Description: 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

Title: Compute Maximal Overlap Discrete Wavelet Transform (MODWT) and À
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")))
Description: 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.
Author: John Quilty [aut] (<https://orcid.org/0000-0002-0207-8077>),

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>.

Title: Fast Adaptive Shrinkage/Thresholding Algorithm
person(given="Tom", family="Goldstein", email="tomg@cs.umd.edu", role="aut", comment = "MATLAB original, https://www.cs.umd.edu/~tomg/projects/fasta/"),
person(given=c("Richard", "G."), family="Baraniuk", email="richb@rice.edu", role="aut")
Description: A collection of acceleration schemes for proximal gradient methods for estimating penalized regression parameters described in
Goldstein, Studer, and Baraniuk (2016) <arXiv:1411.3406>. Schemes such as Fast Iterative Shrinkage and Thresholding Algorithm (FISTA) by Beck and Teboulle (2009) <doi:10.1137/080716542>
and the adaptive stepsize rule introduced in Wright, Nowak, and Figueiredo (2009) <doi:10.1109/TSP.2009.2016892> are included. You provide the objective function and proximal mappings, and it takes care of the issues like stepsize selection, acceleration, and stopping conditions for you.
Author: Eric C. Chi [aut, cre, trl, cph],