Package: mtsdi 0.3.6

mtsdi: Multivariate Time Series Data Imputation

This is an EM algorithm based method for imputation of missing values in multivariate normal time series. The imputation algorithm accounts for both spatial and temporal correlation structures. Temporal patterns can be modeled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalized additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region.

Authors:Washington Junger <[email protected]>

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Peer review:

Bug tracker:https://github.com/wjunger/mtsdi/issues

On CRAN:

38 exports 1 stars 1.65 score 4 dependencies 3 dependents 1 mentions 19 scripts 301 downloads

Last updated 2 years agofrom:8c1a6be55d. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:.onUnloadedaprepelapsedtimeem.arimaem.arrangematem.arrangevecem.contribt1em.contribt2em.correctmatem.countmnearem.countmvecem.detem.dispersionem.existnaem.extractcoordem.filterem.gamem.meanem.nofilterem.partmuem.partsigmaem.putvalueem.rearrangematem.rearrangevecem.recursionem.replacewmeanem.splineem.splitvecem.tracegetmeanmkjnwmnimputmstatsplot.mtsdipredict.mtsdiprint.mtsdiprint.summary.mtsdisummary.mtsdi

Dependencies:codetoolsforeachgamiterators