Showing 5 of total 5 results (show query)
davidrusi
mombf:Model Selection with Bayesian Methods and Information Criteria
Model selection and averaging for regression and mixtures, inclusing Bayesian model selection and information criteria (BIC, EBIC, AIC, GIC).
Maintained by David Rossell. Last updated 2 months ago.
7 stars 7.89 score 73 scripts 1 dependents
blasif
cocons:Covariate-Based Covariance Functions for Nonstationary Spatial Modeling
Estimation, prediction, and simulation of nonstationary Gaussian process with modular covariate-based covariance functions. Sources of nonstationarity, such as spatial mean, variance, geometric anisotropy, smoothness, and nugget, can be considered based on spatial characteristics. An induced compact-supported nonstationary covariance function is provided, enabling fast and memory-efficient computations when handling densely sampled domains.
Maintained by Federico Blasi. Last updated 2 months ago.
covariance-matrixcppestimationgaussian-processeslarge-datasetnonstationarityoptimizationpredictioncpp
3 stars 5.48 score 1 scripts
tingram
surface:Fitting Hansen Models to Investigate Convergent Evolution
This data-driven phylogenetic comparative method fits stabilizing selection models to continuous trait data, building on the 'ouch' methodology of Butler and King (2004) <doi:10.1086/426002>. The main functions fit a series of Hansen models using stepwise AIC, then identify cases of convergent evolution where multiple lineages have shifted to the same adaptive peak. For more information see Ingram and Mahler (2013) <doi:10.1111/2041-210X.12034>.
Maintained by Travis Ingram. Last updated 7 months ago.
1 stars 3.10 score 18 scripts
cran
bild:A Package for BInary Longitudinal Data
Performs logistic regression for binary longitudinal data, allowing for serial dependence among observations from a given individual and a random intercept term. Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed, with some restrictions. M. Helena Goncalves et al.(2007) <DOI: 10.18637/jss.v046.i09>.
Maintained by M. Helena Goncalves. Last updated 1 years ago.
1 stars 1.00 scorecran
cold:Count Longitudinal Data
Performs regression analysis for longitudinal count data, allowing for serial dependence among observations from a given individual and two dimensional random effects on the linear predictor. Estimation is via maximization of the exact likelihood of a suitably defined model. Missing values and unbalanced data are allowed. Details can be found in the accompanying scientific papers: Goncalves & Cabral (2021, Journal of Statistical Software, <doi:10.18637/jss.v099.i03>) and Goncalves et al. (2007, Computational Statistics & Data Analysis, <doi:10.1016/j.csda.2007.03.002>).
Maintained by M. Helena Goncalves. Last updated 4 years ago.
1.00 score