S4-classes and methods for distributions.

This package provides a toolbox to create and manage metadata files and configuration profiles: files used to configure the parameters and initial settings for some computer programs.

Compute estimates and confidence intervals of weighted averages quickly and easily. Weighted averages are computed using data.table for speed. Confidence intervals are approximated using the delta method with either using known formulae or via algorithmic or numerical integration.

Efficient covariate-adjusted estimators of quantities that are useful for establishing the effects of treatments on ordinal outcomes.

Simple feature stores and tools for creating personalised feature stores. diseasystore powers feature stores which can automatically link and aggregate features to a given stratification level. These feature stores are automatically time-versioned (powered by the SCDB package) and allows you to easily and dynamically compute features as part of your continuous integration.

An implementation by Chen, Li, and Zhang (2022) <doi: 10.1093/bioadv/vbac041> of the Depth Importance in Precision Medicine (DIPM) method in Chen and Zhang (2022) <doi:10.1093/biostatistics/kxaa021> and Chen and Zhang (2020) <doi:10.1007/978-3-030-46161-4_16>. The DIPM method is a classification tree that searches for subgroups with especially poor or strong performance in a given treatment group.

Various diffusion models to forecast new product growth. Currently the package contains Bass, Gompertz, Gamma/Shifted Gompertz and Weibull curves. See Meade and Islam (2006) <doi:10.1016/j.ijforecast.2006.01.005>.

We provide a list of functions for replicating the results of the Monte Carlo simulations and empirical application of Jiang et al. (2022). In particular, we provide corresponding functions for generating the three types of random data described in this paper, as well as all the estimation strategies. Detailed information about the data generation process and estimation strategy can be found in Jiang et al. (2022) <doi:10.48550/arXiv.2201.13004>.

This package implements survival proximity score matching in multi-state survival models. Includes tools for simulating survival data and estimating transition-specific coxph models with frailty terms. The primary methodological work on multistate censored data modeling using propensity score matching has been published by Bhattacharjee et al.(2024) <doi:10.1038/s41598-024-54149-y>.

This package provides helper functions to scrape the online archive of the Deutsche Turnliga <https://www.deutsche-turnliga.de/archiv.html>. Also included is data of one competition and one matchday as test data.

This package contains a robust set of tools designed for constructing deep neural networks, which are highly adaptable with user-defined loss function and probability models. It includes several practical applications, such as the (deepAFT) model, which utilizes a deep neural network approach to enhance the accelerated failure time (AFT) model for survival data. Another example is the (deepGLM) model that applies deep neural network to the generalized linear model (glm), accommodating data types with continuous, categorical and Poisson distributions.

Simple Principal Components Analysis (PCA) and (Multiple) Correspondence Analysis (CA) based on the Singular Value Decomposition (SVD). This package provides S4 classes and methods to compute, extract, summarize and visualize results of multivariate data analysis. It also includes methods for partial bootstrap validation described in Greenacre (1984, ISBN: 978-0-12-299050-2) and Lebart et al. (2006, ISBN: 978-2-10-049616-7).

This package provides a drop-in replacement for dplyr', powered by DuckDB for performance. Offers convenient utilities for working with in-memory and larger-than-memory data while retaining full dplyr compatibility.

Data frame, tibble, or tbl objects are converted to data package objects using specific metadata labels (name, version, title, homepage, description). A data package object ('dpkg') can be written to disk as a parquet file or released to a GitHub repository. Data package objects can be read into R from online repositories and downloaded files are cached locally across R sessions.

Base DataSHIELD functions for the server side. DataSHIELD is a software package which allows you to do non-disclosive federated analysis on sensitive data. DataSHIELD analytic functions have been designed to only share non disclosive summary statistics, with built in automated output checking based on statistical disclosure control. With data sites setting the threshold values for the automated output checks. For more details, see citation("dsBase")'.

Imputation of mixed-type and compositional data with neural networks. The architecture (number and size of hidden layers, dropout, activation, optimiser) is user-configurable. See Templ (2021) <doi:10.1007/978-3-030-71175-7>.

S4-classes for setting up a coherent framework for simulation within the distr family of packages.

Distributed Online Covariance Matrix Tests Docovt is a powerful tool designed to efficiently process and analyze distributed datasets. It enables users to perform covariance matrix tests in an online, distributed manner, making it highly suitable for large-scale data analysis. By leveraging advanced computational techniques, Docovt ensures robust and scalable solutions for statistical analysis, particularly in scenarios where data is dispersed across multiple nodes or sources. This package is ideal for researchers and practitioners working with high-dimensional data, providing a flexible and efficient framework for covariance matrix estimation and hypothesis testing. The philosophy of Docovt is described in Guo G.(2025) <doi:10.1016/j.physa.2024.130308>.

Implement download buttons in HTML output from rmarkdown without the need for runtime:shiny'.

Demonstrate the results of a statistical model object as a dynamic nomogram in an RStudio panel or web browser. The package provides two generics functions: DynNom, which display statistical model objects as a dynamic nomogram; DNbuilder, which builds required scripts to publish a dynamic nomogram on a web server such as the <https://www.shinyapps.io/>. Current version of DynNom supports stats::lm, stats::glm, survival::coxph, rms::ols, rms::Glm, rms::lrm, rms::cph, and mgcv::gam model objects.

This package provides a set of tools for empirical analysis of diversity (a number and frequency of different types in a population) and similarity (a number and frequency of shared types in two populations) in biological or ecological systems.

The rapid development of single-cell transcriptomic technologies has helped uncover the cellular heterogeneity within cell populations. However, bulk RNA-seq continues to be the main workhorse for quantifying gene expression levels due to technical simplicity and low cost. To most effectively extract information from bulk data given the new knowledge gained from single-cell methods, we have developed a novel algorithm to estimate the cell-type composition of bulk data from a single-cell RNA-seq-derived cell-type signature. Comparison with existing methods using various real RNA-seq data sets indicates that our new approach is more accurate and comprehensive than previous methods, especially for the estimation of rare cell types. More importantly,our method can detect cell-type composition changes in response to external perturbations, thereby providing a valuable, cost-effective method for dissecting the cell-type-specific effects of drug treatments or condition changes. As such, our method is applicable to a wide range of biological and clinical investigations. Dampened weighted least squares ('DWLS') is an estimation method for gene expression deconvolution, in which the cell-type composition of a bulk RNA-seq data set is computationally inferred. This method corrects common biases towards cell types that are characterized by highly expressed genes and/or are highly prevalent, to provide accurate detection across diverse cell types. See: <https://www.nature.com/articles/s41467-019-10802-z.pdf> for more information about the development of DWLS and the methods behind our functions.

This package provides functions to run the CRM and TITE-CRM in phase I trials and calibration tools for trial planning purposes.

This package provides a wrapper for Google's diff-match-patch library. It provides basic tools for computing diffs, finding fuzzy matches, and constructing / applying patches to strings.