Bi-Conditional Likelihood Estimation for Spatial Data
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Updated
Jan 20, 2024 - R
Bi-Conditional Likelihood Estimation for Spatial Data
River bathymetry interpolation methodology using R
Radial neighbors GP
Statistical profiling (ANOVA, linear-regression, variogram, etc) for geostatistics, stock market, etc
Streamlined Model-based Geostatistics Functions for the SEEG Research Group
Synced-up chart location data side by side with animations
An R implementation of the DSMART algorithm
Project and report files for my MSc capstone at Oregon State: Comparison of Gaussian copula and random forests in spatial prediction
Spatial Analysis Book
Geostatistical inference under preferential sampling
Valizas river (Uruguay - Lat: -34.358407, Lon: -53.844792) bathymetry model from BioSonics DT-X echosounder measured depths.
A geospatial analysis used in my Masterthesis. Its a Least Cost Path analysis written in R that tries to identify the most likely routes camel caravans took to cross ancient Arabia
Kriging assignment for map generation using spatial interpolation
Comparison of spatial Gaussian copula and spatial random forests
SAMUEL-ROSA, A. et al. Do more detailed environmental covariates deliver more accurate soil maps? Geoderma, v. 243–244, p. 214–227, maio 2015.
Mapping of groundwater level for realistic flow flowpaths using semi-automated kriging.
Mandatory work for Introduction to Geostatistics course on University of Buenos Aires (UBA)
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