Showing 6 of total 6 results (show query)
blasif
cocons:Covariate-Based Covariance Functions for Nonstationary Spatial Modeling
Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.
Maintained by Federico Blasi. Last updated 2 months ago.
covariance-matrixcppestimationgaussian-processeslarge-datasetnonstationarityoptimizationpredictioncpp
10.5 match 3 stars 5.48 score 1 scripts
rebeccasalles
TSPred:Functions for Benchmarking Time Series Prediction
Functions for defining and conducting a time series prediction process including pre(post)processing, decomposition, modelling, prediction and accuracy assessment. The generated models and its yielded prediction errors can be used for benchmarking other time series prediction methods and for creating a demand for the refinement of such methods. For this purpose, benchmark data from prediction competitions may be used.
Maintained by Rebecca Pontes Salles. Last updated 4 years ago.
benchmarkinglinear-modelsmachine-learningnonstationaritytime-series-forecasttime-series-prediction
10.0 match 24 stars 5.53 score 94 scripts 1 dependents
cran
BootWPTOS:Test Stationarity using Bootstrap Wavelet Packet Tests
Provides significance tests for second-order stationarity for time series using bootstrap wavelet packet tests. Provides functionality to visualize the time series with the results of the hypothesis tests superimposed. The methodology is described in Cardinali, A and Nason, G P (2016) "Practical powerful wavelet packet tests for second-order stationarity." Applied and Computational Harmonic Analysis, 44, 558-585 <doi:10.1016/j.acha.2016.06.006>.
Maintained by Guy Nason. Last updated 3 years ago.
1.9 match 1.00 score
markdrisser
convoSPAT:Convolution-Based Nonstationary Spatial Modeling
Fits convolution-based nonstationary Gaussian process models to point-referenced spatial data. The nonstationary covariance function allows the user to specify the underlying correlation structure and which spatial dependence parameters should be allowed to vary over space: the anisotropy, nugget variance, and process variance. The parameters are estimated via maximum likelihood, using a local likelihood approach. Also provided are functions to fit stationary spatial models for comparison, calculate the Kriging predictor and standard errors, and create various plots to visualize nonstationarity.
Maintained by Mark D. Risser. Last updated 7 years ago.
0.5 match 2 stars 2.70 score 25 scripts
