To establish a model with a Gaussian process that emulates the code, the user has now three possibilities. The first, he possesses a code written in R but no DOE. The second, he possesses a code written in R and wants to enforce its own DOE. Then, he has no code but possesses the DOE and the corresponding output of a code.
A pipe is defined to parametrize a statistical model.
The function plot for parametrized model takes now only two arguments and is simpler than in version 0.0.1
A fonction forecast replaces the function prediction which did not work very very. The new function forecast allows to predict, based on realized calibration, over a new data set the behaviour of the model.
Two functions chain and estimators are added to get access to the MCMC chains and the MAP and Mean a posteriori estimator of a calibrated object.
The ggplots given for the plot of the calibrated object has changed and now to graphics layout are given. The user is also free to load the ggplots into a variable and draw the graph he wants.
A function that improves calibration for the second and the fourth model have also been written and is called sequentialDesign.
Bug fixes
The likelihoods written in the models are fixed
The R6 classes to create the statistical model have been simplified to have a more robust programmation