The LENNS package (’Lyapunov Exponents for Noisy Nonlinear Systems’) was originally standalone f77, but then it got merged into the FUNFITS package for S-plus. FUNFITS is still available (see below) but is now ’frozen’ meaning that it probably won’t work with a current version of S-plus. Most of its functionality has been incorporated into the fields package for R that can be gotten from CRAN ( The rest -- neural networks, global and local Lyapunov exponents -- is here. LENNS and nnreg for R/Windows. This is not really a package, and it only works under Windows. Create a folder c:lenns and then extract the contents of into the lenns folder using folder names. This will create a folder tree c:lennschtml,c:lennsexec, etc. It is not in shape to be installed from within R (that used to work, but not since the rules changed with R 2.0) -- you will need to source() code from within an R session. The file readme.lenns tells you what to do. There is a set of html help pages that can be accessed from lenns/html/00Index.html. Sorry, this is the best I can do right now. If you have trouble getting it to run, let me know. If you unzip it into c:lenns, it should work ”out of the box” after you source lenns.R.

References in zbMATH (referenced in 23 articles , 1 standard article )

Showing results 1 to 20 of 23.
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  1. Chung, Julianne; Nagy, James G.: An efficient iterative approach for large-scale separable nonlinear inverse problems (2010)
  2. Kang, Emily L.; Cressie, Noel; Shi, Tao: Using temporal variability to improve spatial mapping with application to satellite data (2010)
  3. Lee, Dae-Jin; Durbán, María: Smooth-CAR mixed models for spatial count data (2009)
  4. Chung, Julianne; Nagy, James G.; O’Leary, Dianne P.: A weighted-GCV method for Lanczos-hybrid regularization (2008)
  5. Cressie, Noel; Johannesson, Gardar: Fixed rank Kriging for very large spatial data sets (2008)
  6. Holan, Scott; Wang, Suojin; Arab, Ali; Sadler, E.John; Stone, Kenneth: Semiparametric geographically weighted response curves with application to site-specific agriculture (2008)
  7. Ormerod, John T.; Wand, M.P.; Koch, Inge: Penalized spline support vector classifiers computational issues (2008)
  8. Kneib, Thomas; Fahrmeir, Ludwig: A mixed model approach for geoadditive hazard regression (2007)
  9. Wager, Carrie; Vaida, Florin; Kauermann, Göran: Model selection for penalized spline smoothing using Akaike information criteria (2007)
  10. Delouille, Véronique; Jansen, Maarten; von Sachs, Rainer: Second-generation wavelet denoising methods for irregularly spaced data in two dimensions (2006)
  11. French, Jonathan L.; Wand, Matthew P.: Generalized additive models for cancer mapping with incomplete covariates (2004)
  12. Kim, Young-Ju; Gu, Chong: Smoothing spline Gaussian regression: more scalable computation via efficient approximation (2004)
  13. Shintani, Mototsugu; Linton, Oliver: Nonparametric neural network estimation of Lyapunov exponents and a direct test for chaos (2004)
  14. Kammann, E.E.; Wand, M.P.: Geoadditive models (2003)
  15. Pascual, Mercedes; Mazzega, Pierre: Quasicycles revisited: apparent sensitivity to initial conditions (2003)
  16. Serletis, Apostolos; Shintani, Mototsugu: No evidence of chaos but some evidence of dependence in the US stock market. (2003)
  17. Wand, M.P.: Smoothing and mixed models (2003)
  18. Amato, U.; Antoniadis, A.; De Feis, I.: Fourier series approximation of separable models (2002)
  19. Fung, Wing Kam; He, Xuming; Liu, Li; Shi, Peide: Dimension reduction based on canonical correlation (2002)
  20. Kendall, Bruce E.: Cycles, chaos, and noise in predator-prey dynamics (2001)

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