This package provides a data-driven projection-based method for estimating changepoints in high-dimensional time series. Multiple changepoints are estimated using a (wild) binary segmentation scheme.

Using shiny to demo igraph package makes learning graph theory easy and fun.

Versatile tools and data for graph matching analysis with various forms of prior information that supports working with igraph objects, matrix objects, or lists of either.

This package implements a nonparametric maximum likelihood method for assessing potentially time-varying vaccine efficacy (VE) against SARS-CoV-2 infection under staggered enrollment and time-varying community transmission, allowing crossover of placebo volunteers to the vaccine arm. Lin, D. Y., Gu, Y., Zeng, D., Janes, H. E., and Gilbert, P. B. (2021) <doi:10.1093/cid/ciab630>.

This package provides an R version of the InterVA5 software (<http://www.byass.uk/interva/>) for coding cause of death from verbal autopsies. It also provides simple graphical representation of individual and population level statistics.

This package contains data on Post-Secondary Institution Statistics in 2020 <https://nces.ed.gov/ipeds/use-the-data>. The package allows easy access to a wide variety of information regarding Post-secondary Institutions, its students, faculty, and their demographics, financial aid, educational and recreational offerings, and completions. This package can be used by students, college counselors, or involved parents interested in pursuing higher education, considering their options, and securing admission into their school of choice.

It performs interlaboratory studies (ILS) to detect those laboratories that provide non-consistent results when comparing to others. It permits to work simultaneously with various testing materials, from standard univariate, and functional data analysis (FDA) perspectives. The univariate approach based on ASTM E691-08 consist of estimating the Mandel's h and k statistics to identify those laboratories that provide more significant different results, testing also the presence of outliers by Cochran and Grubbs tests, Analysis of variance (ANOVA) techniques are provided (F and Tuckey tests) to test differences in means corresponding to different laboratories per each material. Taking into account the functional nature of data retrieved in analytical chemistry, applied physics and engineering (spectra, thermograms, etc.). ILS package provides a FDA approach for finding the Mandel's k and h statistics distribution by smoothing bootstrap resampling.

Utility functions that implement and automate common sets of validation tasks. These functions are particularly useful to validate inputs, intermediate objects and output values in user-defined functions, resulting in tidier and less verbose functions.

Ternary plots made simple. This package allows to create ternary plots using graphics'. It provides functions to display the data in the ternary space, to add or tune graphical elements and to display statistical summaries. It also includes common ternary diagrams which are useful for the archaeologist (e.g. soil texture charts, ceramic phase diagram).

Categorization and scoring of injury severity typically involves trained personnel with access to injured persons or their medical records. icdpicr contains a function that provides automated calculation of Abbreviated Injury Scale ('AIS') and Injury Severity Score ('ISS') from International Classification of Diseases ('ICD') codes and may be a useful substitute to manual injury severity scoring. ICDPIC was originally developed in Stata', and icdpicr is an open-access update that accepts both ICD-9 and ICD-10 codes.

This package implements the integrative conditional autoregressive horseshoe model discussed in Jendoubi, T., Ebbels, T.M. Integrative analysis of time course metabolic data and biomarker discovery. BMC Bioinformatics 21, 11 (2020) <doi:10.1186/s12859-019-3333-0>. The model consists in three levels: Metabolic pathways level modeling interdependencies between variables via a conditional auto-regressive (CAR) component, integrative analysis level to identify potential associations between heterogeneous omic variables via a Horseshoe prior and experimental design level to capture experimental design conditions through a mixed-effects model. The package also provides functions to simulate data from the model, construct pathway matrices, post process and plot model parameters.

This package contains bibliographic information for the U.S. Geological Survey (USGS) Idaho National Laboratory (INL) Project Office.

Tree height is an important dendrometric variable and forms the basis of vertical structure of a forest stand. This package will help to fit and validate various non-linear height diameter models for assessing the underlying relationship that exists between tree height and diameter at breast height in case of conifer trees. This package has been implemented on Naslund, Curtis, Michailoff, Meyer, Power, Michaelis-Menten and Wykoff non linear models using algorithm of Huang et al. (1992) <doi:10.1139/x92-172> and Zeide et al. (1993) <doi:10.1093/forestscience/39.3.594>.

Instrumental variable (IV) estimators for homogeneous and heterogeneous treatment effects with efficient machine learning instruments. The estimators are based on double/debiased machine learning allowing for nonlinear and potentially high-dimensional control variables. Details can be found in Scheidegger, Guo and Bühlmann (2025) "Inference for heterogeneous treatment effects with efficient instruments and machine learning" <doi:10.48550/arXiv.2503.03530>.

This package implements the compartment model from Tokars (2018) <doi:10.1016/j.vaccine.2018.10.026>. This enables quantification of population-wide impact of vaccination against vaccine-preventable diseases such as influenza.

Estimates the density of a spatially distributed animal population sampled with an array of passive detectors, such as traps. Models incorporating distance-dependent detection are fitted by simulation and inverse prediction as proposed by Efford (2004) <doi:10.1111/j.0030-1299.2004.13043.x>.

An implementation of randomization-based hypothesis testing for three different estimands in a cluster-randomized encouragement experiment. The three estimands include (1) testing a cluster-level constant proportional treatment effect (Fisher's sharp null hypothesis), (2) pooled effect ratio, and (3) average cluster effect ratio. To test the third estimand, user needs to install Gurobi (>= 9.0.1) optimizer via its R API. Please refer to <https://www.gurobi.com/documentation/9.0/refman/ins_the_r_package.html>.

Allows an interactive assessment of the timing of interim analyses. The algorithm simulates both the recruitment and treatment/event phase of a clinical trial based on the package interim'.

Time parceling method and Bayesian variability modeling methods for modeling within individual variability indicators as predictors.For more details, see <https://github.com/xliu12/IIVpredicitor>.

This package provides a collection of statistical tests for genetic association studies and summary data based Mendelian randomization.

This package provides functions for converting time series of spatial abundance or density data in raster format to vector fields of population movement using the digital image correlation technique. More specifically, the functions in the package compute cross-covariance using discrete fast Fourier transforms for computational efficiency. Vectors in vector fields point in the direction of highest two dimensional cross-covariance. The package has a novel implementation of the digital image correlation algorithm that is designed to detect persistent directional movement when image time series extend beyond a sequence of two raster images.

This toolbox makes working with oxygen, carbon, and clumped isotope data reproducible and straightforward. Use it to quickly calculate isotope fractionation factors, and apply paleothermometry equations.

Imputation (replacement) of missing values in univariate time series. Offers several imputation functions and missing data plots. Available imputation algorithms include: Mean', LOCF', Interpolation', Moving Average', Seasonal Decomposition', Kalman Smoothing on Structural Time Series models', Kalman Smoothing on ARIMA models'. Published in Moritz and Bartz-Beielstein (2017) <doi:10.32614/RJ-2017-009>.

This package implements likelihood based methods for mediation analysis.