 
   stopifnot(inherits(object, "lavaan"))
 
-  # what if estimator != ML? 
+  # what if estimator != ML?
   # avoid hard error (using stop); throw a warning, and return an empty matrix
   if(object@Options$estimator != "ML") {
       warning("lavaan WARNING: scores only availalbe if estimator is ML")
@@ -53,7 +53,7 @@ estfun.lavaan <- lavScores <- function(object, scaling = FALSE,
   #   npar <- NCOL(object@Model@eq.constraints.K)
   #}
   Score.mat <- matrix(NA, ntot, npar)
-  
+
   for(g in 1:lavsamplestats@ngroups) {
     if (lavsamplestats@ngroups > 1){
       moments <- fitted(object)[[g]]
@@ -67,7 +67,7 @@ estfun.lavaan <- lavScores <- function(object, scaling = FALSE,
     } else {
         N1 <- 1
     }
-  
+
     if(!lavsamplestats@missing.flag) { # complete data
       #if(lavmodel@meanstructure) { # mean structure
         nvar <- ncol(lavsamplestats@cov[[g]])
@@ -115,7 +115,7 @@ estfun.lavaan <- lavScores <- function(object, scaling = FALSE,
       nvar <- ncol(lavsamplestats@cov[[g]])
       score.sigma   <- matrix(0, nsub, nvar*(nvar+1)/2)
       score.mu <- matrix(0, nsub, nvar)
-    
+
       for(p in 1:length(M)) {
         ## Data
         #X <- M[[p]][["X"]]
@@ -127,7 +127,7 @@ estfun.lavaan <- lavScores <- function(object, scaling = FALSE,
         ## (Used to keep track of scores in score.sigma)
         var.idx.mat <- tcrossprod(var.idx)
         Sigma.idx <- which(var.idx.mat[lower.tri(var.idx.mat, diag=T)]==1)
-      
+
         J <- matrix(1, 1L, nobs) #[var.idx]
         J2 <- matrix(1, nvar, nvar)[var.idx, var.idx, drop = FALSE]
         diag(J2) <- 0.5
@@ -148,7 +148,7 @@ estfun.lavaan <- lavScores <- function(object, scaling = FALSE,
         scores.H1 <- group.w[g] * scores.H1
       }
     } # missing
-    
+
     Delta <- computeDelta(lavmodel = lavmodel)[[g]]
     #if(lavmodel@eq.constraints) {
     #    Delta <- Delta %*% lavmodel@eq.constraints.K # + lavmodel@eq.constraints.k0
@@ -160,12 +160,12 @@ estfun.lavaan <- lavScores <- function(object, scaling = FALSE,
     if(scaling){
       Score.mat[wi,] <- (-1/ntot) * Score.mat[wi,]
     }
-    
+
   } # g
 
   # handle empty rows
   if(remove.empty.cases) {
-      #empty.idx <- which( apply(Score.mat, 1L, 
+      #empty.idx <- which( apply(Score.mat, 1L,
       #                        function(x) sum(is.na(x))) == ncol(Score.mat) )
       empty.idx <- unlist(lapply(lavdata@Mp, "[[", "empty.idx"))
       if(length(empty.idx) > 0L) {