Showing 5 of total 5 results (show query)
babaknaimi
sdm:Species Distribution Modelling
An extensible framework for developing species distribution models using individual and community-based approaches, generate ensembles of models, evaluate the models, and predict species potential distributions in space and time. For more information, please check the following paper: Naimi, B., Araujo, M.B. (2016) <doi:10.1111/ecog.01881>.
Maintained by Babak Naimi. Last updated 2 months ago.
24 stars 9.31 score 312 scripts 1 dependents
darunabas
phyloregion:Biogeographic Regionalization and Macroecology
Computational infrastructure for biogeography, community ecology, and biodiversity conservation (Daru et al. 2020) <doi:10.1111/2041-210X.13478>. It is based on the methods described in Daru et al. (2020) <doi:10.1038/s41467-020-15921-6>. The original conceptual work is described in Daru et al. (2017) <doi:10.1016/j.tree.2017.08.013> on patterns and processes of biogeographical regionalization. Additionally, the package contains fast and efficient functions to compute more standard conservation measures such as phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness and global endangerment, as well as compositional turnover (e.g., beta diversity).
Maintained by Barnabas H. Daru. Last updated 5 months ago.
18 stars 7.21 score 50 scripts 1 dependents
venpopov
bmm:Easy and Accessible Bayesian Measurement Models Using 'brms'
Fit computational and measurement models using full Bayesian inference. The package provides a simple and accessible interface by translating complex domain-specific models into 'brms' syntax, a powerful and flexible framework for fitting Bayesian regression models using 'Stan'. The package is designed so that users can easily apply state-of-the-art models in various research fields, and so that researchers can use it as a new model development framework. References: Frischkorn and Popov (2023) <doi:10.31234/osf.io/umt57>.
Maintained by Vencislav Popov. Last updated 26 days ago.
15 stars 5.92 score 35 scripts
tkrisztin
estimateW:Estimation of Spatial Weight Matrices
Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial Durbin (SDM), and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) <doi:10.1080/17421772.2022.2095426>.
Maintained by Tamas Krisztin. Last updated 2 years ago.
2.70 score 2 scripts
cran
CGE:Computing General Equilibrium
Developing general equilibrium models, computing general equilibrium and simulating economic dynamics with structural dynamic models in LI (2019, ISBN: 9787521804225) "General Equilibrium and Structural Dynamics: Perspectives of New Structural Economics. Beijing: Economic Science Press". When developing complex general equilibrium models, GE package should be used in addition to this package.
Maintained by LI Wu. Last updated 5 years ago.
2.48 score 1 dependents