A simple implementation for creating a Meta-learning rule using spatial statistical parameters of a dataset
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Updated
Jan 16, 2023 - Python
A simple implementation for creating a Meta-learning rule using spatial statistical parameters of a dataset
This is an interactive app (run on local computer) to visualize neural likelihood surfaces from the paper "Neural Likelihood Surfaces for Spatial Processes with Computationally Intensive or Intractable Likelihoods"
A Python wrapper for Fragstats.
Intyearpolator is, as its name suggests, a spatial interpolator designed for predicting years of a random field.
Python package to perform Tan et al. (2014)'s analysis of distance
Sample a repelled point process, compute a Monte Carlo estimation for the integral of a function using various variants of the Monte Carlo method including the Monte Carlo with a repelled point process, and visualize gravitational allocations 2D.
Provide functions for computing multi-scale, multi-tapers estimator of the hyperuniformity exponent and associated asymptotic confidence intervals.
A package for fast bayesian analysis of single season spatial occupancy models.
Spatial effects in network measures of spatially embedded networks
Code for ridesharing paper @ WWW 2018.
Compute structure factor of stationary and isotropic point processes
Spatial modeling using machine learning concepts
SParse Generalized Linear Models (spglm)
Pieces of code that have appeared on my blog with a focus on stochastic simulations.
Spatial econometric regression in Python
Core components of Python Spatial Analysis Library
Kriging Toolkit for Python
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