[R] ssa gapfill of series with large window and sparse gaps with Rssa

I have several years of univariate wind speed data to which I would like to apply singular spectrum analysis. The data are sampled every 15min and a year is a fundamental periodicity, which suggests L=35,040 values. I would like to fill the gaps. The missing values are scattered at low density

[R] disparate data collections and resolutions in a GAM

Hi, I have point values of elevations on land (high resolution lidar) and in the water (some are lower resolution single beam soundings or even just prior elevation maps, others are high res multibeam). Let's say high resolution is 1m and low is 10m, although the coarse case can be worse.

[R] Multichannel reconstruction in Rssa

Hi. I have been trying to use the Rssa singular spectrum analysis library on a dataset of 15 minute data at four (cohesive) water temperature stations in an estuary. The stations have different missing data patterns, though I can find a period of 2 years where all four are mostly present. There

[R] concentrated likelihood in dlm

Hi, I am trying to do maximum likelihood estimation on a univariate structural model with diffuse components in dlm. The package already has an MLE function, but I would like to implement two enhancements, both of which are discussed in Harvey's Forecasting structural time series models and

[R] Mixtures of smooths on the same covariate in mgcv?

Hi all, I have observations that mix together contributions from several smooth functions of time. These smooths represent fluxes on a graph. Some of the observations are centered on the edges of the graph, and directly measure one flux. The others measurements are centered on the nodes of the

[R] Principal component / EOF analysis of data dominated by a couple frequencies

Hi R folk, I have a time series of scalar downstream velocity data measured across a tidal channel. The variables are the locations in bins across the channel, the samples are over time. The fluctuation over the tide cycle is an enormous fraction of the time variation in the data ... 96%. The