add use="pairwise.complete.obs" in cov() when computing unbiased Gamma

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: lavaan
 Title: Latent Variable Analysis
-Version: 0.6-19.2150
+Version: 0.6-19.2168
 Authors@R: c(person(given   = "Yves", family = "Rosseel",
                     role    = c("aut", "cre"),
                     email   = "Yves.Rosseel@UGent.be",

R/lav_samplestats_gamma.R

@@ -441,7 +441,8 @@ lav_samplestats_Gamma <- function(Y, # Y+X if cond!
         gettext("unbiased Gamma only available for the simple
         (not conditional.x or fixed.x or model-based or clustered) setting."))
     } else {
-      COV <- COV.unbiased <- cov(Y)
+	  # data really should be complete
+      COV <- COV.unbiased <- stats::cov(Y, use = "pairwise.complete.obs")
       COV <- COV * (N - 1) / N
       cov.vech <- lav_matrix_vech(COV)
     }

R/lav_test_LRT.R

@@ -483,12 +483,12 @@ lav_test_lrt_single_model <- function(object, method = "default",
     aic <- c(NA, AIC(object))
     bic <- c(NA, BIC(object))
   }
-  
+
   ## determine which @test element
   tn <- names(object@test)
   if (length(tn) == 1L) {
     TEST <- 1L # only choice
-    
+
     ## More than 1.  Cycle through possible user specifications:
   } else if (method[1] == "standard") {
     TEST <- 1L
@@ -521,14 +521,14 @@ lav_test_lrt_single_model <- function(object, method = "default",
   if (!is.null(object@test[[TEST]]$shift.parameter)) {
     attr(val, "shift") <- c(NA, object@test[[TEST]]$shift.parameter)
   }
-  
+
   ## heading
   if (grepl(pattern = "browne", x = TEST)) {
     attr(val, "heading") <- object@test[[TEST]]$label
-    
+
   } else if (TEST == 1L) {
     attr(val, "heading") <- "Chi-Squared Test Statistic (unscaled)\n"
-    
+
   } else {
     LABEL <- object@test[[TEST]]$label
     attr(val, "heading") <- paste0("Chi-Squared Test Statistic (scaled",
@@ -538,7 +538,7 @@ lav_test_lrt_single_model <- function(object, method = "default",
                                           yes = "\n", no = paste("\n ", LABEL)),
                                    "\n")
   }
-  
+
   class(val) <- c("anova", class(val))
 
   val

R/lav_test_diff.R

@@ -205,12 +205,12 @@ lav_test_diff_SatorraBentler2001 <- function(m1, m0, test = 2) {
   T1 <- m1@test[[1]]$stat
   r1 <- m1@test[[1]]$df
   c1 <- m1@test[[test]]$scaling.factor
-  
+
   ## check for situations when scaling.factor would be NA
   if (r1 == 0) {
     ## saturated model
     c1 <- 1 # canceled out by 0 when calculating "cd"
-    
+
   } else if (r1 > 0 && isTRUE(all.equal(T1, 0))) {
     ## perfect fit
     c1 <- 0 # cancels out r1 when calculating "cd"