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Bayes Screening and Model Discrimination
Bayes screening and model discrimination follow-up designs.
Empirical Bayes Estimation Strategies
Empirical Bayes methods for learning prior distributions from data.
An unknown prior distribution (g) has yielded (unobservable) parameters, each of
which produces a data point from a parametric exponential family (f). The goal
is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical
Bayes methods. Details and examples are in the paper by Narasimhan and Efron
(2020,
Geostatistical Modelling with Likelihood and Bayes
Geostatistical modelling facilities using 'SpatRaster' and 'SpatVector'
objects are provided. Non-Gaussian models are fit using 'INLA', and Gaussian
geostatistical models use Maximum Likelihood Estimation. For details see Brown (2015)
Empirical Bayes Matrix Factorization
Methods for matrix factorization based on Wang and Stephens (2021) < https://jmlr.org/papers/v22/20-589.html>.
Bayes Linear Estimators for Finite Population
Allows the user to apply the Bayes Linear approach to finite population with the Simple Random Sampling - BLE_SRS() - and the Stratified Simple Random Sampling design - BLE_SSRS() - (both without replacement), to the Ratio estimator (using auxiliary information) - BLE_Ratio() - and to categorical data - BLE_Categorical(). The Bayes linear estimation approach is applied to a general linear regression model for finite population prediction in BLE_Reg() and it is also possible to achieve the design based estimators using vague prior distributions. Based on Gonçalves, K.C.M, Moura, F.A.S and Migon, H.S.(2014) < https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X201400111886>.
Implements Empirical Bayes Incidence Curves
Make empirical Bayes incidence curves from reported case data using a specified delay distribution.
Bayes Classifier for Verbal Autopsy Data
An implementation of the Naive Bayes Classifier (NBC) algorithm
used for Verbal Autopsy (VA) built on code from Miasnikof et al (2015)
Robust Empirical Bayes Confidence Intervals
Computes empirical Bayes confidence estimators and confidence
intervals in a normal means model. The intervals are robust in the sense
that they achieve correct coverage regardless of the distribution of the
means. If the means are treated as fixed, the intervals have an average
coverage guarantee. The implementation is based on Armstrong, Kolesár and
Plagborg-Møller (2020)
Scalable Bayes with Median of Subset Posteriors
Median-of-means is a generic yet powerful framework for scalable and robust estimation. A framework for Bayesian analysis is called M-posterior, which estimates a median of subset posterior measures. For general exposition to the topic, see the paper by Minsker (2015)
Interactive Document for Working with Naive Bayes Classification
An interactive document on the topic of naive Bayes classification analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at < https://kartikeyab.shinyapps.io/NBShiny/>.