assist

assist: A Suite of R Functions Implementing Spline Smoothing Techniques. A comprehensive package for fitting various non-parametric/semi-parametric linear/nonlinear fixed/mixed smoothing spline models.

Keywords for this software

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References in zbMATH (referenced in 11 articles )

Showing results 1 to 11 of 11.
Sorted by year (citations)

  1. Helwig, Nathaniel E.: Efficient estimation of variance components in nonparametric mixed-effects models with large samples (2016)
  2. Cheng, Guang; Shang, Zuofeng: Joint asymptotics for semi-nonparametric regression models with partially linear structure (2015)
  3. Arribas-Gil, Ana; Bertin, Karine; Meza, Cristian; Rivoirard, Vincent: Lasso-type estimators for semiparametric nonlinear mixed-effects models estimation (2014)
  4. Shang, Zuofeng; Cheng, Guang: Local and global asymptotic inference in smoothing spline models (2013)
  5. Cheng, Chin-I.; Speckman, Paul L.: Bayesian smoothing spline analysis of variance (2012)
  6. Wand, M. P.: Book review of: Y. Wang, Smoothing splines. Methods and applications (2012)
  7. Connolly, Robert A.; Rendleman, Richard J. jun.: Skill, luck, and streaky play on the PGA tour (2008)
  8. Liu, Anna; Wang, Yuedong: Modeling of hormone secretion-generating mechanisms with splines: a pseudo-likelihood approach (2007)
  9. Yang, Yu-Chieh; Liu, Anna; Wang, Yuedong: Detecting pulsatile hormone secretions using nonlinear mixed effects partial spline models (2006)
  10. Kim, Young-Ju; Gu, Chong: Smoothing spline Gaussian regression: more scalable computation via efficient approximation (2004)
  11. Wang, Yuedong; Ke, Chunlei; Brown, Morton B.: Shape-invariant modeling of circadian rhythms with random effects and smoothing spline ANOVA decompositions (2003)