diff --git a/DESCRIPTION b/DESCRIPTION
index d91939f..dfa6e1e 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: OutSeekR
 Type: Package
 Title: Statistical Approach to Outlier Detection in RNA-Seq and Related Data
-Version: 0.1.0
-Date: 2024-01-23
+Version: 1.0.0
+Date: 2024-11-15
 Authors@R: c(
     person("Jee Yun", "Han", email = "jyhan@mednet.ucla.edu", role = "aut"),
     person("John", "Sahrmann", email = "jsahrmann@mednet.ucla.edu", role = "aut"),
@@ -16,13 +16,13 @@ Description: An approach to outlier detection in RNA-seq and related data
 Depends: 
     R (>= 2.10)
 Imports:
-    future,
     future.apply,
     gamlss,
     gamlss.dist,
     lsa,
     truncnorm
 Suggests: 
+    future,
     knitr,
     rmarkdown,
     testthat (>= 3.0.0)
diff --git a/NEWS.md b/NEWS.md
index 5e4b6ed..28678c8 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,6 +1,6 @@
 # Unreleased
 
-# OutSeekR 1.0.0 - 2024-11-07 
+# OutSeekR 1.0.0 - 2024-11-15
 
 ## Added
 * Implementation of core *Outlier Detection Algorithm*, a statistical approach for detecting transcript-level outliers in RNA-seq or related data types, leveraging normalized data (e.g., FPKM) and several statistical metrics.
diff --git a/R/detect.outliers.R b/R/detect.outliers.R
index 8269203..7069fe6 100644
--- a/R/detect.outliers.R
+++ b/R/detect.outliers.R
@@ -3,7 +3,7 @@
 #' Detect outliers in normalized RNA-seq data.
 #'
 #' @param data A matrix or data frame of normalized RNA-seq data, organized with transcripts on rows and samples on columns.  Transcript identifiers should be stored as `rownames(data)`.
-#' @param num.null The number of transcripts to generate when simulating from null distributions; default is 1000.
+#' @param num.null The number of transcripts to generate when simulating from null distributions; default is 1000. We recommend using at least 10,000 iterations for publication-level results, with 100,000 or even one million iterations providing more robust estimates.
 #' @param initial.screen.method The statistical criterion for initial gene selection; valid options are 'FDR' and 'p-value'.
 #' @param p.value.threshold The p-value threshold for the outlier test; default is 0.05.  Once the p-value for a sample exceeds `p.value.threshold`, testing for that transcript ceases, and all remaining samples will have p-values equal to `NA`.
 #' @param fdr.threshold The false discovery rate (FDR)-adjusted p-value threshold for determining the final count of outliers; default is 0.01.
diff --git a/man/detect.outliers.Rd b/man/detect.outliers.Rd
index 3eada03..954ab6c 100644
--- a/man/detect.outliers.Rd
+++ b/man/detect.outliers.Rd
@@ -16,7 +16,7 @@ detect.outliers(
 \arguments{
 \item{data}{A matrix or data frame of normalized RNA-seq data, organized with transcripts on rows and samples on columns.  Transcript identifiers should be stored as \code{rownames(data)}.}
 
-\item{num.null}{The number of transcripts to generate when simulating from null distributions; default is 1000.}
+\item{num.null}{The number of transcripts to generate when simulating from null distributions; default is 1000. We recommend using at least 10,000 iterations for publication-level results, with 100,000 or even one million iterations providing more robust estimates.}
 
 \item{initial.screen.method}{The statistical criterion for initial gene selection; valid options are 'FDR' and 'p-value'.}