Automated and Early Detection of Disease Outbreaks

A powerful tool for automating the early detection of disease outbreaks in time series data. 'aeddo' employs advanced statistical methods, including hierarchical models, in an innovative manner to effectively characterize outbreak signals. It is particularly useful for epidemiologists, public health professionals, and researchers seeking to identify and respond to disease outbreaks in a timely fashion. For a detailed reference on hierarchical models, consult Henrik Madsen and Poul Thyregod's book (2011), ISBN: 9781420091557.

Fitting (Exponential/Diffusion) RT-MPT Models

Fit (exponential or diffusion) response-time extended multinomial processing tree (RT-MPT) models by Klauer and Kellen (2018) and Klauer, Hartmann, and Meyer-Grant (submitted). The RT-MPT class not only incorporate frequencies like traditional multinomial processing tree (MPT) models, but also latencies. This enables it to estimate process completion times and encoding plus motor execution times next to the process probabilities of traditional MPTs. 'rtmpt' is a hierarchical Bayesian framework and posterior samples are sampled using a Metropolis-within-Gibbs sampler (for exponential RT-MPTs) or Hamiltonian-within-Gibbs sampler (for diffusion RT-MPTs).

Dynamic Models for Confidence and Response Time Distributions

Provides density functions for the joint distribution of choice, response time and confidence for discrete confidence judgments as well as functions for parameter fitting, prediction and simulation for various dynamical models of decision confidence. All models are explained in detail by Hellmann et al. (2023; Preprint available at < https://osf.io/9jfqr/>, published version: ). Implemented models are the dynaViTE model, dynWEV model, the 2DSD model (Pleskac & Busemeyer, 2010, ), and various race models. C++ code for dynWEV and 2DSD is based on the 'rtdists' package by Henrik Singmann.

Complete Functional Regulation Analysis

Calculates complete functional regulation analysis and visualize the results in a single heatmap. The provided example data is for biological data but the methodology can be used for large data sets to compare quantitative entities that can be grouped. For example, a store might divide entities into cloth, food, car products etc and want to see how sales changes in the groups after some event. The theoretical background for the calculations are provided in New insights into functional regulation in MS-based drug profiling, Ana Sofia Carvalho, Henrik Molina & Rune Matthiesen, Scientific Reports .

Item Selection and Exhaustive Search for Rasch Models

Automation of the item selection processes for Rasch scales by means of exhaustive search for suitable Rasch models (dichotomous, partial credit, rating-scale) in a list of item-combinations. The item-combinations to test can be either all possible combinations or item-combinations can be defined by several rules (forced inclusion of specific items, exclusion of combinations, minimum/maximum items of a subset of items). Tests for model fit and item fit include ordering of the thresholds, item fit-indices, likelihood ratio test, Martin-Löf test, Wald-like test, person-item distribution, person separation index, principal components of Rasch residuals, empirical representation of all raw scores or Rasch trees for detecting differential item functioning. The tests, their ordering and their parameters can be defined by the user. For parameter estimation and model tests, functions of the packages 'eRm', 'psychotools' or 'pairwise' can be used.

A Crew Launcher Plugin for AWS Batch

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'crew.aws.batch' package extends the 'mirai'-powered 'crew' package with a worker launcher plugin for AWS Batch. Inspiration also comes from packages 'mirai' by Gao (2023) < https://github.com/r-lib/mirai>, 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) < https://github.com/mrc-ide/rrq>, 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischl, and Surmann (2017). .

Crew Launcher Plugins for Traditional High-Performance Computing Clusters

In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The 'crew.cluster' package extends the 'mirai'-powered 'crew' package with worker launcher plugins for traditional high-performance computing systems. Inspiration also comes from packages 'mirai' by Gao (2023) < https://github.com/r-lib/mirai>, 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) < https://github.com/mrc-ide/rrq>, 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischl, and Surmann (2017). .

Genetic Population Level Functions

This collection of gene representation-independent functions implements the population layer of extended evolutionary and genetic algorithms and its support. The population layer consists of functions for initializing, logging, observing, evaluating a population of genes, as well as of computing the next population. For parallel evaluation of a population of genes 4 execution models - named Sequential, MultiCore, FutureApply, and Cluster - are provided. They are implemented by configuring the lapply() function. The execution model FutureApply can be externally configured as recommended by Bengtsson (2021) . Configurable acceptance rules and cooling schedules (see Kirkpatrick, S., Gelatt, C. D. J, and Vecchi, M. P. (1983) , and Aarts, E., and Korst, J. (1989, ISBN:0-471-92146-7) offer simulated annealing or greedy randomized approximate search procedure elements. Adaptive crossover and mutation rates depending on population statistics generalize the approach of Stanhope, S. A. and Daida, J. M. (1996, ISBN:0-18-201-031-7).

R Fortunes

A collection of fortunes from the R community.