This package provides a collection of utilities and ggplot2 extensions to assist with visualisations in genomic epidemiology. This includes the phylepic chart, a visual combination of a phylogenetic tree and a matched epidemic curve. The included ggplot2 extensions such as date axes binned by week are relevant for other applications in epidemiology and beyond. The approach is described in Suster et al. (2024) <doi:10.1101/2024.04.02.24305229>.

This is an implementation of model-based trees with global model parameters (PALM trees). The PALM tree algorithm is an extension to the MOB algorithm (implemented in the partykit package), where some parameters are fixed across all groups. Details about the method can be found in Seibold, Hothorn, Zeileis (2016) <arXiv:1612.07498>. The package offers coef(), logLik(), plot(), and predict() functions for PALM trees.

Create phantom variables, which are variables that were not observed, for the purpose of sensitivity analyses for structural equation models. The package makes it easier for a user to test different combinations of covariances between the phantom variable(s) and observed variables. The package may be used to assess a model's or effect's sensitivity to temporal bias (e.g., if cross-sectional data were collected) or confounding bias.

This package provides an interface to PDFMiner <https://github.com/pdfminer/pdfminer.six> a Python package for extracting information from PDF'-files. PDFMiner has the goal to get all information available in a PDF'-file, position of the characters, font type, font size and informations about lines. Which makes it the perfect starting point for extracting tables from PDF'-files. More information can be found in the package README'-file.

These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates. - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.

This package provides a set of functions of increasing complexity allows users to (1) convert QuadKey-identified datasets, based on Microsoft's Bing Maps Tile System', into Simple Features data frames, (2) transform Simple Features data frames into rasters, and (3) process multiple Meta ('Facebook') QuadKey-identified human mobility files directly into raster files. For more details, see Dâ Andrea et al. (2024) <doi:10.21105/joss.06500>.

This package provides a fast implementation of the weighted information similarity aggregation (WISE) test for detecting serial dependence, particularly suited for high-dimensional and non-Euclidean time series. Includes functions for constructing similarity matrices and conducting hypothesis testing. Users can use different similarity measures and define their own weighting schemes. For more details see Q Zhu, M Liu, Y Han, D Zhou (2025) <doi:10.48550/arXiv.2509.05678>.

This package provides a novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. SpaTopic is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see <https://xiyupeng.github.io/SpaTopic/>.

This package provides a very nice interface to Princeton's WordNet without rJava dependency. WordNet data is not included. Princeton University makes WordNet available to research and commercial users free of charge provided the terms of their license (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and proper reference is made to the project using an appropriate citation (<https://wordnet.princeton.edu/citing-wordnet>).

Provide estimation and data generation tools for the skew-unit family discussed based on Mukhopadhyay and Brani (1995) <doi:10.2307/2348710>. The family contains extensions for popular distributions such as the ArcSin discussed in Arnold and Groeneveld (1980) <doi:10.1080/01621459.1980.10477449>, triangular, U-quadratic and Johnson-SB proposed in Cortina-Borja (2006) <doi:10.1111/j.1467-985X.2006.00446_12.x> distributions, among others.

This package provides a general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort. The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results. It follows a clear object-oriented design and supports parallel computing to increase computational performance.

Includes built-in methods for generating long SQL CASE statements, and other SQL statements that may otherwise be arduous to construct by hand.The generated statement can easily be concatenated to string literals to form queries to SQL'-like databases, such as when using the RODBC package. The current methods include casewhen() for building CASE statements, inlist() for building IN statements, and updatetable() for building UPDATE statements.

Fits cause-specific random survival forests for flexible multistate survival analysis with covariate-adjusted transition probabilities computed via product-integral. State transitions are modeled by random forests. Subject-specific transition probability matrices are assembled from predicted cumulative hazards using the product-integral formula. Also provides a standalone Aalen-Johansen nonparametric estimator as a covariate-free baseline. Supports arbitrary state spaces with any number of states (three or more) and any set of allowed transitions, applicable to clinical trials, disease progression, reliability engineering, and other domains where subjects move among discrete states over time. Provides per-transition feature importance, bias-variance diagnostics, and comprehensive visualizations. Handles right censoring and competing transitions. Methods are described in Ishwaran et al. (2008) <doi:10.1214/08-AOAS169> for random survival forests, Putter et al. (2007) <doi:10.1002/sim.2712> for multistate competing risks decomposition, and Aalen and Johansen (1978) <https://www.jstor.org/stable/4615704> for the nonparametric estimator.

Finding an optimal Bayesian experimental design involves maximizing an objective function given by the expectation of some appropriately chosen utility function with respect to the joint distribution of unknown quantities (including responses). This objective function is usually not available in closed form and the design space can be continuous and of high dimensionality. This package uses Approximate Coordinate Exchange (ACE) to maximise an approximation to the expectation of the utility function.

This package provides a multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The Galgo framework combines the advantages of clustering algorithms for grouping heterogeneous omics data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high.

This package provides a lightweight but powerful R interface to the Azure Resource Manager REST API. The package exposes a comprehensive class framework and related tools for creating, updating and deleting Azure resource groups, resources and templates. While AzureRMR can be used to manage any Azure service, it can also be extended by other packages to provide extra functionality for specific services. Part of the AzureR family of packages.

Fit Bayesian models using brms'/'Stan with parsnip'/'tidymodels via bayesian <doi:10.5281/zenodo.4426836>. tidymodels is a collection of packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>). The technical details of brms and Stan are described in Bürkner (2017) <doi:10.18637/jss.v080.i01>, Bürkner (2018) <doi:10.32614/RJ-2018-017>, and Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Implementation of the bunching estimator for kinks and notches. Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). It produces publication-ready plots in the style followed since Chetty et al. (2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.

This package provides functions to align curves and to compute mean curves based on the elastic distance defined in the square-root-velocity framework. For more details on this framework see Srivastava and Klassen (2016, <doi:10.1007/978-1-4939-4020-2>). For more theoretical details on our methods and algorithms see Steyer et al. (2023, <doi:10.1111/biom.13706>) and Steyer et al. (2023, <arXiv:2305.02075>).

Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) <doi:10.1002/sim.9352> and Zhou et al (2020) <doi:10.1093/biomet/asaa016>. This package relies on the optimization software MOSEK', <https://www.mosek.com>.

The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.

This algorithm is described in detail in the paper "Hedging Forecast Combinations With an Application to the Random Forest" by Beck et al. (2024) <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5032102>. The package provides a function hedgedrf() that can be used to train a Hedged Random Forest model on a dataset, and a function predict.hedgedrf() that can be used to make predictions with the model.

The wiDB...() functions provide an interface to the public API of the wiDB <https://github.com/SPATIAL-Lab/isoWater/blob/master/Protocol.md>: build, check and submit queries, and receive and unpack responses. Data analysis functions support Bayesian inference of the source and source isotope composition of water samples that may have experienced evaporation. Algorithms adapted from Bowen et al. (2018, <doi:10.1007/s00442-018-4192-5>).

Fit parametric models for time-to-event data that show an initial incubation period', i.e., a variable delay phase where no events occur. The delayed Weibull distribution serves as the foundational data model. For parameter estimation, different flavours of maximum likelihood estimation ('MLE') and the method of maximum product of spacings estimation ('MPSE') are implemented. Bootstrap confidence intervals for parameters and significance tests in a two group setting are provided.