International audienceThe problem of estimating (from n noisy observations of a single realization, at known sites) the parameters of a centered stationary Gaussian process whose autocorrelation function belongs to the Matérn class appears in many contexts (e.g. [1, 2, 3]). The recently proposed CGEM-EV method [4] only requires the computation of several conditional means, at the observation sites, corresponding to candidate values for the Matérn parameters. In dimension 1 and when the “Matérn differentiability” parameter is fixed to k + 1/2 with k integer (an often-used value is k = 0 or k = 1, see e.g. [3], [1], [6], [7], [8]), each of these conditional means reduces to a Kalman smoothing.An R implementation of CGEM-EV for this context is presented : it is built on the R-package dlm [5]. It proves to be quite fast, even for high-frequency sampling (e.g. n = 8196), and an empirical comparison with the classical maximum likelihood estimator confirms the near- efficiency results of [4]
