Dynamic Linear Model for Wastewater-Based Epidemiology

Implement dynamic linear models outlined in Shumway and Stoffer (2025) . Two model structures for data smoothing and forecasting are considered. The specific models proposed will be added once the manuscript is published.

Superpixels of Spatial Data

Creates superpixels based on input spatial data. This package works on spatial data with one variable (e.g., continuous raster), many variables (e.g., RGB rasters), and spatial patterns (e.g., areas in categorical rasters). It is based on the SLIC algorithm (Achanta et al. (2012) ), and readapts it to work with arbitrary dissimilarity measures.

Uniformly Most Powerful Tests

Does uniformly most powerful (UMP) and uniformly most powerful unbiased (UMPU) tests. At present only distribution implemented is binomial distribution. Also does fuzzy tests and confidence intervals (following Geyer and Meeden, 2005, ) for the binomial distribution (one-tailed procedures based on UMP test and two-tailed procedures based on UMPU test).

Generalized Linear Mixed Models via Monte Carlo Likelihood Approximation

Approximates the likelihood of a generalized linear mixed model using Monte Carlo likelihood approximation. Then maximizes the likelihood approximation to return maximum likelihood estimates, observed Fisher information, and other model information.

Two Stage Hazard Rate Comparison

Two-stage procedure compares hazard rate functions, which may or may not cross each other.

R Client for 'Customer Journey Analytics' ('CJA') API

Connect and pull data from the 'CJA' API, which powers 'CJA Workspace' < https://github.com/AdobeDocs/cja-apis>. The package was developed with the analyst in mind and will continue to be developed with the guiding principles of iterative, repeatable, timely analysis. New features are actively being developed and we value your feedback and contribution to the process.

Simulating from the Polya Posterior

Simulate via Markov chain Monte Carlo (hit-and-run algorithm) a Dirichlet distribution conditioned to satisfy a finite set of linear equality and inequality constraints (hence to lie in a convex polytope that is a subset of the unit simplex).

R Client for 'Adobe Analytics' API 2.0

Connect to the 'Adobe Analytics' API v2.0 < https://github.com/AdobeDocs/analytics-2.0-apis> which powers 'Analysis Workspace'. The package was developed with the analyst in mind, and it will continue to be developed with the guiding principles of iterative, repeatable, timely analysis.

Sequential Imputation with Bayesian Trees Mixed-Effects Models for Longitudinal Data

Implements a sequential imputation framework using Bayesian Mixed-Effects Trees ('SBMTrees') for handling missing data in longitudinal studies. The package supports a variety of models, including non-linear relationships and non-normal random effects and residuals, leveraging Dirichlet Process priors for increased flexibility. Key features include handling Missing at Random (MAR) longitudinal data, imputation of both covariates and outcomes, and generating posterior predictive samples for further analysis. The methodology is designed for applications in epidemiology, biostatistics, and other fields requiring robust handling of missing data in longitudinal settings.

GPU Functions for R Objects

Provides GPU enabled functions for 'R' objects in a simple and approachable manner. New 'gpu*' and 'vcl*' classes have been provided to wrap typical 'R' objects (e.g. vector, matrix), in both host and device spaces, to mirror typical 'R' syntax without the need to know 'OpenCL'.