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BsMD — by Ernesto Barrios, 6 months ago

Bayes Screening and Model Discrimination

Bayes screening and model discrimination follow-up designs.

deconvolveR — by Balasubramanian Narasimhan, 4 years ago

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, ).

geostatsp — by Patrick Brown, a month ago

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) . The 'RandomFields' package is available at < https://www.wim.uni-mannheim.de/schlather/publications/software>.

flashier — by Jason Willwerscheid, 5 months ago

Empirical Bayes Matrix Factorization

Methods for matrix factorization based on Wang and Stephens (2021) < https://jmlr.org/papers/v22/20-589.html>.

BayesSampling — by Pedro Soares Figueiredo, 3 years ago

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>.

incidental — by Lauren Hannah, 4 years ago

Implements Empirical Bayes Incidence Curves

Make empirical Bayes incidence curves from reported case data using a specified delay distribution.

nbc4va — by Richard Wen, 2 years ago

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) .

ebci — by Michal Kolesár, 3 years ago

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) .

SBmedian — by Kisung You, 3 years ago

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) .

NBShiny2 — by Kartikeya Bolar, 4 years ago

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/>.