Package: mvLSWimpute 0.1.1

Matt Nunes

mvLSWimpute: Imputation Methods for Multivariate Locally Stationary Time Series

Implementation of imputation techniques based on locally stationary wavelet time series forecasting methods from Wilson, R. E. et al. (2021) <doi:10.1007/s11222-021-09998-2>.

Authors:Rebecca Wilson [aut], Matt Nunes [aut, cre], Idris Eckley [ctb, ths], Tim Park [ctb]

mvLSWimpute_0.1.1.tar.gz
mvLSWimpute_0.1.1.tar.gz(r-4.5-noble)mvLSWimpute_0.1.1.tar.gz(r-4.4-noble)
mvLSWimpute_0.1.1.tgz(r-4.4-emscripten)mvLSWimpute_0.1.1.tgz(r-4.3-emscripten)
mvLSWimpute.pdf |mvLSWimpute.html✨
mvLSWimpute/json (API)

# Install 'mvLSWimpute' in R:

install.packages('mvLSWimpute', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 1 scripts 192 downloads 10 exports 66 dependencies

Last updated 2 years agofrom:f2de33b3c4. Checks:OK: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Dec 08 2024
R-4.5-linux	OK	Dec 08 2024

Exports:correct_per form_lacv_backward form_lacv_forward haarWT mv_impute pdef pred_eq_backward pred_eq_forward smooth_per spec_estimation

Dependencies:adlift binhf cli colorspace commonmark curl dotCall64 EbayesThresh fansi farver fields forecast fracdiff generics ggplot2 ggtext glue gridtext gtable imputeTS isoband jpeg jsonlite labeling lattice lifecycle lmtest magrittr maps markdown MASS Matrix mgcv munsell mvLSW nlme nnet pillar pkgconfig png quadprog quantmod R6 RColorBrewer Rcpp RcppArmadillo rlang scales spam stinepack stringi stringr tibble timeDate tseries TTR urca utf8 vctrs viridisLite wavethresh withr xfun xml2 xts zoo

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Readme and manuals

Help Manual

Help page	Topics
Imputation Methods for Multivariate Locally Stationary Time Series	mvLSWimpute-package mvLSWimpute
Function to smooth the raw wavelet periodogram	correct_per
Function to form the local autocovariance array for the forecasting / backcasting step.	form_lacv_backward form_lacv_forward
Function to apply the (univariate) Haar wavelet transform	haarWT
Function to apply the mvLSWimpute method and impute missing values in a multivariate locally stationary time series	mv_impute
Function to regularise the LWS matrix.	pdef
Function to form the prediction equations for the forecasting / backcasting step.	pred_eq_backward pred_eq_forward
Function to smooth the raw wavelet periodogram using the default 'mvLSW' routine.	smooth_per
Function to estimate the Local Wavelet Spectral matrix for a multivariate locally stationary time series containing missing values	spec_estimation

Package: mvLSWimpute 0.1.1

mvLSWimpute: Imputation Methods for Multivariate Locally Stationary Time Series

Citation

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)