Multiple Imputation for Informative Censoring. This package implements two methods. Gamma Imputation described in <DOI:10.1002/sim.6274> and Risk Score Imputation described in <DOI:10.1002/sim.3480>.

This package provides datasets and supporting functions for the book Introduction to Biostatistics with R by Schifano and Yan (2026+), published by Taylor & Francis. The package is intended for teaching introductory biostatistics and for reproducing examples in the text.

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).

An implementation of the Otsu's Image Segmentation Method described in the paper: "A C++ Implementation of Otsu's Image Segmentation Method". The algorithm is explained at <doi:10.5201/ipol.2016.158>.

It provides multiple functions that are useful for ecological research and teaching statistics to ecologists. It is based on data analysis courses offered at the Instituto de Ecologà a AC (INECOL). For references and published evidence see, Manrique-Ascencio, et al (2024) <doi:10.1111/gcb.17282>, Manrique-Ascencio et al (2024) <doi:10.1111/plb.13683>, Ruiz-Guerra et al(2017) <doi:10.17129/botsci.812>, Juarez-Fragoso et al (2024) <doi:10.1007/s10980-024-01809-z>, Papaqui-Bello et al (2024) <doi:10.13102/sociobiology.v71i2.10503>.

The goal is to improve a permutation-based approach (R package: pARI') for simultaneous inference on the true discovery proportion by a branch-and-bound algorithm. It is designed to return a list with a bracketing for the true discovery proportion, rather than a single lower bound from pARI'. For more details see Andreella. A (2023) <doi:10.1002/sim.9725>.

Distributional regression under stochastic order restrictions for numeric and binary response variables and partially ordered covariates, including right-censored responses via Survival-IDR. See Henzi, Ziegel, Gneiting (2021) <doi:10.1111/rssb.12450> and Bladt, Henzi, van den Heuvel, Ziegel (2026) <doi:10.48550/arXiv.2608.02914>.

Algorithms and utility functions for indoor positioning using fingerprinting techniques. These functions are designed for manipulation of RSSI (Received Signal Strength Intensity) data sets, estimation of positions,comparison of the performance of different models, and graphical visualization of data. Machine learning algorithms and methods such as k-nearest neighbors or probabilistic fingerprinting are implemented in this package to perform analysis and estimations over RSSI data sets.

Launches a shiny based application for Nuclear Magnetic Resonance (NMR)data importation and Statistical TOtal Correlation SpectroscopY (STOCSY) analyses in a full interactive approach. The theoretical background and applications of STOCSY method could be found at Cloarec, O., Dumas, M. E., Craig, A., Barton, R. H., Trygg, J., Hudson, J., Blancher, C., Gauguier, D., Lindon, J. C., Holmes, E. & Nicholson, J. (2005) <doi:10.1021/ac048630x>.

This package implements design-aware, margin-constrained estimation of population indicators for geographic areas crossed with demographic subgroups. Complex-survey microdata supply outcomes and association information, while one or more aggregate census tables supply overlapping population margins. A generalized iterative proportional fitting engine reconstructs a coherent latent population table, and an augmented model-assisted estimator produces domain means, proportions, and totals for sampled and unsampled intersections. Tools diagnose non-identification, compute linear-programming sensitivity bounds, propagate sampling and model uncertainty with replicate-weight or multiplier bootstrap procedures, enforce structural zeros, and benchmark estimates to official totals. The framework extends calibration ideas from Deville and Sarndal (1992) <doi:10.1080/01621459.1992.10475217> and small area estimation ideas from Fay and Herriot (1979) <doi:10.1080/01621459.1979.10482505>.

Visualize interactions between multiple experimental factors using interactive 3D surface plots powered by plotly'. Instead of examining combinatorial pairwise interaction plots, map factor combinations to response surfaces and use surface crossings as geometric indicators of interaction effects. Supports continuous, categorical, and mixed factor designs with automatic binning for continuous conditioning variables.

This package provides direct access to the macroeconomic, financial, and regional database maintained by the Institute for Applied Economic Research (Ipea) via the Ipeadata API. For more information, see <https://www.ipeadata.gov.br/>.

It offers a sophisticated and versatile tool for creating and evaluating artificial intelligence based neural network models tailored for regression analysis on datasets with continuous target variables. Leveraging the power of neural networks, it allows users to experiment with various hidden neuron configurations across two layers, optimizing model performance through "5 fold"" or "10 fold"" cross validation. The package normalizes input data to ensure efficient training and assesses model accuracy using key metrics such as R squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Percentage Error (PER). By storing and visualizing the best performing models, it provides a comprehensive solution for precise and efficient regression modeling making it an invaluable tool for data scientists and researchers aiming to harness AI for predictive analytics.

This package provides a literature-derived database of immune cell markers formatted as Gene Matrix Transposed (GMT) files. Users can search immune cell signatures, retrieve marker lists, export GMT files, create custom marker sets, and score gene-by-cell expression matrices with dependency-free rank-based or mean-expression methods. Cell subpopulations are distinguished by their source PMIDs. For the core curation of the lung cell atlas, see Travaglini et al. (2020) <doi:10.1038/s41586-020-2922-4>. For the pan-cancer B cell signatures, see Fitzsimons et al. (2024) <doi:10.1016/j.ccell.2024.09.011>.

This package implements likelihood based methods for mediation analysis.

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>.

R dependency injection framework. Dependency injection allows a program design to follow the dependency inversion principle. The user delegates to external code (the injector) the responsibility of providing its dependencies. This separates the responsibilities of use and construction.

An implementation of various methods for estimating intrinsic dimension of vector-valued dataset or distance matrix. Most methods implemented are based on different notion of fractal dimension such as the capacity dimension, the box-counting dimension, and the information dimension.

Multivariate outlier detection is performed using invariant coordinates where the package offers different methods to choose the appropriate components. ICS is a general multivariate technique with many applications in multivariate analysis. ICSOutlier offers a selection of functions for automated detection of outliers in the data based on a fitted ICS object or by specifying the dataset and the scatters of interest. The current implementation targets data sets with only a small percentage of outliers.

The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.

It provides in-place operators for R that are equivalent to +=', -=', *=', /= in C++. Those can be applied on integer|double vectors|matrices. You have also access to sweep operations (in-place).

Download and manage data sets of statistical projects and geographic data created by Instituto Nacional de Estadistica y Geografia (INEGI). See <https://www.inegi.org.mx/>.

This is the central location for data and tools for the development, maintenance, analysis, and deployment of the International Soil Radiocarbon Database (ISRaD). ISRaD was developed as a collaboration between the U.S. Geological Survey Powell Center and the Max Planck Institute for Biogeochemistry. This R package provides tools for accessing and manipulating ISRaD data, compiling local data using the ISRaD data structure, and simple query and reporting functions for ISRaD. For more detailed information visit the ISRaD website at: <https://soilradiocarbon.org/>.

This package implements information-theoretic measures to explore variable interactions, including KSG mutual information estimation for continuous variables from Kraskov et al. (2004) <doi:10.1103/PhysRevE.69.066138>, knockoff conditional mutual information described in Zhang & Chen (2025) <doi:10.1126/sciadv.adu6464>, synergistic-unique-redundant decomposition introduced by Martinez-Sanchez et al. (2024) <doi:10.1038/s41467-024-53373-4>, allowing detection of complex and diverse relationships among variables.