State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
Documentation: Downloads: Reverse dependencies: Reverse depends: CausalMBSTS Reverse imports: cbsREPS, countSTAR, MARSS, mbsts, RGAP, sectorgap, tsgc, tsPI, TSPred, tspredit, walker Reverse suggests: bssm, ggfortify, sarima Linking:Please use the canonical form https://CRAN.R-project.org/package=KFAS to link to this page.
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