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ngreifer · Mar 22, 2024 · f83080a · f83080a
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: WeightIt
 Type: Package
 Title: Weighting for Covariate Balance in Observational Studies
-Version: 0.14.2.9004
+Version: 1.0.0
 Authors@R: c(
     person("Noah", "Greifer", role=c("aut", "cre"), 
            email = "noah.greifer@gmail.com",
@@ -38,7 +38,7 @@ Suggests:
     brglm2 (>= 0.5.2),
     osqp (>= 0.6.0.5),
     survival,
-    fwb,
+    fwb (>= 0.2.0),
     splines,
     marginaleffects (>= 0.11.1),
     sandwich,

diff --git a/NEWS.md b/NEWS.md
@@ -1,7 +1,7 @@
 WeightIt News and Updates
 ======
 
-# WeightIt (development version)
+# WeightIt 1.0.0
 
 * Added a new function, `glm_weightit()` (along with wrapper `lm_weightit()`) and associated methods for fitting generalized linear models in the weighted sample, with the option of accounting for estimation of the weights in computing standard errors via M-estimation or two forms of bootstrapping. `glm_weightit()` also supports multinomial logistic regression in addition to all models supported by `glm()`. Cluster-robust standard errors are supported, and output is compatible with any functions that accept `glm()` objects. Not all weighting methods support M-estimation, but for those that do, a new component is added to the `weightit` output object. Currently, GLM propensity scores, entropy balancing, just-identified CBPS, and inverse probability tilting (described below) support M-estimation-based standard errors with `glm_weightit()`.