This package is designed for time series data.  Uses fast
implementations to estimate Vector Autoregressive models and
Vector Autoregressive models with Exogenous Inputs.  For
speedup, fastVAR can use multiple cpu cores to calculate the
estimates.  For very large systems, fastVAR uses Lasso penalty
to return very sparse coefficient matrices. Regression
diagnostics can be used to compare models, and prediction
functions can be used to calculate the n-step ahead prediction.
Map-Reduce functions are in the works (Beta) for estimating
large VAR models on a compute cluster.

This package is designed for time series data.  Fits
Vector Autoregressive models and Vector Autoregressive models
with Exogenous Inputs.  For speedup, fastVAR can use multiple
cpu cores to calculate the estimates.  For very large systems,
fastVAR uses Lasso penalty to return very sparse coefficient
matrices.  Regression diagnostics can be used to compare
models, and prediction functions can be used to calculate the
n-step ahead prediction.  Faster implementations in C coming
soon.

Compute Maximal Overlap Discrete Wavelet Transform (MODWT) and À
Trous Discrete Wavelet Transform
c(
person("John", "Quilty", , "jquilty@uwaterloo.ca", role = c("aut"), comment = c(ORCID = "0000-0002-0207-8077")),
person("John", "You", , "johnswyou@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-3509-0312")))
A lightweight package to compute Maximal Overlap Discrete Wavelet
Transform (MODWT) and À Trous Discrete Wavelet Transform by leveraging the
power of 'Rcpp' to make these operations fast.
See Maheswaran and Khosa (2012) <doi:10.1016/j.cageo.2011.12.015>.

A lightweight package to compute Maximal Overlap Discrete Wavelet
Transform (MODWT) and À Trous Discrete Wavelet Transform by leveraging the
power of 'Rcpp' to make these operations fast. This package was designed for use in forecasting, and
allows users avoid the inclusion of future data when performing wavelet decomposition of time series.
See Quilty and Adamowski (2018) <doi:10.1016/j.jhydrol.2018.05.003>.