diff --git a/NEWS.md b/NEWS.md
index 037caf0..46a67dd 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -13,8 +13,12 @@ WeightIt News and Updates
 
 * `moments` can now be set to 0 when `quantile` is supplied to ensure balance on the quantiles without the moments for the methods that accepts `quantiles`. Thanks to @BERENZ for the suggestion.
 
+* For `ordinal_weightit` objects, `summary()` now has the option to omit thresholds from the output.
+
 * Fixed a bug in `ordinal_weightit()` where the Hessian (and therefore the HC0 robust variance) were calculated incorrectly when come coefficients were aliased (i.e., due to linearly dependent predictors).
 
+* Fixed a bug in `print.summary.glm_weightit()` when confidence intervals were requested. A new printing function is used that produces slightly nicer tables.
+
 * Fixes to vignettes and tests to satisfy CRAN checks.
 
 * Minor bug, performance, and readability fixes.
diff --git a/man/glm_weightit-methods.Rd b/man/glm_weightit-methods.Rd
index 5f93463..686bbaf 100644
--- a/man/glm_weightit-methods.Rd
+++ b/man/glm_weightit-methods.Rd
@@ -45,6 +45,8 @@
 \item{method}{the kind of test used to compare models. Currently only \code{"Wald"} is allowed.}
 
 \item{tolerance}{for the Wald test, the tolerance used to determine if models are symbolically nested.}
+
+\item{thresholds}{\code{logical}; whether to include thresholds in the \code{summary()} output for \code{ordinal_weightit} objects. Default is \code{TRUE}.}
 }
 \value{
 \code{summary()} returns a \code{summary.glm_weightit()} object, which has its own print method. For \code{coxph_weightit()} objects, the \code{print()} and \code{summary()} methods are more like those for \code{glm} objects then for \code{coxph} objects.
diff --git a/man/method_bart.Rd b/man/method_bart.Rd
index 1c35286..282c34e 100644
--- a/man/method_bart.Rd
+++ b/man/method_bart.Rd
@@ -85,7 +85,7 @@ When \code{include.obj = TRUE}, the \code{bart2} fit(s) used to generate the pre
 
 \examples{
 \dontshow{if (requireNamespace("dbarts", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
-library("cobalt")
+\donttest{library("cobalt")
 data("lalonde", package = "cobalt")
 
 #Balancing covariates between treatment groups (binary)
@@ -94,7 +94,7 @@ data("lalonde", package = "cobalt")
                 method = "bart", estimand = "ATT"))
 summary(W1)
 bal.tab(W1)
-\donttest{
+
 #Balancing covariates with respect to race (multi-category)
 (W2 <- weightit(race ~ age + educ + married +
                 nodegree + re74, data = lalonde,
diff --git a/man/method_super.Rd b/man/method_super.Rd
index 0d0b22d..be87193 100644
--- a/man/method_super.Rd
+++ b/man/method_super.Rd
@@ -111,7 +111,7 @@ When \code{include.obj = TRUE}, the SuperLearner fit(s) used to generate the pre
 
 \examples{
 \dontshow{if (all(sapply(c("SuperLearner", "MASS"), requireNamespace, quietly = TRUE))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
-library("cobalt")
+\donttest{library("cobalt")
 data("lalonde", package = "cobalt")
 
 #Balancing covariates between treatment groups (binary)
@@ -122,26 +122,26 @@ data("lalonde", package = "cobalt")
                                "SL.glm.interaction")))
 summary(W1)
 bal.tab(W1)
-\donttest{
-  #Balancing covariates with respect to race (multi-category)
-  (W2 <- weightit(race ~ age + educ + married +
-                    nodegree + re74, data = lalonde,
-                  method = "super", estimand = "ATE",
-                  SL.library = c("SL.glm", "SL.stepAIC",
-                                 "SL.glm.interaction")))
-  summary(W2)
-  bal.tab(W2)
-
-  #Balancing covariates with respect to re75 (continuous)
-  #assuming t(8) conditional density for treatment
-  (W3 <- weightit(re75 ~ age + educ + married +
-                    nodegree + re74, data = lalonde,
-                  method = "super", density = "dt_8",
-                  SL.library = c("SL.glm", "SL.ridge",
-                                 "SL.glm.interaction")))
-  summary(W3)
-  bal.tab(W3)
-}
+
+#Balancing covariates with respect to race (multi-category)
+(W2 <- weightit(race ~ age + educ + married +
+                  nodegree + re74, data = lalonde,
+                method = "super", estimand = "ATE",
+                SL.library = c("SL.glm", "SL.stepAIC",
+                               "SL.glm.interaction")))
+summary(W2)
+bal.tab(W2)
+
+#Balancing covariates with respect to re75 (continuous)
+#assuming t(8) conditional density for treatment
+(W3 <- weightit(re75 ~ age + educ + married +
+                  nodegree + re74, data = lalonde,
+                method = "super", density = "dt_8",
+                SL.library = c("SL.glm", "SL.ridge",
+                               "SL.glm.interaction")))
+summary(W3)
+bal.tab(W3)
+
 #Balancing covariates between treatment groups (binary)
 # using balance SuperLearner to minimize the maximum
 # KS statistic
@@ -153,7 +153,7 @@ bal.tab(W1)
                 SL.method = "method.balance",
                 criterion = "ks.max"))
 summary(W4)
-bal.tab(W4, stats = c("m", "ks"))
+bal.tab(W4, stats = c("m", "ks"))}
 \dontshow{\}) # examplesIf}
 }
 \references{