Bayesian Inference of State Space Models (bssm)
Efficient methods for Bayesian inference of state space models via particle Markov chain Monte Carlo and importance sampling type corrected Markov chain Monte Carlo. Gaussian, Poisson, binomial, or negative binomial observation densities and Gaussian state dynamics, as well as general non-linear Gaussian models are supported.

Generalized Framework for Cross-Validation (origami)
Provides a general framework for the application of cross-validation schemes to particular functions. By allowing arbitrary lists of results, origami accommodates a range of cross-validation applications.

Estimation and Prediction of Skewed Spatial Processes (DZEXPM)
A collection of functions designed to estimate and predict skewed spatial processes, and a real data set.

Nonlinear Conditional Independence Tests (CondIndTests)
Code for a variety of nonlinear conditional independence tests: Kernel conditional independence test (Zhang et al., UAI 2011, <arXiv:1202.3775>), Residual Prediction test (based on Shah and Buehlmann, <arXiv:1511.03334>), Invariant environment prediction, Invariant target prediction, Invariant residual distribution test, Invariant conditional quantile prediction (all from Heinze-Deml et al., <arXiv:1706.08576>).

Experiment Repetitions (ExpRep)
Allows to calculate the probabilities of occurrences of an event in a great number of repetitions of Bernoulli experiment, through the application of the local and the integral theorem of De Moivre Laplace, and the theorem of Poisson. Gives the possibility to show the results graphically and analytically, and to compare the results obtained by the application of the above theorems with those calculated by the direct application of the Binomial formula. Is basically useful for educational purposes.