{"page":"182-195","intvolume":"        14","language":[{"iso":"eng"}],"issue":"1","date_updated":"2023-12-21T12:10:49Z","title":"The smoots Package in R for Semiparametric Modeling of Trend Stationary Time Series","publication":"The R Journal","_id":"50025","status":"public","date_created":"2023-12-21T12:09:53Z","doi":"10.32614/rj-2022-017","publication_status":"published","author":[{"first_name":"Yuanhua","full_name":"Feng, Yuanhua","last_name":"Feng"},{"first_name":"Thomas","full_name":"Gries, Thomas","last_name":"Gries"},{"last_name":"Letmathe","full_name":"Letmathe, Sebastian","first_name":"Sebastian"},{"last_name":"Schulz","full_name":"Schulz, Dominik","first_name":"Dominik"}],"year":"2022","user_id":"186","publication_identifier":{"issn":["2073-4859"]},"citation":{"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>.","short":"Y. Feng, T. Gries, S. Letmathe, D. Schulz, The R Journal 14 (2022) 182–195.","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>.","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>","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>"},"volume":14,"publisher":"The R Foundation","type":"journal_article","keyword":["Statistics","Probability and Uncertainty","Numerical Analysis","Statistics and Probability"]}