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Distributions for Generalized Additive Models for Location Scale and Shape
A set of distributions which can be used for modelling the response variables in Generalized Additive Models for Location Scale and Shape, Rigby and Stasinopoulos (2005),
Spatially and Temporally Varying Coefficient Models Using Generalized Additive Models
A framework for specifying spatially, temporally and spatially-and-temporally varying coefficient models using Generalized Additive Models with Gaussian Process smooths. The smooths are parameterised with location and / or time attributes. Importantly the framework supports the investigation of the presence and nature of any space-time dependencies in the data, allows the user to evaluate different model forms (specifications) and to pick the most probable model or to combine multiple varying coefficient models using Bayesian Model Averaging. For more details see: Brunsdon et al (2023)
Survival Analysis in Health Economic Evaluation
Contains a suite of functions for survival analysis in health economics.
These can be used to run survival models under a frequentist (based on maximum likelihood)
or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian
Monte Carlo). To run the Bayesian models, the user needs to install additional modules
(packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed using
'remotes::install_github' from their GitHub repositories:
(< https://github.com/giabaio/survHEhmc> and < https://github.com/giabaio/survHEinla/>
respectively). 'survHEinla' is based on the package INLA, which is available for download at
< https://inla.r-inla-download.org/R/stable/>. The user can specify a set of parametric models
using a common notation and select the preferred mode of inference. The results can also be
post-processed to produce probabilistic sensitivity analysis and can be used to export the
output to an Excel file (e.g. for a Markov model, as often done by modellers and
practitioners).
Fitting Shared Atoms Nested Models via Markov Chains Monte Carlo
Estimate Bayesian nested mixture models via Markov Chain Monte Carlo methods. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model.
All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyzing the results of the fitting procedure.
References:
Denti, Camerlenghi, Guindani, Mira (2023)
Estimate Pollinator Body Size and Co-Varying Ecological Traits
Tools to estimate pollinator body size and co-varying traits. This package contains novel Bayesian predictive models of pollinator body size (for bees and hoverflies) as well as preexisting predictive models for pollinator body size (currently implemented for ants, bees, butterflies, flies, moths and wasps) as well as bee tongue length and foraging distance, total field nectar loads and wing loading. An additional GitHub repository < https://github.com/liamkendall/pollimetrydata> provides model objects to use the bodysize function internally. All models are described in Kendall et al (2018)
Estimating the Degrees of Freedom of the Student's t-Distribution under a Bayesian Framework
A Bayesian framework to estimate the Student's t-distribution's degrees of freedom is developed. Markov Chain Monte Carlo sampling routines are developed as in
Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models
Efficient approximate leave-one-out cross-validation (LOO)
for Bayesian models fit using Markov chain Monte Carlo, as
described in Vehtari, Gelman, and Gabry (2017)
Tcl/Tk Additions
A series of additional Tcl commands and Tk widgets with style and various functions (under Windows: DDE exchange, access to the registry and icon manipulation) to supplement the tcltk package
Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Gaussian finite mixture models fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization, dimension reduction for visualisation, and resampling-based inference.
MCMC, Particle Filtering, and Programmable Hierarchical Modeling
A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at < https://r-nimble.org>.