itsadug: Interpreting Time Series and Autocorrelated Data Using GAMMs
By Jacolien van Rij, Martijn Wieling, R. Harald Baayen, H. van Rijn, Maintainer Jacolien, Van Rij. 2015
490 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/cabb2bc589276a41ddb29c59e27e0daea4c14e34
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Harald Baayen
- Shravan Vasishth
- Douglas M. Bates
- Reinhold Kliegl
- Martijn B. Wieling
- Keith Rayner
- Lena A. Jäger
- Christopher Eager
Citations in the corpus
- Analyzing dynamic phonetic data using generalized additive mixed modeling: A tutorial focusing on articulatory differences between L1 and L2 speakers of English (2018, methodology): “With respect to the actual modeling, we have used the R package mgcv (Wood 2011, 2017) for model fitting, and the R package itsadug (van Rij et al., 2017) for visualizing most of the resulting patterns.”
- The cave of Shadows. Addressing the human factor with generalized additive mixed models (2015, methodology): “The analyses in this study were carried out with the help of the mgcv package, version 1.8-12 (Wood, 2006, 2011) and the itsadug package (van Rij et al., 2016) for R (version 3.2.2, R Core Team, 2015).3 These analyses are all exploratory, in that a sequence of increasingly complex models was…”
- Neural correlates of processing case in adults and children. (2025, unknown): “Statistical analyses were performed in R, using the package mgcv (Wood, 2004, 2011) for modeling and the packages itsadug (van Rij, Wieling, Baayen, & van Rijn, 2020) and erpR (Arcara & Petrova, 2014) for plotting.”