varmapack

varmapack simulates and analyzes Gaussian VAR, VMA, VARMA, and VARMAX time-series models. Simulated stationary series have the correct distribution from the first returned term, without discarding a burn-in segment. The package also provides model testcases, theoretical and sample autocovariances, covariance-to-correlation conversion, spectral radii, and impulse response functions.

Documentation

The Getting Started with Varmapack vignette shows model construction, simulation, testcases, and analysis. The Mathematical Description of Varmapack vignette defines the supported models and describes the simulation method.

After installing the CRAN release, open the vignettes with vignette("getting-started", package = "varmapack") and vignette("mathematical-description", package = "varmapack"). The installed reference documentation is available through help(package = "varmapack"). For information about the underlying C library, see the C README.

Installation

The package will be installable from CRAN when released:

install.packages("varmapack")

For the development version, install the companion randompack package first:

remotes::install_github("jonasson2/randompack", subdir = "r-package")
remotes::install_github("jonasson2/varmapack", subdir = "r-package")

Example

model <- varmapack_testcase("smallARMA1")
gamma <- model$acvf(maxlag = 2)
corr <- varmapack_cov2corr(gamma)

varmapack_cov2corr() converts theoretical or sample autocovariances to correlations by dividing each entry by the product of the corresponding lag-zero marginal standard deviations. The returned array has the same shape as the input. Lag-zero diagonal entries are exactly one. Other entries are not clipped to [-1,1], so correlations obtained from lag-corrected sample autocovariances may lie outside that interval.