Found 2546 packages in 0.01 seconds
Number of Newly Discovered Rare Species Estimation
A Bayesian-weighted estimator and two unweighted estimators are developed to estimate the number of newly found rare species in additional ecological samples. Among these methods, the Bayesian-weighted estimator and an unweighted (Chao-derived) estimator are of high accuracy and recommended for practical applications. Technical details of the proposed estimators have been well described in the following paper: Shen TJ, Chen YH (2018) A Bayesian weighted approach to predicting the number of newly discovered rare species. Conservation Biology, In press.
Generalized Additive Mixed Model Interface
An interface for fitting generalized additive models (GAMs) and generalized additive mixed models (GAMMs) using the 'lme4' package as the computational engine, as described in Helwig (2024)
Causal Inference using Bayesian Causal Forests
Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020)
Computer Model Calibration for Deterministic and Stochastic Simulators
Implements the Bayesian calibration model described
in Pratola and Chkrebtii (2018)
Bayesian Penalized Quantile Regression
Bayesian regularized quantile regression utilizing sparse priors to
impose exact sparsity leads to efficient Bayesian shrinkage estimation, variable
selection and statistical inference. In this package, we have implemented robust
Bayesian variable selection with spike-and-slab priors under high-dimensional
linear regression models (Fan et al. (2024)
Choice Item Response Theory
Jointly model the accuracy of cognitive responses and item choices
within a Bayesian hierarchical framework as described by Culpepper and
Balamuta (2015)
Model Selection in Multivariate Longitudinal Data Analysis
An efficient Gibbs sampling algorithm is developed for Bayesian multivariate longitudinal data analysis with the focus on selection of important elements in the generalized autoregressive matrix. It provides posterior samples and estimates of parameters. In addition, estimates of several information criteria such as Akaike information criterion (AIC), Bayesian information criterion (BIC), deviance information criterion (DIC) and prediction accuracy such as the marginal predictive likelihood (MPL) and the mean squared prediction error (MSPE) are provided for model selection.
Utilising Normalisation Constant Optimisation via Edge Removal (UNCOVER)
Model data with a suspected clustering structure (either in
co-variate space, regression space or both) using a Bayesian product model
with a logistic regression likelihood. Observations are represented
graphically and clusters are formed through various edge removals or
additions. Cluster quality is assessed through the log Bayesian evidence of
the overall model, which is estimated using either a Sequential Monte Carlo
sampler or a suitable transformation of the Bayesian Information Criterion
as a fast approximation of the former. The internal Iterated Batch
Importance Sampling scheme (Chopin (2002
Contrast-Based Bayesian Network Meta Analysis
A function that facilitates fitting three types of models
for contrast-based Bayesian Network Meta Analysis. The first model is that which
is described in Lu and Ades (2006)
Plotting for Bayesian Models
Plotting functions for posterior analysis, MCMC diagnostics,
prior and posterior predictive checks, and other visualizations
to support the applied Bayesian workflow advocated in
Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019)