: Mixed GAM Computation Vehicle w'th GCV/AIC/REML Smoothness Estimation, R package version, 1.8-17.
The GAM fitting was carried out with the functions "mgcv
" library (Wood, 2001).
The software packages IBM SPSS Statistics for Windows, version 22.0 (IBM Corp.) and R version 3.0.2 (R Foundation for Statistical Computing; packages nlme, ppcor, mgcv
, segmented, Hmisc, ROCR, PredictABEL, and ICC) were used for statistical analyses, and the significance level was 0.05 for all models.
GAMs were fitted using the mgcv
library (Wood, 2006) in R (R Core Team, 2013).
All analyses were performed using the mgcv
package of the R software.
All model analyses were performed using R, version 2.5.0, using the mgcv
package, 1.3-24, which provides functions for GAMs (20).
The GAM procedure was performed using R software version 2.15.3  and specifically the "mgcv
" package .
We estimated the GAM using the MGCV
package in R (Wood 2006) except for Figure 1, which we prepared using Statistica (StatSoft 2010).
GAMs were performed using the default values in the mgcv
library of the statistical package R (version 2.3.1, www.r-project.org/).
(Wood 2000), and akima (Akima 1978) libraries.
All analyses were conducted using R (version 3.4.2; R Core Team) with the mgcv
package for fitting the GAM, the tlnise package for the Bayesian hierarchical model, and the metafor package for meta-regression analyses.
Modelling was performed through the MGCV
library of R.