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Found 12 papers, 5 papers with code
pasta: Pattern Analysis for Spatial Omics Data
Spatial omics assays allow for the molecular characterisation of cells in their spatial context.
A class of modular and flexible covariate-based covariance functions for nonstationary spatial modeling
The assumptions of stationarity and isotropy often stated over spatial processes have not aged well during the last two decades, partly explained by the combination of computational developments and the increasing availability of high-resolution spatial data.
Iterative Methods for Full-Scale Gaussian Process Approximations for Large Spatial Data
Gaussian processes are flexible probabilistic regression models which are widely used in statistics and machine learning.
BiasBed - Rigorous Texture Bias Evaluation
With the release of BiasBed, we hope to foster a common understanding of consistent and meaningful comparisons, and consequently faster progress towards learning methods free of texture bias.
BiasBed -- Rigorous Texture Bias Evaluation
With the release of BiasBed, we hope to foster a common understanding of consistent and meaningful comparisons, and consequently faster progress towards learning methods free of texture bias.
Joint Variable Selection of both Fixed and Random Effects for Gaussian Process-based Spatially Varying Coefficient Models
It relies on a penalized maximum likelihood estimation (PMLE) and allows variable selection both with respect to fixed effects and Gaussian process random effects.
Variable Selection
Methodology
Additive Bayesian Network Modelling with the R Package abn
The R package abn is designed to fit additive Bayesian models to observational datasets.
Is a single unique Bayesian network enough to accurately represent your data?
Unfortunately, they essentially all rely on very crude decisions that result in too simplistic approaches for such complex systems.
Comparison between Suitable Priors for Additive Bayesian Networks
The second prior belongs to the Student's t-distribution, specifically designed for logistic regressions and, finally, the strongly informative prior is again Gaussian with mean equal to true parameter value and a small variance.
Information-Theoretic Scoring Rules to Learn Additive Bayesian Network Applied to Epidemiology
Bayesian network modelling is a well adapted approach to study messy and highly correlated datasets which are very common in, e. g., systems epidemiology.
optimParallel: an R Package Providing Parallel Versions of the Gradient-Based Optimization Methods of optim()
The R package optimParallel provides a parallel version of the gradient-based optimization methods of optim().
Computation
varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets
This article describes the R package varrank.
