Provides computationally efficient tools related to the multivariate normal and Student's t distributions. The main functionalities are: simulating multivariate random vectors, evaluating multivariate normal or Student's t densities and Mahalanobis distances. These tools are very efficient thanks to the use of C++ code and of the OpenMP API.

Helps you imagine your data before you collect it. Hierarchical data structures and correlated data can be easily simulated, either from random number generators or by resampling from existing data sources.

Implements optimal matching with near-fine balance in large observational studies with the use of optimal calipers to get a sparse network. The caliper is optimal in the sense that it is as small as possible such that a matching exists.

A nonparametric Bayesian approach to survival analysis. The functions perform inference via MCMC simulations from the posterior distributions for a Dependent Dirichlet Process-Gaussian Process prior.

Improves the balance of optimal matching with near-fine balance by giving penalties on the unbalanced covariates with the unbalanced directions. Many directional penalties can also be viewed as Lagrange multipliers, pushing a matched sample in the ...

Contains functions for the MCMC simulation of dyadic network models j2 (Zijlstra, 2017, <doi:10.1080/0022250X.2017.1387858>) and p2 (Van Duijn, Snijders & Zijlstra, 2004, <doi: 10.1046/j.0039-0402.2003.00258.x>) as described in Zijlstra, Van Duijn & ...

Maximum likelihood estimation of Gaussian Mixture Vector Autoregressive (GMVAR) model, quantile residual tests, graphical diagnostics, forecasting and simulations. Applying general linear constraints to the autoregressive parameters is supported.

An implementation of the modelling and reporting features described in reference textbooks and guidelines: deterministic and probabilistic sensitivity analysis, heterogeneity analysis, time dependency on state-time and model-time (semi-Markov and ...

Implementation of the HorseRule model a flexible tree based Bayesian regression method for linear and nonlinear regression described in Nalenz & Villani (2017) <arXiv:1702.05008>.

Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2018) <arXiv:1701.07010v4>.

Fits a family of mixtures of multivariate t-distributions under a continuous t-distributed latent variable structure for the purpose of clustering or classification. The alternating expectation-conditional maximization algorithm is used for parameter ...