fix optim.method = "bfgs" issue

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: lavaan
 Title: Latent Variable Analysis
-Version: 0.6-19.2170
+Version: 0.6-19.2180
 Authors@R: c(person(given   = "Yves", family = "Rosseel",
                     role    = c("aut", "cre"),
                     email   = "Yves.Rosseel@UGent.be",

R/lav_model_estimate.R

@@ -561,7 +561,7 @@ lav_model_estimate <- function(lavmodel = NULL,
       OPTIMIZER <- toupper(lavoptions$optim.method)
       stopifnot(OPTIMIZER %in% c(
         "NLMINB0", "NLMINB1", "NLMINB2",
-        "NLMINB", "BFGS", "L-BFGS-B", "NONE"
+        "NLMINB", "BFGS", "L.BFGS.B", "NONE"
       ))
       if (OPTIMIZER == "NLMINB1") {
         OPTIMIZER <- "NLMINB"
@@ -760,7 +760,7 @@ lav_model_estimate <- function(lavmodel = NULL,
     } else {
       converged <- FALSE
     }
-  } else if (OPTIMIZER == "L-BFGS-B") {
+  } else if (OPTIMIZER == "L.BFGS.B") {
     # warning, does not cope with Inf values!!
 
     control.lbfgsb <- list(
@@ -938,7 +938,7 @@ lav_model_estimate <- function(lavmodel = NULL,
   if (converged) {
     # check.gradient
     if (!is.null(GRADIENT) &&
-      OPTIMIZER %in% c("NLMINB", "BFGS", "L-BFGS-B")) {
+      OPTIMIZER %in% c("NLMINB", "BFGS", "L.BFGS.B")) {
       # compute unscaled gradient
       dx <- GRADIENT(x)
 

R/lav_options_default.R

@@ -369,7 +369,8 @@ lav_options_default <- function() {
   # optimization
   elm("control", list(), oklen = c(0L, 100L))
   elm("optim.method", "default", chr = c(
-    "nlminb", "gn", "default", "noniter", "none", "em"
+    "nlminb", "nlminb0", "nlminb1", "nlminb2",
+    "bfgs", "l.bfgs.b", "gn", "default", "noniter", "none", "em"
   )) # gn for DLS, nlminb rest
   elm("optim.attempts", 4L, nm = "[1, 4]")
   elm("optim.force.converged", FALSE, bl = TRUE)