Fix citation

petersonR · Mar 28, 2024 · 63949e1 · 63949e1
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -16,7 +16,7 @@ Description: An implementation of sparsity-ranked lasso and related methods
     presence of prior informational asymmetry. This situation exists for time 
     series data with complex seasonality, as shown in Peterson and Cavanaugh 
     (2024) <doi:10.1177/1471082X231225307>, which also describes this package
-    in greater detail. The sparsity-ranked penalization methods for Time Series
+    in greater detail. The sparsity-ranked penalization methods for time series
     implemented in 'fastTS' can fit large/complex/high-frequency time series
     quickly, even with a high-dimensional exogenous feature set. The method is
     considerably faster than its competitors, while often producing more 
@@ -49,4 +49,4 @@ VignetteBuilder: knitr
 URL: https://petersonr.github.io/fastTS/, 
     https://github.com/petersonR/fastTS/
 BugReports: https://github.com/petersonR/fastTS/issues
-Date: 2024-03-08
+Date: 2024-03-28
diff --git a/NEWS.md b/NEWS.md
@@ -1,6 +1,6 @@
 # fastTS 1.0.1
 
-- Update citation with now published [paper](https://doi.org/10.1177/1471082X231225307)!  
+- Update citation with now published [paper](https://doi.org/10.1177/1471082X231225307)
 
 # fastTS 1.0.0
 

diff --git a/README.Rmd b/README.Rmd
@@ -27,11 +27,11 @@ The `fastTS` package efficiently fits long, high-frequency time series with comp
 
 Originally described in [Peterson and Cavanaugh (2022)](https://doi.org/10.1007/s10182-021-00431-7) in the context of variable selection with interactions and/or polynomials, *ranked sparsity* is a philosophy of variable selection in the presence of prior informational asymmetry. 
 
-This package implements such methods for fast fitting of time series data with complex seasonality or exogenous features. More information is included in [Peterson and Cavanaugh (2024)](https://doi.org/10.1177/1471082X23122530) ([accepted version](https://arxiv.org/abs/2211.01492)). The basic premise is to utilize the sparsity-ranked lasso (or similar) to be less skeptical of more recent lags, and suspected seasonal relationships. 
+This package implements such methods for fast fitting of time series data with complex seasonality or exogenous features. More information is included in [Peterson and Cavanaugh (2024)](https://doi.org/10.1177/1471082X231225307). The basic premise is to utilize the sparsity-ranked lasso (or similar) to be less skeptical of more recent lags, and suspected seasonal relationships. 
 
 Please cite `fastTS` as: 
 
-Peterson R, Cavanaugh J. Fast, effective, and coherent time series modelling using the sparsity-ranked lasso. *Statistical Modelling*. 2024. doi:10.1177/1471082X231225307
+Peterson R. A. & Cavanaugh J. E. (2024). Fast, effective, and coherent time series modelling using the sparsity-ranked lasso. *Statistical Modelling*. doi:10.1177/1471082X231225307
 
 ## Installation
 

diff --git a/README.md b/README.md
@@ -28,16 +28,15 @@ informational asymmetry.
 This package implements such methods for fast fitting of time series
 data with complex seasonality or exogenous features. More information is
 included in [Peterson and Cavanaugh
-(2024)](https://doi.org/10.1177/1471082X23122530) ([accepted
-version](https://arxiv.org/abs/2211.01492)). The basic premise is to
-utilize the sparsity-ranked lasso (or similar) to be less skeptical of
-more recent lags, and suspected seasonal relationships.
+(2024)](https://doi.org/10.1177/1471082X231225307). The basic premise is
+to utilize the sparsity-ranked lasso (or similar) to be less skeptical
+of more recent lags, and suspected seasonal relationships.
 
 Please cite `fastTS` as:
 
-Peterson R, Cavanaugh J. Fast, effective, and coherent time series
-modelling using the sparsity-ranked lasso. *Statistical Modelling*.
-2024. <doi:10.1177/1471082X231225307>
+Peterson R. A. & Cavanaugh J. E. (2024). Fast, effective, and coherent
+time series modelling using the sparsity-ranked lasso. *Statistical
+Modelling*. <doi:10.1177/1471082X231225307>
 
 ## Installation
 

diff --git a/inst/CITATION b/inst/CITATION
@@ -12,5 +12,5 @@ bibentry(
   pages    = "",
   doi      = "10.1177/1471082X231225307",
   textVersion =
-        "Ryan A. Peterson & Joseph E. Cavanaugh (2024). Fast, effective, and coherent time series modeling using the sparsity-ranked lasso. Statistical Modelling. DOI: 10.1177/1471082X231225307"
+        "Peterson, R.A. & Cavanaugh, J.E. (2024). Fast, effective, and coherent time series modelling using the sparsity-ranked lasso. Statistical Modelling. DOI: 10.1177/1471082X231225307"
 )