Rlibeemd: Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN)

An R interface for libeemd (Luukko, Helske, Räsänen, 2016) doi:10.1007/s00180-015-0603-9, a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). Due to the possible portability issues CRAN version no longer supports OpenMP, you can install OpenMP-supported version from GitHub: https://github.com/helske/Rlibeemd/.

tsPI: Improved Prediction Intervals for ARIMA Processes and Structural Time Series

Prediction intervals for ARIMA and structural time series models using importance sampling approach with uninformative priors for model parameters, leading to more accurate coverage probabilities in frequentist sense. Instead of sampling the future observations and hidden states of the state space representation of the model, only model parameters are sampled, and the method is based solving the equations corresponding to the conditional coverage probability of the prediction intervals. This makes method relatively fast compared to for example MCMC methods, and standard errors of prediction limits can also be computed straightforwardly.

ramcmc: Robust Adaptive Metropolis Algorithm

Function for adapting the shape of the random walk Metropolis proposal as specified by robust adaptive Metropolis algorithm by Vihola (2012) doi:10.1007/s11222-011-9269-5. The package also includes fast functions for rank-one Cholesky update and downdate. These functions can be used directly from R or the corresponding C++ header files can be easily linked to other R packages.

diagis: Diagnostic Plot and Multivariate Summary Statistics of Weighted Samples from Importance Sampling

Fast functions for effective sample size, weighted multivariate mean and variance computation, and weight diagnostic plot for generic importance sampling type results.

changer: Change R Package Name

Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task.