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Naive Bayes Transmission Analysis
Estimates the relative transmission probabilities between cases in an infectious disease outbreak or cluster using naive Bayes. Included are various functions to use these probabilities to estimate transmission parameters such as the generation/serial interval and reproductive number as well as finding the contribution of covariates to the probabilities and visualizing results. The ideal use is for an infectious disease dataset with metadata on the majority of cases but more informative data such as contact tracing or pathogen whole genome sequencing on only a subset of cases. For a detailed description of the methods see Leavitt et al. (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)
Group Sequential Bayes Design
Group Sequential Operating Characteristics for Clinical,
Bayesian two-arm Trials with known Sigma and Normal Endpoints,
as described in Gerber and Gsponer (2016)
Empirical Bayes Matrix Factorization
Methods for matrix factorization based on Wang and Stephens (2021) < https://jmlr.org/papers/v22/20-589.html>.
Project Code - Nonparametric Bayes
Basic implementation of a Gibbs sampler for a Chinese Restaurant Process along with some visual aids to help understand how the sampling works. This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute.
Implementation of the Empirical Bayes Method
Implements a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response. The method was first presented in Martin, Ryan and Mess, Raymond and Walker, Stephen G (2017)
Recommended Learners for 'mlr3'
Recommended Learners for 'mlr3'. Extends 'mlr3' with interfaces to essential machine learning packages on CRAN. This includes, but is not limited to: (penalized) linear and logistic regression, linear and quadratic discriminant analysis, k-nearest neighbors, naive Bayes, support vector machines, and gradient boosting.
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>.