diff --git a/DESCRIPTION b/DESCRIPTION
index d3b39d6f..9709ad72 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -18,7 +18,7 @@ Description: Animal abundance estimation via conventional, multiple covariate
     fitting is performed via maximum likelihood. Also included are diagnostics
     and plotting for fitted detection functions. Abundance estimation is via a
     Horvitz-Thompson-like estimator.
-Version: 3.0.0.9000
+Version: 3.0.0.9001
 URL: https://github.com/DistanceDevelopment/mrds/
 BugReports: https://github.com/DistanceDevelopment/mrds/issues
 Depends:
diff --git a/NEWS.md b/NEWS.md
index c392ef72..231f16b3 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,9 @@
+# mrds 3.0.1
+
+Bug Fixes
+
+* Fixed formatting issue in flnl.grad help
+
 # mrds 3.0.0
 
 New features
diff --git a/R/flnl.grad.R b/R/flnl.grad.R
index 5d387cbf..d0b08557 100644
--- a/R/flnl.grad.R
+++ b/R/flnl.grad.R
@@ -1,11 +1,11 @@
-#' Gradients of negative log likelihood function
-#'
+#' Gradient of the negative log likelihood function
+#' 
 #' This function derives the gradients of the negative log likelihood function, 
 #' with respect to all parameters. It is based on the theory presented in 
 #' Introduction to Distance Sampling (2001) and Distance Sampling: Methods and
 #' Applications (2015). It is not meant to be called by users of the \code{mrds}
 #' and \code{Distance} packages directly but rather by the gradient-based 
-#' solver. This solver is use when our distance sampling model is for 
+#' solver. This solver is used when our distance sampling model is for 
 #' single-observer data coming from either line or point transect and only when  
 #' the detection function contains an adjustment series but no covariates. It is 
 #' implement for the following key + adjustment series combinations for the 
diff --git a/man/flnl.grad.Rd b/man/flnl.grad.Rd
index e9566204..00bb4987 100644
--- a/man/flnl.grad.Rd
+++ b/man/flnl.grad.Rd
@@ -2,19 +2,7 @@
 % Please edit documentation in R/flnl.grad.R
 \name{flnl.grad}
 \alias{flnl.grad}
-\title{This function derives the gradients of the negative log likelihood function, 
-with respect to all parameters. It is based on the theory presented in 
-Introduction to Distance Sampling (2001) and Distance Sampling: Methods and
-Applications (2015). It is not meant to be called by users of the \code{mrds}
-and \code{Distance} packages directly but rather by the gradient-based 
-solver. This solver is use when our distance sampling model is for 
-single-observer data coming from either line or point transect and only when  
-the detection function contains an adjustment series but no covariates. It is 
-implement for the following key + adjustment series combinations for the 
-detections function: the key function can be half-normal, hazard-rate or 
-uniform, and the adjustment series can be cosine, simple polynomial or 
-Hermite polynomial. Data can be either binned or exact, but a combination
-of the two has not been implemented yet.}
+\title{Gradient of the negative log likelihood function}
 \usage{
 flnl.grad(pars, ddfobj, misc.options, fitting = "all")
 }
@@ -41,7 +29,7 @@ with respect to all parameters. It is based on the theory presented in
 Introduction to Distance Sampling (2001) and Distance Sampling: Methods and
 Applications (2015). It is not meant to be called by users of the \code{mrds}
 and \code{Distance} packages directly but rather by the gradient-based 
-solver. This solver is use when our distance sampling model is for 
+solver. This solver is used when our distance sampling model is for 
 single-observer data coming from either line or point transect and only when  
 the detection function contains an adjustment series but no covariates. It is 
 implement for the following key + adjustment series combinations for the