Under- and over-dispersed binary data are modeled using an extended Poisson 
 process model (EPPM) appropriate for binary data. A feature of the model is that the 
 under-dispersion relative to the binomial distribution only needs to be greater than
 zero, but the over-dispersion is  restricted compared to other distributional models  
 such as the beta and correlated binomials. Because of this, the examples focus on 
 under-dispersed data and how, in combination with the beta or correlated distributions,
 flexible models can be fitted to data displaying both under- and over-dispersion. Using
 Generalized Linear Model (GLM)  terminology, the functions utilize linear predictors for
 the probability of success and scale-factor with various link functions for p, and log 
 link for scale-factor, to fit a variety of models relevant to areas such as bioassay. 
 Details of the EPPM are in Faddy and Smith (2012) <doi:10.1002/bimj.201100214> and 
 Smith and Faddy (2019) <doi:10.18637/jss.v090.i08>.

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>.