Process Accelerometer Data for Physical Activity Measurement

It provides a function "wearingMarking" for classification of monitor wear and nonwear time intervals in accelerometer data collected to assess physical activity. The package also contains functions for making plot for accelerometer data and obtaining the summary of various information including daily monitor wear time and the mean monitor wear time during valid days. "deliveryPred" and "markDelivery" can classify days for ActiGraph delivery by mail; "deliveryPreprocess" can process accelerometry data for analysis by zeropadding incomplete days and removing low activity days; "markPAI" can categorize physical activity intensity level based on user-defined cut-points of accelerometer counts. It also supports importing ActiGraph AGD files with "readActigraph" and "queryActigraph" functions.

Project Management Tools

Tools for data importation, recoding, and inspection. There are functions to create new project folders, R code templates, create uniquely named output directories, and to quickly obtain a visual summary for each variable in a data frame. The main feature here is the systematic implementation of the "variable key" framework for data importation and recoding. We are eager to have community feedback about the variable key and the vignette about it. In version 1.7, the function 'semTable' is removed. It was deprecated since 1.67. That is provided in a separate package, 'semTable'.

Markov Chain Monte Carlo for Potts Models

Do Markov chain Monte Carlo (MCMC) simulation of Potts models (Potts, 1952, ), which are the multi-color generalization of Ising models (so, as as special case, also simulates Ising models). Use the Swendsen-Wang algorithm (Swendsen and Wang, 1987, ) so MCMC is fast. Do maximum composite likelihood estimation of parameters (Besag, 1975, , Lindsay, 1988, ).

Multi-Gene Descent Probabilities

Do multi-gene descent probabilities (Thompson, 1983, ) and special cases thereof (Thompson, 1986, ) including inbreeding and kinship coefficients. But does much more: probabilities of any set of genes descending from any other set of genes.

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.

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).