Add citation in README file

mmhs013 · Jul 26, 2019 · 9d34d00 · 9d34d00
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diff --git a/... pyMannKendall a python package for non parametric Mann Kendall family of trend tests.pdf b/... pyMannKendall a python package for non parametric Mann Kendall family of trend tests.pdf
diff --git a/README.md b/README.md
@@ -124,6 +124,32 @@ or, you can directly unpack your results like this:
 trend, h, p, z, Tau, s, var_s, slope = mk.original_test(data)
 ```
 
+## Citation
+
+If you publish results for which you used `pyMannKendall`, please give credit by citing [Hussain et al., (2019)](https://doi.org/10.21105/joss.01556):
+
+> Hussain et al., (2019). pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.. Journal of Open Source Software, 4(39), 1556, https://doi.org/10.21105/joss.01556
+
+
+```
+@article{Hussain2019pyMannKendall,
+	journal = {Journal of Open Source Software},
+	doi = {10.21105/joss.01556},
+	issn = {2475-9066},
+	number = {39},
+	publisher = {The Open Journal},
+	title = {pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.},
+	url = {http://dx.doi.org/10.21105/joss.01556},
+	volume = {4},
+	author = {Hussain, Md. and Mahmud, Ishtiak},
+	pages = {1556},
+	date = {2019-07-25},
+	year = {2019},
+	month = {7},
+	day = {25},
+}
+```
+
 ## Contributions
 
 `pyMannKendall` is a community project and welcomes contributions. Additional information can be found in the [contribution guidelines](https://github.com/mmhs013/pyMannKendall/blob/master/CONTRIBUTING.md)
@@ -134,3 +160,33 @@ trend, h, p, z, Tau, s, var_s, slope = mk.original_test(data)
 `pyMannKendall` wishes to maintain a positive community. Additional details can be found in the [Code of Conduct](https://github.com/mmhs013/pyMannKendall/blob/master/CODE_OF_CONDUCT.md)
 
 
+## References
+
+1. Bari,S.H., Rahman,M.T.U., Hoque,M.A., & Hussain,M.M.(2016). Analysis of seasonal and annual rainfall trends in the northern region of Bangladesh. Atmospheric Research, 176, 148–158. doi:[10.1016/j.atmosres.2016.02.008](https://doi.org/10.1016/j.atmosres.2016.02.008)
+
+2. Cox, D. R., & Stuart, A. (1955). Some quick sign tests for trend in location and dispersion. Biometrika, 42(1/2), 80–95. doi:[10.2307/2333424](https://doi.org/10.2307/2333424)
+
+3. Hamed, K. H., & Rao, A. R. (1998). A modified Mann–Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182–196. doi:[10.1016/S0022-1694(97)00125-X](https://doi.org/10.1016/S0022-1694(97)00125-X)
+
+4. Helsel, D. R., & Frans, L. M. (2006). Regional Kendall test for trend. Environmental science & technology, 40(13), 4066–4073. doi:[10.1021/es051650b](https://doi.org/10.1021/es051650b)
+
+5. Hipel, K. W., & McLeod, A. I. (1994). Time series modelling of water resources and environmental systems (Vol. 45). Elsevier.
+
+6. Hirsch, R. M., Slack, J. R., & Smith, R. A. (1982). Techniques of trend analysis for monthly water quality data. Water resources research, 18(1), 107–121. doi:[10.1029/WR018i001p00107](https://doi.org/10.1029/WR018i001p00107)
+
+7. Kendall, M. (1975). Rank correlation measures. Charles Griffin, London, 202, 15.
+
+8. Libiseller, C., & Grimvall, A. (2002). Performance of partial Mann–Kendall tests for trend detection in the presence of covariates. Environmetrics: The official journal of the International Environmetrics Society, 13(1), 71–84. doi:[10.1002/env.507](https://doi.org/1010.1002/env.507)
+
+9. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 245–259. doi:[10.2307/1907187](https://doi.org/10.2307/1907187)
+
+10. Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American statistical association, 63(324), 1379–1389. doi:[10.1080/01621459.1968.10480934](https://doi.org/10.1080/01621459.1968.10480934)
+
+11. Theil,H.(1950). A rank-invariant method of linear and polynominal regression analysis (parts 1-3). In Ned. Akad. Wetensch. Proc. Ser. A (Vol. 53, pp. 1397–1412).
+
+12. Yue, S., & Wang, C. (2004). The Mann–Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water resources management, 18(3), 201–218. doi:[10.1023/B:WARM.0000043140.61082.60](https://doi.org/10.1023/B:WARM.0000043140.61082.60)
+
+13. Yue, S., & Wang, C. Y. (2002). Applicability of prewhitening to eliminate the influence of serial correlation on the Mann–Kendall test. Water resources research, 38(6), 4–1. doi:[10.1029/2001WR000861](https://doi.org/10.1029/2001WR000861)
+
+14. Yue, S., Pilon, P., Phinney, B., & Cavadias, G. (2002). The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological processes, 16(9), 1807–1829. doi:[10.1002/hyp.1095](https://doi.org/10.1002/hyp.1095)
+
diff --git a/pymannkendall/pymannkendall.py b/pymannkendall/pymannkendall.py
@@ -1,9 +1,10 @@
 """
 Created on 05 March 2018
-Update on 23 July 2019
+Update on 26 July 2019
 @author: Md. Manjurul Hussain Shourov
 version: 1.1
 Approach: Vectorisation
+Citation: Hussain et al., (2019). pyMannKendall: a python package for non parametric Mann Kendall family of trend tests.. Journal of Open Source Software, 4(39), 1556, https://doi.org/10.21105/joss.01556
 """
 
 from __future__ import division