diff --git a/.gitattributes b/.gitattributes
index 682f2b98..530d3344 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -19,5 +19,5 @@ vignettes/tex export-ignore
 vignettes/figures export-ignore
 vignettes/manual_files export-ignore
 vignettes/debugger_template.Rnw export-ignore
-inst/WORDLIST
-man/MARSS_out.Rd
+inst/WORDLIST export-ignore
+man/MARSS_out.Rd export-ignore
diff --git a/NEWS.md b/NEWS.md
index 69e8d400..6cc709a2 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -9,7 +9,7 @@ New work on MARSS before posting to CRAN is at the GitHub repo.  See issues post
 
 MARSS 3.11.1 (released 2020-08-18 on GitHub)
 ------------------------------------
-Version 3.11.1 is focused on addition of the `predict`, `forecast`, `fitted` and `residuals` functions along with plotting functions for the output. Documentation for these functions along with background literature and the derivation of the Residuals algorithms have been updated. Residuals in state-space models are complex as there are two processes (observation and state), three types of conditioning (data to t-1, t or T), and four types of standardization used in the literature (none, marginal, Cholesky on the full variance matrix, and Cholesky on only model or state residual variance). The MARSS package computes all the variants of residuals. Many of the `predict` changes are listed below for 3.10.13 release on GitHub. New chapters illustrating structural equation models using MARSS versus `StructTS` and the KFAS package were added. The KFAS chapter compares the residuals to the MARSS. The two packages use different algorithms to compute the same residuals.
+Version 3.11.1 is focused on addition of the `predict`, `forecast`, `fitted` and `residuals` functions along with plotting functions for the output. Documentation for these functions along with background literature and the derivation of the residuals algorithms have been updated. Residuals in state-space models are complex as there are two processes (observation and state), three types of conditioning (data to t-1, t or T), and four types of standardization used in the literature (none, marginal, Cholesky on the full variance matrix, and Cholesky on only model or state residual variance). The MARSS package computes all the variants of residuals. Many of the `predict` changes are listed below for 3.10.13 release on GitHub. New chapters illustrating structural equation models using MARSS versus `StructTS` and the KFAS package were added. The KFAS chapter compares the KFAS residuals functions to the MARSS residuals functions. The two packages use different algorithms and different semantics to compute the same residuals.
 
 
 ENHANCEMENTS