Multivariate time series data imputation This is a 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 modelled using an ARIMA(p,d,q), optionally with seasonal components, a non-parametric cubic spline or generalised additive models with exogenous covariates. This algorithm is specially tailored for climate data with missing measurements from several monitors along a given region. Software Depends: R (>= 2.11.0),stats,gam,splines Washington Junger <wjunger@ims.uerj.br> Washington Junger <wjunger@ims.uerj.br> and Antonio Ponce de Leon <ponce@ims.uerj.br> GPL (>= 2) 2010-11-24 application/tgz http://CRAN.R-project.org/package=mtsdi