From fbecc0c1ca98c383ebb5dfd908425a056e25c680 Mon Sep 17 00:00:00 2001
From: zejiang-unsw <47626771+zejiang-unsw@users.noreply.github.com>
Date: Fri, 2 Apr 2021 19:48:32 +1100
Subject: [PATCH] update readme

---
 DESCRIPTION |  1 -
 README.md   | 12 ++++++------
 2 files changed, 6 insertions(+), 7 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index beb43c0..42c53e3 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -13,7 +13,6 @@ Description: Generate synthetic time series from commonly used statistical model
 Depends: R (>= 3.5.0)
 License: GPL (>= 2)
 Encoding: UTF-8
-LazyData: true
 URL: https://github.com/zejiang-unsw/synthesis#readme
 BugReports: https://github.com/zejiang-unsw/synthesis/issues
 Imports: 
diff --git a/README.md b/README.md
index e8d2131..ebfddfd 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,6 @@
 # synthesis: Synthetic data generator
 
-Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) <doi:10.1016/j.advwatres.2019.103430> and Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962> associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>.
+Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) \doi{10.1016/j.advwatres.2019.103430} and Jiang, Z., Sharma, A., & Johnson, F. (2020) \doi{10.1029/2019WR026962} associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) \doi{10.1016/j.envsoft.2020.104907}.
 
 It can be used for variable selection, prediction, and classification and clustering problem generation. 
 
@@ -29,13 +29,13 @@ install.packages("synthesis")
 
 ## Citation
 
-Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020). A wavelet-based tool to modulate variance in predictors: An application to predicting drought anomalies. Environmental modelling & software, 135, 104907. doi:10.1016/j.envsoft.2020.104907
+Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020). A wavelet-based tool to modulate variance in predictors: An application to predicting drought anomalies. Environmental modelling & software, 135, 104907.
 
-Jiang, Z., Sharma, A., & Johnson, F. (2020). Refining Predictor Spectral Representation Using Wavelet Theory for Improved Natural System Modeling. Water Resources Research, 56(3), e2019WR026962. doi:10.1029/2019WR026962
+Jiang, Z., Sharma, A., & Johnson, F. (2020). Refining Predictor Spectral Representation Using Wavelet Theory for Improved Natural System Modeling. Water Resources Research, 56(3), e2019WR026962.
 
-Jiang, Z., Sharma, A., & Johnson, F. (2019). Assessing the sensitivity of hydro-climatological change detection methods to model uncertainty and bias. Advances in Water Resources, 134, 103430. doi:10.1016/j.advwatres.2019.103430
+Jiang, Z., Sharma, A., & Johnson, F. (2019). Assessing the sensitivity of hydro-climatological change detection methods to model uncertainty and bias. Advances in Water Resources, 134, 103430.
 
-Galelli, S., Humphrey, G. B., Maier, H. R., Castelletti, A., Dandy, G. C., & Gibbs, M. S. (2014). An evaluation framework for input variable selection algorithms for environmental data-driven models. Environmental modelling & software, 62, 33-51. doi:10.1016/j.envsoft.2014.08.015
+Galelli, S., Humphrey, G. B., Maier, H. R., Castelletti, A., Dandy, G. C., & Gibbs, M. S. (2014). An evaluation framework for input variable selection algorithms for environmental data-driven models. Environmental modelling & software, 62, 33-51.
 
-Sharma, A. (2000). Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 - A strategy for system predictor identification. Journal of Hydrology, 239(1), 232-239. doi:10.1016/S0022-1694(00)00346-2
+Sharma, A. (2000). Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 1 - A strategy for system predictor identification. Journal of Hydrology, 239(1), 232-239.