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Flexible Co-Data Learning for High-Dimensional Prediction
Fit linear, logistic and Cox survival regression models penalised with adaptive multi-group ridge penalties.
The multi-group penalties correspond to groups of covariates defined by (multiple) co-data sources.
Group hyperparameters are estimated with an empirical Bayes method of moments, penalised with an extra level of hyper shrinkage.
Various types of hyper shrinkage may be used for various co-data.
Co-data may be continuous or categorical.
The method accommodates inclusion of unpenalised covariates, posterior selection of covariates and multiple data types.
The model fit is used to predict for new samples.
The name 'ecpc' stands for Empirical Bayes, Co-data learnt, Prediction and Covariate selection.
See Van Nee et al. (2020)
Density Estimation via Bayesian Inference Engines
Bayesian density estimates for univariate continuous random samples are provided using the Bayesian inference engine paradigm. The engine options are: Hamiltonian Monte Carlo, the no U-turn sampler, semiparametric mean field variational Bayes and slice sampling. The methodology is described in Wand and Yu (2020)
Bayesian Model Averaging for Random and Fixed Effects Meta-Analysis
Computes the posterior model probabilities for standard meta-analysis models
(null model vs. alternative model assuming either fixed- or random-effects, respectively).
These posterior probabilities are used to estimate the overall mean effect size
as the weighted average of the mean effect size estimates of the random- and
fixed-effect model as proposed by Gronau, Van Erp, Heck, Cesario, Jonas, &
Wagenmakers (2017,
Bayesian Restricted Mean Survival Time for Cluster Effect
The parametric Bayes analysis for the
restricted mean survival time (RMST) with cluster effect,
as described in Hanada and Kojima (2024)
Bayesian Change-Point Detection for Process Monitoring with Fault Detection
Bayes Watch fits an array of Gaussian Graphical Mixture Models to groupings of homogeneous data in time, called regimes, which are modeled as the observed states of a Markov process with unknown transition probabilities. In doing so, Bayes Watch defines a posterior distribution on a vector of regime assignments, which gives meaningful expressions on the probability of every possible change-point. Bayes Watch also allows for an effective and efficient fault detection system that assesses what features in the data where the most responsible for a given change-point. For further details, see: Alexander C. Murph et al. (2023)
Bayesian Analysis of Replication Studies
Provides tools for the analysis of replication studies using Bayes factors (Pawel and Held, 2022)
Bayesian reconstruction of growth velocity
A nonparametric empirical Bayes method for recovering gradients (or growth velocities) from observations of smooth functions (e.g., growth curves) at isolated time points.
EAP Scoring in Exploratory FA Solutions with Correlated Residuals
Obtaining Bayes Expected A Posteriori (EAP) individual score estimates based on linear and non-linear extended Exploratoy Factor Analysis solutions that include a correlated-residual structure.
Joint N-Mixture Models for Site-Associated Species
Fits univariate and joint N-mixture models for data on two unmarked site-associated species. Includes functions to estimate latent abundances through empirical Bayes methods.
Quantify and Control Reproducibility in High-Throughput Experiments
Estimate the proportions of the null and the reproducibility and non-reproducibility of the signal group for the input data set. The Bayes factor calculation and EM (Expectation Maximization) algorithm procedures are also included.