An implementation of a statistical language based on JAVA
AbstractAs computing technologies have been developed rapidly and continuously, statistical languages should be frequently improved by adopting them for realizing comfortable statistical environments. Although that work was a difficult task before we can implement such languages relatively easily by modern software techniques such as the Java language which unifies recent computer technologies widely and neatly. In this paper, we describe the idea of a new Jasp (JAva based Statistical Processor) language, which is a function based and object oriented language) is suitable for interactive operation and has flexibilities and diverse extendibilities. Considering required characteristics for a statistical language, we decide to implement the Jasp language by Java technologies through the Puuts language, which is a script language written in Java and a preprocessing approach. This implementation brings several advantages such as reduction of development costs, effective uses of existing resources and enough reliabilities. --
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Bibliographic InfoPaper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 2001,72.
Date of creation: 2001
Date of revision:
Java; Function based language; GUI; Object oriented language; Preprocessing; Statistical System;
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- Yoshikazu Yamamoto & Junji Nakano & Takeshi Fujiwara, 2010. "Parallel computing in the statistical system Jasp," Computational Statistics, Springer, vol. 25(2), pages 291-298, June.
- Junji Nakano & Moon Huh & Yoshikazu Yamamoto & Takeshi Fujiwara & Ikunori Kobayashi, 2004. "Adding visualization functions of DAVIS to Jasp: Mixing two Java-based statistical systems," Computational Statistics, Springer, vol. 19(1), pages 137-146, February.
- Fujiwara, Takeshi & Nakano, Junji & Yamamoto, Yoshikazu, 2007. "Using mathematical expressions in a statistical language," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 650-662, October.
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