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) .

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>.

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) .

Empirical Bayes Estimation of Dynamic Bayesian Networks

Infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks.

Computation of Bayes Factors for Common Biomedical Designs

BAYesian inference for MEDical designs in R. Functions for the computation of Bayes factors for common biomedical research designs. Implemented are functions to test the equivalence (equiv_bf), non-inferiority (infer_bf), and superiority (super_bf) of an experimental group compared to a control group on a continuous outcome measure. Bayes factors for these three tests can be computed based on raw data (x, y) or summary statistics (n_x, n_y, mean_x, mean_y, sd_x, sd_y [or ci_margin and ci_level]).

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/>.

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/>.

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/>.