Allows the estimation and prediction for binary Gaussian process model. The mean function can be assumed to have time-series structure. The estimation methods for the unknown parameters are based on penalized quasi-likelihood/penalized quasi-partial likelihood and restricted maximum likelihood. The predicted probability and its confidence interval are computed by Metropolis-Hastings algorithm. More details can be seen in Sung et al (2017) <arXiv:1705.02511>.

Tools for building Rescorla-Wagner Models for Two-Alternative 
  Forced Choice tasks, commonly employed in psychological research. 
  Most concepts and ideas within this R package are referenced from 
  Sutton and Barto (2018) <ISBN:9780262039246>. 
  The package allows for the intuitive definition of RL models using simple 
  if-else statements and three basic models built into this R package are 
  referenced from 
  Niv et al. (2012)<doi:10.1523/JNEUROSCI.5498-10.2012>. 
  Our approach to constructing and evaluating these computational models 
  is informed by the guidelines proposed in 
  Wilson & Collins (2019) <doi:10.7554/eLife.49547>. 
  Example datasets included with the package are sourced from the work of
  Mason et al. (2024) <doi:10.3758/s13423-023-02415-x>.

A collection of 'LaTeX' styles using 'Beamer' customization for
 pdf-based presentation slides in 'RMarkdown'. At present it contains
 'RMarkdown' adaptations of the LaTeX themes 'Metropolis' (formerly 'mtheme')
 theme by Matthias Vogelgesang and others (now included in 'TeXLive'), the
 'IQSS' by Ista Zahn (which is included here), and the 'Monash' theme by
 Rob J Hyndman. Additional (free) fonts may be needed: 'Metropolis' prefers
 'Fira', and 'IQSS' requires 'Libertinus'.

Estimate the correlation between two irregular time series that are not necessarily sampled on identical time points. This program is also applicable to the situation of two evenly spaced time series that are not on the same time grid. 'BINCOR' is based on a novel estimation approach proposed by Mudelsee (2010, 2014) to estimate the correlation between two climate time series with different timescales. The idea is that autocorrelation (AR1 process) allows to correlate values obtained on different time points. 'BINCOR' contains four functions: bin_cor() (the main function to build the binned time series), plot_ts() (to plot and compare the irregular and binned time series, cor_ts() (to estimate the correlation between the binned time series) and ccf_ts() (to estimate the cross-correlation between the binned time series).