[{"issue":"1","publication":"The R Journal","keyword":["Statistics","Probability and Uncertainty","Numerical Analysis","Statistics and Probability"],"type":"journal_article","department":[{"_id":"475"},{"_id":"19"},{"_id":"200"}],"date_created":"2023-12-21T12:09:31Z","publication_status":"published","date_updated":"2024-06-12T12:57:13Z","intvolume":"        14","title":"The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series","year":"2022","publication_identifier":{"issn":["2073-4859"]},"author":[{"full_name":"Feng, Yuanhua","last_name":"Feng","first_name":"Yuanhua"},{"full_name":"Gries, Thomas","last_name":"Gries","first_name":"Thomas"},{"full_name":"Letmathe, Sebastian","last_name":"Letmathe","first_name":"Sebastian"},{"last_name":"Schulz","first_name":"Dominik","full_name":"Schulz, Dominik"}],"doi":"10.32614/rj-2022-017","language":[{"iso":"eng"}],"citation":{"bibtex":"@article{Feng_Gries_Letmathe_Schulz_2022, title={The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series}, volume={14}, DOI={<a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>}, number={1}, journal={The R Journal}, publisher={The R Foundation}, author={Feng, Yuanhua and Gries, Thomas and Letmathe, Sebastian and Schulz, Dominik}, year={2022}, pages={182–195} }","ama":"Feng Y, Gries T, Letmathe S, Schulz D. The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series. <i>The R Journal</i>. 2022;14(1):182-195. doi:<a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>","mla":"Feng, Yuanhua, et al. “The Smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series.” <i>The R Journal</i>, vol. 14, no. 1, The R Foundation, 2022, pp. 182–95, doi:<a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>.","short":"Y. Feng, T. Gries, S. Letmathe, D. Schulz, The R Journal 14 (2022) 182–195.","chicago":"Feng, Yuanhua, Thomas Gries, Sebastian Letmathe, and Dominik Schulz. “The Smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series.” <i>The R Journal</i> 14, no. 1 (2022): 182–95. <a href=\"https://doi.org/10.32614/rj-2022-017\">https://doi.org/10.32614/rj-2022-017</a>.","ieee":"Y. Feng, T. Gries, S. Letmathe, and D. Schulz, “The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series,” <i>The R Journal</i>, vol. 14, no. 1, pp. 182–195, 2022, doi: <a href=\"https://doi.org/10.32614/rj-2022-017\">10.32614/rj-2022-017</a>.","apa":"Feng, Y., Gries, T., Letmathe, S., &#38; Schulz, D. (2022). The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series. <i>The R Journal</i>, <i>14</i>(1), 182–195. <a href=\"https://doi.org/10.32614/rj-2022-017\">https://doi.org/10.32614/rj-2022-017</a>"},"status":"public","user_id":"186","volume":14,"page":"182-195","_id":"50024","publisher":"The R Foundation"}]