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