Geometric Density Estimation (RGeode)
Provides the hybrid Bayesian method Geometric Density Estimation. On the one hand, it scales the dimension of our data, on the other it performs inference. The method is fully described in the paper ‘Scalable Geometric Density Estimation’ by Y. Wang, A. Canale, D. Dunson (2016) <http://…/wang16e.pdf>.

High Performance Algorithms for Vine Copula Modeling (rvinecopulib)
Provides an interface to ‘vinecopulib’, a high performance C++ library based on ‘Boost’, ‘Eigen’ and ‘NLopt’. It provides high-performance implementations of the core features of the popular ‘VineCopula’ package, in particular inference algorithms for both vine copula and bivariate copula models. Advantages over VineCopula are a sleaker and more modern API, shorter runtimes, especially in high dimensions, nonparametric and multi-parameter families.

Estimate Percentiles from an Ordered Categorical Variable (perccalc)
An implementation of two functions that estimate values for percentiles from an ordered categorical variable as described by Reardon (2011, isbn:978-0-87154-372-1). One function estimates percentile differences from two percentiles while the other returns the values for every percentile from 1 to 100.

Core Inflation (Inflation)
Provides access to core inflation functions. Four different core inflation functions are provided. The well known trimmed means, exclusion and double weighing methods, alongside the new Triple Filter method introduced in Ferreira et al. (2016) <https://goo.gl/UYLhcj>.

Polygonal Symbolic Data Analysis (psda)
An implementation of symbolic polygonal data analysis. The package presents the estimation of main descriptive statistical measures, e.g, mean, covariance, variance, correlation and coefficient of variation. In addition, transformation of the data in polygons. Empirical probability distribution function based on polygonal histogram and regression models are presented.

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