One function to read files. One function to write files. One function to direct plots to screen or file. Automatic file format inference and directory structure creation.

An R package for inferring cell-type specific gene regulatory network from single-cell RNA-seq data.

This package provides a Bayesian framework that integrates several regression models to identify a parsimonious set of biomarkers shared across disparate data platforms (for example genomic, transcriptomic and proteomic assays). Subjects are partitioned into subgroups defined by their pattern of platform availability, so that no subject with partially missing platform data is excluded, and information is borrowed across subgroups through a Markov random field prior on the variable-selection indicators together with non-local (product moment) priors on the regression effects. The methodology was introduced for time-to-event outcomes by Chekouo, Stingo, Doecke and Do (2017) <doi:10.1111/biom.12587>; this package additionally supports continuous (Gaussian) and binary (probit) outcomes. Posterior inference is carried out by a Markov chain Monte Carlo sampler implemented in C for computational efficiency.

Includes a collection of shiny applications to demonstrate or to explore fundamental item response theory (IRT) concepts such as estimation, scoring, and multidimensional IRT models.

The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data, as well as functions to calibrate radiocarbon dates.

Implementation of the methodology proposed in Data-driven design of targeted gene panels for estimating immunotherapy biomarkers', Bradley and Cannings (2021) <arXiv:2102.04296>. This package allows the user to fit generative models of mutation from an annotated mutation dataset, and then further to produce tunable linear estimators of exome-wide biomarkers. It also contains functions to simulate mutation annotated format (MAF) data, as well as to analyse the output and performance of models.

Simulation of the random evolution of heterogeneous populations using stochastic Individual-Based Models (IBMs) <doi:10.48550/arXiv.2303.06183>. The package enables users to simulate population evolution, in which individuals are characterized by their age and some characteristics, and the population is modified by different types of events, including births/arrivals, death/exit events, or changes of characteristics. The frequency at which an event can occur to an individual can depend on their age and characteristics, but also on the characteristics of other individuals (interactions). Such models have a wide range of applications. For instance, IBMs can be used for simulating the evolution of a heterogeneous insurance portfolio with selection or for validating mortality forecasts. This package overcomes the limitations of time-consuming IBMs simulations by implementing new efficient algorithms based on thinning methods, which are compiled using the Rcpp package while providing a user-friendly interface.

Graphical visualization tools for analyzing the data produced by irace'. The iraceplot package enables users to analyze the performance and the parameter space data sampled by the configuration during the search process. It provides a set of functions that generate different plots to visualize the configurations sampled during the execution of irace and their performance. The functions just require the log file generated by irace and, in some cases, they can be used with user-provided data.

Sample states from the Ising model and compute the probability of states. Sampling can be done for any number of nodes, but due to the intractability of the Ising model the distribution can only be computed up to roughly 10 nodes. The Blume-Capel model, an Ising model with an additional on-site quadratic (crystal-field) term, is also supported.

Make empirical Bayes incidence curves from reported case data using a specified delay distribution.

Most existing approaches for network reconstruction can only infer an overall network and, also, fail to capture a complete set of network properties. To address these issues, a new model has been developed, which converts static data into their dynamic form. idopNetwork is an R interface to this model, it can inferring informative, dynamic, omnidirectional and personalized networks. For more information on functional clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>, Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model, see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) <doi:10.1080/19490976.2022.2106103>.

Computes bootstrapped Monte Carlo estimate of p value of Kolmogorov-Smirnov (KS) test and likelihood ratio test for zero-inflated count data, based on the work of Aldirawi et al. (2019) <doi:10.1109/BHI.2019.8834661>. With the package, user can also find tools to simulate random deviates from zero inflated or hurdle models and obtain maximum likelihood estimate of unknown parameters in these models.

This package provides a set of functions to run simple and composite box-models to describe the dynamic or static distribution of stable isotopes in open or closed systems. The package also allows the sweeping of many parameters in both static and dynamic conditions. The mathematical models used in this package are derived from Albarede, 1995, Introduction to Geochemical Modelling, Cambridge University Press, Cambridge <doi:10.1017/CBO9780511622960>.

Derivation of indexes for benchmarking purposes. A methodology with flexible number of constituents is implemented. Also functions for market capitalization and volume weighted indexes with fixed number of constituents are available. The main function of the package, indexComp(), provides the derived index, suitable for analysis purposes. The functions indexUpdate(), indexMemberSelection() and indexMembersUpdate() are components of indexComp() and enable one to construct and continuously update an index, e.g. for display on a website. The methodology behind the functions provided gets introduced in Trimborn and Haerdle (2018) <doi:10.1016/j.jempfin.2018.08.004>.

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

Allows for the non-parametric estimation of transition intensities in interval-censored multi-state models using the approach of Gomon and Putter (2024) <doi:10.48550/arXiv.2409.07176> or Gu et al. (2023) <doi:10.1093/biomet/asad073>.

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 provides a general-purpose framework for Interpretable Contextual-Accountable and Responsible Machine Learning (ICARM) that works with any clean tabular data across any application domain including healthcare, finance, social science, business, and education. Automatically detects whether a prediction task is binary classification, multi-class classification, or regression from the target variable type. Provides a unified entry point icarm_fit() supporting both interpretable learners (Classification and Regression Trees (CART), logistic regression, linear regression, Generalized Additive Models (GAM)) and extended learners (random forest, XGBoost', Support Vector Machines (SVM)) with consistent interfaces for global and local model explanation including approximate SHapley Additive exPlanations (SHAP) values and Partial Dependence Profiles (PDPs), learning curve diagnostics, group-level fairness auditing across protected attributes, probability calibration, threshold analysis, multi-model comparison, reproducible JavaScript Object Notation (JSON) audit trails, and accountability scorecards. The contextual accountability framing emphasises that algorithmic fairness and interpretability requirements depend on the deployment domain and must be evaluated accordingly. Extends the civic.icarm framework (Awe 2025) <https://cran.r-project.org/package=civic.icarm> to general-purpose applications beyond civic and political education.

This package provides functions to calculate the requisite sample size for studies where ICC is the primary outcome. Can also be used for calculation of power. In both cases it allows the user to test the impact of changing input variables by calculating the outcome for several different values of input variables. Based off the work of Zou. Zou, G. Y. (2012). Sample size formulas for estimating intraclass correlation coefficients with precision and assurance. Statistics in medicine, 31(29), 3972-3981.

Creation of tables of summary statistics or counts for clinical data (for TLFs'). These tables can be exported as in-text table (with the flextable package) for a Clinical Study Report (Word format) or a topline presentation (PowerPoint format), or as interactive table (with the DT package) to an html document for clinical data review.

This package provides classes and methods for seismic data analysis. The base classes and methods are inspired by the python code found in the ObsPy python toolbox <https://github.com/obspy/obspy>. Additional classes and methods support data returned by web services provided by the EarthScope Consortium. <https://service.earthscope.org/>.

Interesting igraph datasets from Melanie Walsh's sample social network datasets repository <https://github.com/melaniewalsh/sample-social-network-datasets>.

This package provides a key-value store data structure. The keys are integers and the values can be any R object. This is like a list but indexed by a set of integers, not necessarily contiguous and possibly negative. The implementation uses a R6 class. These containers are not faster than lists but their usage can be more convenient for certain situations.

This software does Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. An overview of this software, including references presenting details on the methods, can be found here: <https://www.fda.gov/medical-devices/science-and-research-medical-devices/imrmc-software-do-multi-reader-multi-case-statistical-analysis-reader-studies>.