This package provides customizable 3D tree models (as OBJ files) for use in data visualization. Includes both planar and solid tree models, various crown types (columnar, oval, palm, pyramidal, rounded, spreading, vase, weeping), and options to change the diameter, height, and color of the tree's crown and trunk.

Write output (plots and tables) ensuring traceability back to code. Includes a graphics saver with simple automation of stamping with source, destination and creation time. A list of plots can be saved at once. A user-friendly selection of output dimensions for presentations, on-screen inspections, and more available.

This package implements a probabilistic ensemble time-series forecaster that combines an auto-encoder with a neural decision forest whose split variables are learned through a differentiable feature-mask layer. Functions are written with torch tensors and provide CRPS (Continuous Ranked Probability Scores) training plus mixture-distribution post-processing.

This package provides a port of Inspect', a widely adopted Python framework for large language model evaluation. Specifically aimed at ellmer users who want to measure the effectiveness of their large language model-based products, the package supports prompt engineering, tool usage, multi-turn dialog, and model graded evaluations.

This package provides a set of functions for receiver operating characteristic (ROC) curve estimation and area under the curve (AUC) calculation. All functions are designed to work with aggregated data; nevertheless, they can also handle raw samples. In ROCket', we distinguish two types of ROC curve representations: 1) parametric curves - the true positive rate (TPR) and the false positive rate (FPR) are functions of a parameter (the score), 2) functions - TPR is a function of FPR. There are several ROC curve estimation methods available. An introduction to the mathematical background of the implemented methods (and much more) can be found in de Zea Bermudez, Gonçalves, Oliveira & Subtil (2014) and Cai & Pepe (2004).

In order to create smooth animation between states of data, tweening is necessary. This package provides a range of functions for creating tweened data that can be used as basis for animation. Furthermore it adds a number of vectorized interpolaters for common R data types such as numeric, date and color.

This package generates area-proportional Euler diagrams using numerical optimization. An Euler diagram is a generalization of a Venn diagram, relaxing the criterion that all interactions need to be represented. Diagrams may be fit with ellipses and circles via a wide range of inputs and can be visualized in numerous ways.

GEMINI uses log-fold changes to model sample-dependent and independent effects, and uses a variational Bayes approach to infer these effects. The inferred effects are used to score and identify genetic interactions, such as lethality and recovery. More details can be found in Zamanighomi et al. 2019 (in press).

This package provides a preprocessing pipeline for single cell RNA-seq/ATAC-seq data that starts from the fastq files and produces a feature count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols.

The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variation (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided.

This package provides a tidy text corpus of Aesop's Fables sourced from the Library of Congress, along with analysis-ready datasets for sentiment, emotion, and linguistic analysis of moral storytelling. The package includes both full narrative texts and word-level representations to support exploratory text analysis and teaching workflows.

Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data. This methodology based on Psioda et al (2025) <doi:10.1080/10543406.2025.2489285>.

Constrained quantile regression is performed. One constraint is that all beta coefficients (including the constant) cannot be negative, they can be either 0 or strictly positive. Another constraint is that the beta coefficients lie within an interval. References: Koenker R. (2005) Quantile Regression, Cambridge University Press. <doi:10.1017/CBO9780511754098>.

Correlates of protection (CoP) and correlates of risk (CoR) study the immune biomarkers associated with an infectious disease outcome, e.g. COVID or HIV-1 infection. This package contains shared functions for analyzing CoP and CoR, including bootstrapping procedures, competing risk estimation, and bootstrapping marginalized risks.

Enables user interactivity with large-language models ('LLM') inside the RStudio integrated development environment (IDE). The user can interact with the model using the shiny app included in this package, or directly in the R console. It comes with back-ends for OpenAI', GitHub Copilot', and LlamaGPT'.

Exploration of simulation models (apps) of various infectious disease transmission dynamics scenarios. The purpose of the package is to help individuals learn about infectious disease epidemiology (ecology/evolution) from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models.

This package provides functions and example datasets to run a decision-analytic model for prevention and treatment strategies across depression severity states (sub-clinical, mild, moderate, severe, and recurrent). The package supports scenario analyses (base and alternative inputs) and summarises outcomes such as coverage, adherence, effect sizes, and healthcare costs.

DataSHIELD is an infrastructure and series of R packages that enables the remote and non-disclosive analysis of sensitive research data. This DataSHIELD Interface implementation is for analyzing datasets living in the current R session. The purpose of this is primarily for lightweight DataSHIELD analysis package development.

This package provides tools to estimate and manage empirical distributions, which should work with survey data. One of the main features is the possibility to create data cubes of estimated statistics, that include all the combinations of the variables of interest (see for example functions dcc5() and dcc6()).

This package provides a collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.

Simulation models (apps) of various within-host immune response scenarios. The purpose of the package is to help individuals learn about within-host infection and immune response modeling from a dynamical systems perspective. All apps include explanations of the underlying models and instructions on what to do with the models.

The core of this package is a function eDT() which enhances DT::datatable() such that it can be used to interactively modify data in shiny'. By the use of generic dplyr methods it supports many types of data storage, with relational databases ('dbplyr') being the main use case.

Given a continuous-time dynamic network, this package allows one to fit a stochastic blockmodel where nodes belonging to the same group create interactions and non-interactions of similar lengths. This package implements the methodology described by R. Rastelli and M. Fop (2020) <doi:10.1007/s11634-020-00403-w>.

This package provides tools for making epidemiological reporting easier with consistent static and dynamic charts and maps. Builds on ggplot2 for static visualizations as described in Wickham (2016) <doi:10.1007/978-3-319-24277-4> and plotly for interactive visualizations as described in Sievert (2020) <doi:10.1201/9780429447273>.