Showing 6 of total 6 results (show query)
bhklab
mRMRe:Parallelized Minimum Redundancy, Maximum Relevance (mRMR)
Computes mutual information matrices from continuous, categorical and survival variables, as well as feature selection with minimum redundancy, maximum relevance (mRMR) and a new ensemble mRMR technique. Published in De Jay et al. (2013) <doi:10.1093/bioinformatics/btt383>.
Maintained by Benjamin Haibe-Kains. Last updated 4 years ago.
19 stars 8.79 score 105 scripts 2 dependents
gregorkastner
stochvol:Efficient Bayesian Inference for Stochastic Volatility (SV) Models
Efficient algorithms for fully Bayesian estimation of stochastic volatility (SV) models with and without asymmetry (leverage) via Markov chain Monte Carlo (MCMC) methods. Methodological details are given in Kastner and Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002> and Hosszejni and Kastner (2019) <doi:10.1007/978-3-030-30611-3_8>; the most common use cases are described in Hosszejni and Kastner (2021) <doi:10.18637/jss.v100.i12> and Kastner (2016) <doi:10.18637/jss.v069.i05> and the package examples.
Maintained by Darjus Hosszejni. Last updated 5 months ago.
15 stars 7.69 score 90 scripts 8 dependents
matthieu-bruneaux
isotracer:Isotopic Tracer Analysis Using MCMC
Implements Bayesian models to analyze data from tracer addition experiments. The implemented method was originally described in the article "A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020) <doi:10.1086/708546>.
Maintained by Matthieu Bruneaux. Last updated 5 months ago.
5.92 score 60 scripts
edsandorf
spdesign:Designing Stated Preference Experiments
Contemporary software commonly used to design stated preference experiments are expensive and the code is closed source. This is a free software package with an easy to use interface to make flexible stated preference experimental designs using state-of-the-art methods. For an overview of stated choice experimental design theory, see e.g., Rose, J. M. & Bliemer, M. C. J. (2014) in Hess S. & Daly. A. <doi:10.4337/9781781003152>. The package website can be accessed at <https://spdesign.edsandorf.me>. We acknowledge funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant INSPiRE (Grant agreement ID: 793163).
Maintained by Erlend Dancke Sandorf. Last updated 6 months ago.
4.60 score 20 scripts
iiasa
ibis.iSDM:Modelling framework for integrated biodiversity distribution scenarios
Integrated framework of modelling the distribution of species and ecosystems in a suitability framing. This package allows the estimation of integrated species distribution models (iSDM) based on several sources of evidence and provided presence-only and presence-absence datasets. It makes heavy use of point-process models for estimating habitat suitability and allows to include spatial latent effects and priors in the estimation. To do so 'ibis.iSDM' supports a number of engines for Bayesian and more non-parametric machine learning estimation. Further, the 'ibis.iSDM' is specifically customized to support spatial-temporal projections of habitat suitability into the future.
Maintained by Martin Jung. Last updated 5 months ago.
bayesianbiodiversityintegrated-frameworkpoisson-processscenariossdmspatial-grainspatial-predictionsspecies-distribution-modelling
21 stars 4.36 score 12 scripts 1 dependents
ksbakar
spTDyn:Spatially Varying and Spatio-Temporal Dynamic Linear Models
Fits, spatially predicts, and temporally forecasts space-time data using Gaussian Process (GP): (1) spatially varying coefficient process models and (2) spatio-temporal dynamic linear models. Bakar et al., (2016). Bakar et al., (2015).
Maintained by K. Shuvo Bakar. Last updated 7 months ago.
1 stars 1.82 score 22 scripts 1 dependents