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paulojus

geoR:Analysis of Geostatistical Data

Geostatistical analysis including variogram-based, likelihood-based and Bayesian methods. Software companion for Diggle and Ribeiro (2007) <doi:10.1007/978-0-387-48536-2>.

Maintained by Paulo Justiniano Ribeiro Jr. Last updated 1 years ago.

10 stars 7.57 score 1.8k scripts 12 dependents

obreschkow

cooltools:Practical Tools for Scientific Computations and Visualizations

Collection of routines for efficient scientific computations in physics and astrophysics. These routines include utility functions, numerical computation tools, as well as visualisation tools. They can be used, for example, for generating random numbers from spherical and custom distributions, information and entropy analysis, special Fourier transforms, two-point correlation estimation (e.g. as in Landy & Szalay (1993) <doi:10.1086/172900>), binning & gridding of point sets, 2D interpolation, Monte Carlo integration, vector arithmetic and coordinate transformations. Also included is a non-exhaustive list of important constants and cosmological conversion functions. The graphics routines can be used to produce and export publication-ready scientific plots and movies, e.g. as used in Obreschkow et al. (2020, MNRAS Vol 493, Issue 3, Pages 4551–4569). These routines include special color scales, projection functions, and bitmap handling routines.

Maintained by Danail Obreschkow. Last updated 4 months ago.

cpp

1 stars 3.17 score 21 scripts

cran

SpatialML:Spatial Machine Learning

Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>). Allows for a geographically weighted random forest regression including a function to find the optical bandwidth. (Georganos and Kalogirou (2022) <https://www.mdpi.com/2220-9964/11/9/471>).

Maintained by Stamatis Kalogirou. Last updated 12 months ago.

8 stars 2.20 score

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