Piecewise linear segmentation of ordered data by a dynamic programming algorithm. The algorithm was developed for time series data, e.g. growth curves, and for genome-wide read-count data from next generation sequencing, but is broadly applicable. Generic implementations of dynamic programming routines allow to scan for optimal segmentation parameters and test custom segmentation criteria ("scoring functions").

This package provides methods to compute verifiable false discovery rate (FDR) diagnostic checks for workflows based on target-decoy competition and related confidence measures. Implements calibration, stability and tail diagnostics, including tail support, threshold elasticity, posterior error probability (PEP) reliability, and equal-chance checks. If you used this package in your research, please cite the associated preprint <doi:10.64898/2026.04.16.718468>. Detailed examples of using this package can also be found on the GitHub repository (<https://github.com/Jacky11/diagFDR>).

High-frequency time-series support via nanotime and data.table'.

An anonymization algorithm to resist neighbor label attack in a dynamic network.

This package provides a HTML widget that shows differences between files (text, images, and data frames).

This package provides tools to help the design and analysis of resilient non-inferiority trials. These include functions for sample size calculations and analyses of trials, with either a risk difference, risk ratio or arc-sine difference margin, and a function to run simulations to design a trial with the methods described in Quartagno et al. (2019) <arXiv:1905.00241>.

This package provides functions are provided to fit temporal lag models to dynamic networks. The models are build on top of exponential random graph models (ERGM) framework. There are functions for simulating or forecasting networks for future time points. Abhirup Mallik & Zack W. Almquist (2019) Stable Multiple Time Step Simulation/Prediction From Lagged Dynamic Network Regression Models, Journal of Computational and Graphical Statistics, 28:4, 967-979, <DOI: 10.1080/10618600.2019.1594834>.

Client for programmatic access to the South Florida Water Management District's DBHYDRO database at <https://www.sfwmd.gov/science-data/dbhydro>, with functions for accessing hydrologic and water quality data.

This package implements the Dyad Ratios algorithm for estimating latent variables from time-series survey data. The algorithm estimates a latent mood dimension (or two dimensions) from a set of issue opinion series. Supports annual, quarterly, monthly, and daily aggregation intervals, optional exponential smoothing, and up to two latent dimensions. Input data can be provided as a data frame or read from delimited text files. Based on Stimson's MCalc C++ program. See Stimson (2018) <doi:10.1177/0759106318761614> for more details.

This package provides a framework for the identification and analysis of Differentially Expressed Single Nucleotide Polymorphisms (deSNPs) using high-throughput sequencing data. It enables users to import SNP count data from variant files, perform allele-specific read count extraction, and statistically detect SNPs showing significant differences in allele expression between biological conditions or sample groups. This package contains tools for calculating SNP-index and Delta SNP-index from VCF-derived allele depth data with statistical testing and filtering, including sliding-window analysis of genomic regions.

An intuitive, cross-platform graphical data analysis system. It uses menus and dialogs to guide the user efficiently through the data manipulation and analysis process, and has an excel like spreadsheet for easy data frame visualization and editing. Deducer works best when used with the Java based R GUI JGR, but the dialogs can be called from the command line. Dialogs have also been integrated into the Windows Rgui.

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.

Flexibly convert data between long and wide format using just two functions: reshape_toLong() and reshape_toWide().

This package contains data organized by topics: categorical data, regression model, means comparisons, independent and repeated measures ANOVA, mixed ANOVA and ANCOVA.

Collection of functions to help retrieve U.S. Geological Survey and U.S. Environmental Protection Agency water quality and hydrology data from web services.

Fit latent variable linear models, estimating score distributions for groups of people, following Cohen and Jiang (1999) <doi:10.2307/2669917>. In this model, a latent distribution is conditional on students item response, item characteristics, and conditioning variables the user includes. This latent trait is then integrated out. This software is intended to fit the same models as the existing software AM <https://am.air.org/>. As of version 2, also allows the user to draw plausible values.

This package provides access to Dataverse APIs <https://dataverse.org/> (versions 4-5), enabling data search, retrieval, and deposit. For Dataverse versions <= 3.0, use the archived dvn package <https://cran.r-project.org/package=dvn>.

The recovery of visual sensitivity in a dark environment is known as dark adaptation. In a clinical or research setting the recovery is typically measured after a dazzling flash of light and can be described by the Mahroo, Lamb and Pugh (MLP) model of dark adaptation. The functions in this package take dark adaptation data and use nonlinear regression to find the parameters of the model that best describe the data. They do this by firstly, generating rapid initial objective estimates of data adaptation parameters, then a multi-start algorithm is used to reduce the possibility of a local minimum. There is also a bootstrap method to calculate parameter confidence intervals. The functions rely upon a dark list or object. This object is created as the first step in the workflow and parts of the object are updated as it is processed.

Allows users to quickly and easily detect data containing Personally Identifiable Information (PII) through convenience functions.

Calculates the desparsified lasso as originally introduced in van de Geer et al. (2014) <doi:10.1214/14-AOS1221>, and provides inference suitable for high-dimensional time series, based on the long run covariance estimator in Adamek et al. (2020) <doi:10.48550/arXiv.2007.10952>. Also estimates high-dimensional local projections by the desparsified lasso, as described in Adamek et al. (2022) <doi:10.48550/arXiv.2209.03218>.

This package contains an implementation of the d-variable Hilbert Schmidt independence criterion and several hypothesis tests based on it, as described in Pfister et al. (2018) <doi:10.1111/rssb.12235>.

This package provides curated early warning data on landslides in Sri Lanka during the Ditwah storm. It includes structured, machine-readable tidy dataset. This is developed for education and research purposes.

This package provides methods for efficient algebraic operations and factorization of dyadic matrices using Rcpp and RcppArmadillo'. The details of dyadic matrices and the corresponding methodology are described in Kos, M., Podgórski, K., and Wu, H. (2025) <doi:10.48550/arXiv.2505.08144>.

Statistical modelling and forecasting in claims reserving in non-life insurance under the Double Chain Ladder framework by Martinez-Miranda, Nielsen and Verrall (2012).