Updating version+news file

DESCRIPTION

@@ -1,8 +1,8 @@
 Package: lava
 Type: Package
 Title: Latent Variable Models
-Version: 1.7.4
-Date: 2024-02-03
+Version: 1.8.0
+Date: 2024-02-22
 Authors@R: c(person("Klaus K.", "Holst", email="klaus@holst.it", role=c("aut", "cre")),
  person("Brice", "Ozenne", role = "ctb"),
  person("Thomas", "Gerds", role = "ctb"))

NEWS.md

@@ -1,10 +1,14 @@
-# lava 1.7.4
+# lava 1.8.0
  - New methods `estimate.mlm`, `IC.mlm`, `pars.mlm`, `estimate.array`, `estimate.data.frame`
- - `Print` method for tabular data (matrix, data.frame)
- - `merge` now supports regular expressions 
+ - `Print` method for tabular data (matrix, data.frame, data.table)
+ - `merge` now supports regular expressions
  - `IC` returns row-names (default id) as obtained from model.matrix or similar
  - New vignette: Influence Functions
- - operators %in.open%, %in.closed% for checking if elements are within a range `3 %in.open% c(0,1)`)
+ - operators `%in.open%`, `%in.closed%` for checking if elements are within a range
+ `3 %in.open% c(0,1)`)
+- `estimate(..., estimator='glm')` now works with formulas with just an intercept
+- `as.data.frame.sim`, `as.matrix.sim`
+- fixed issues with quasi* families and negative binomial regr (MASS:glm.nb)
 
 # lava 1.7.3 
 - `parameter.estimate` method to extract matrix with estimates, standard errors, and confidence limits from and estimate object (coefmat element)

R/glmest.R

@@ -54,14 +54,16 @@ GLMest <- function(m,data,control=list(),...) {
  ## if (isEventTime) {
  ## y <- yvar.et[y]
  ## }
- #nn0 <- paste(y,xx,sep=lava.options()$symbol[1])
+ # nn0 <- paste(y,xx,sep=lava.options()$symbol[1])
 
- f <- as.formula(paste0(y,"~",paste(xx,collapse="+")))
- isSurv <- inherits(data[1,y],"Surv")
+ f <- as.formula(paste0(y, "~",
+ paste(xx, collapse = "+")
+ ))
+ isSurv <- inherits(data[1, y], "Surv")
  if (isSurv) {
-  g <- survival::survreg(f,data=data,dist=fam$family,...)
+ g <- survival::survreg(f,data=data,dist=fam$family,...)
  } else {
-  g <- glm(f,family=fam,data=data,...)
+ g <- glm(f,family=fam,data=data,...)
  }
  p <- pars(g)
  ii <- IC(g)