Multiverse Analysis of Multinomial Processing Tree Models

Statistical or cognitive modeling usually requires a number of more or less arbitrary choices creating one specific path through a 'garden of forking paths'. The multiverse approach (Steegen, Tuerlinckx, Gelman, & Vanpaemel, 2016, ) offers a principled alternative in which results for all possible combinations of reasonable modeling choices are reported. MPTmultiverse performs a multiverse analysis for multinomial processing tree (MPT, Riefer & Batchelder, 1988, ) models combining maximum-likelihood/frequentist and Bayesian estimation approaches with different levels of pooling (i.e., data aggregation) as described in Singmann et al. (2024, ). For the frequentist approaches, no pooling (with and without parametric or nonparametric bootstrap) and complete pooling are implemented using MPTinR < https://cran.r-project.org/package=MPTinR>. For the Bayesian approaches, no pooling, complete pooling, and three different variants of partial pooling are implemented using TreeBUGS < https://cran.r-project.org/package=TreeBUGS>. The main function is fit_mpt() which performs the multiverse analysis in one call.

Convert Gene IDs Between Each Other and Fetch Annotations from Biomart

Gene Symbols or Ensembl Gene IDs are converted using the Bimap interface in 'AnnotationDbi' in convertId2() but that function is only provided as fallback mechanism for the most common use cases in data analysis. The main function in the package is convert.bm() which queries BioMart using the full capacity of the API provided through the 'biomaRt' package. Presets and defaults are provided for convenience but all "marts", "filters" and "attributes" can be set by the user. Function convert.alias() converts Gene Symbols to Aliases and vice versa and function likely_symbol() attempts to determine the most likely current Gene Symbol.

Automated Transcriptome Classifier Pipeline: Comprehensive Transcriptome Analysis

An unsupervised fully-automated pipeline for transcriptome analysis or a supervised option to identify characteristic genes from predefined subclasses. We rely on the 'pamr' < http://www.bioconductor.org/packages//2.7/bioc/html/pamr.html> clustering algorithm to cluster the Data and then draw a heatmap of the clusters with the most significant genes and the least significant genes according to the 'pamr' algorithm. This way we get easy to grasp heatmaps that show us for each cluster which are the clusters most defining genes.

A Distributed Worker Launcher Framework

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 'NNG'-powered 'mirai' R package by Gao (2023) is a sleek and sophisticated scheduler that efficiently processes these intense workloads. The 'crew' package extends 'mirai' with a unifying interface for third-party worker launchers. Inspiration also comes from packages. 'future' by Bengtsson (2021) , 'rrq' by FitzJohn and Ashton (2023) < https://github.com/mrc-ide/rrq>, 'clustermq' by Schubert (2019) ), and 'batchtools' by Lang, Bischel, and Surmann (2017) .

Complete Environment for Bayesian Inference

Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).

Continuous Time Stochastic Modelling using Template Model Builder

Perform state and parameter inference, and forecasting, in stochastic state-space systems using the 'ctsmTMB' class. This class, built with the 'R6' package, provides a user-friendly interface for defining and handling state-space models. Inference is based on maximum likelihood estimation, with derivatives efficiently computed through automatic differentiation enabled by the 'TMB'/'RTMB' packages (Kristensen et al., 2016) . The available inference methods include Kalman filters, in addition to a Laplace approximation-based smoothing method. For further details of these methods refer to the documentation of the 'CTSMR' package < https://ctsm.info/ctsmr-reference.pdf> and Thygesen (2025) . Forecasting capabilities include moment predictions and stochastic path simulations, both implemented in 'C++' using 'Rcpp' (Eddelbuettel et al., 2018) for computational efficiency.

Estimate Global Clustering in Infectious Disease

Implements various novel and standard clustering statistics and other analyses useful for understanding the spread of infectious disease.

Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'

Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.

Ensemble Clustering using K Means and Hierarchical Clustering

Implements an ensemble algorithm for clustering combining a k-means and a hierarchical clustering approach.

Parallel Processing Options for Package 'dataRetrieval'

Provides methods for retrieving United States Geological Survey (USGS) water data using sequential and parallel processing (Bengtsson, 2022 ). In addition to parallel methods, data wrangling and additional statistical attributes are provided.