This package provides tools for temporal disaggregation, including: (1) High-dimensional and low-dimensional series generation for simulation studies; (2) A toolkit for temporal disaggregation and benchmarking using low-dimensional indicator series as proposed by Dagum and Cholette (2006, ISBN:978-0-387-35439-2); (3) Novel techniques by Mosley, Gibberd, and Eckley (2022, <doi:10.1111/rssa.12952>) for disaggregating low-frequency series in the presence of high-dimensional indicator matrices.

Cluster-randomized trials (CRTs) assign treatment to groups rather than individuals, so valid analyses must distinguish cluster-level and individual-level effects and define estimands within a potential-outcomes framework. This package supports right-censored survival outcomes for both single-state (binary) and multi-state settings. For single-state outcomes, it provides estimands based on stage-specific survival contrasts (SPCE) and restricted mean survival time (RMST). For multi-state outcomes, it provides SPCE as well as a generalized win-based restricted mean time-in-favor estimand (RMT-IF). The package implements doubly robust estimators that accommodate covariate-dependent censoring and remain consistent if either the outcome model or the censoring model is correctly specified. Users can choose marginal Cox or gamma-frailty Cox working models for nuisance estimation, and inference is supported via leave-one-cluster-out jackknife variance and confidence interval estimation. Methods are described in Fang et al. (2025) "Estimands and doubly robust estimation for cluster-randomized trials with survival outcomes" <doi:10.48550/arXiv.2510.08438>.

Calculates Distinctiveness Centrality in social networks. For formulas and descriptions, see Fronzetti Colladon and Naldi (2020) <doi:10.1371/journal.pone.0233276>.

Go beyond standard probability distributions such as the Normal or Exponential by combining, shifting, maximizing, and otherwise transforming distributions with simple, verb-based functions. Provides easy access to a broader space of distributions more representative of real-world systems such as river flows or insurance claims. Part of the probaverse framework of packages to support advanced statistical modeling and simulations with an intuitive workflow.

Makes deck.gl <https://deck.gl/>, a WebGL-powered open-source JavaScript framework for visual exploratory data analysis of large datasets, available within R via the htmlwidgets package. Furthermore, it supports basemaps from mapbox <https://www.mapbox.com/> via mapbox-gl-js <https://github.com/mapbox/mapbox-gl-js>.

This package provides a small package containing helper utilities for creating functions for computing statistics.

Edit and validate taxonomic data in compliance with Darwin Core standards (Darwin Core Taxon class <https://dwc.tdwg.org/terms/#taxon>).

Utilities for handling dates and times, such as selecting particular days of the week or month, formatting timestamps as required by RSS feeds, or converting timestamp representations of other software (such as MATLAB and Excel') to R. The package is lightweight (no dependencies, pure R implementations) and relies only on R's standard classes to represent dates and times ('Date and POSIXt'); it aims to provide efficient implementations, through vectorisation and the use of R's native numeric representations of timestamps where possible.

An R interface to the Free Dictionary API <https://dictionaryapi.dev/>, <https://github.com/meetDeveloper/freeDictionaryAPI>. Retrieve dictionary definitions for English words, as well as additional information including phonetics, part of speech, origins, audio pronunciation, example usage, synonyms and antonyms, returned in tidy format for ease of use.

Statistical hypothesis testing of pattern heterogeneity via differences in underlying distributions across multiple contingency tables. Five tests are included: the comparative chi-squared test (Song et al. 2014) <doi:10.1093/nar/gku086> (Zhang et al. 2015) <doi:10.1093/nar/gkv358>, the Sharma-Song test (Sharma et al. 2021) <doi:10.1093/bioinformatics/btab240>, the heterogeneity test, the marginal-change test (Sharma et al. 2020) <doi:10.1145/3388440.3412485>, and the strength test (Sharma et al. 2020) <doi:10.1145/3388440.3412485>. Under the null hypothesis that row and column variables are statistically independent and joint distributions are equal, their test statistics all follow an asymptotically chi-squared distribution. A comprehensive type analysis categorizes the relation among the contingency tables into type null, 0, 1, and 2 (Sharma et al. 2020) <doi:10.1145/3388440.3412485>. They can identify heterogeneous patterns that differ in either the first order (marginal) or the second order (differential departure from independence). Second-order differences reveal more fundamental changes than first-order differences across heterogeneous patterns.

The Ditwah storm began impacting Sri Lanka on 25 November 2025. Ditwah provides a collection of tidy, well-structured datasets to support storm data management, monitoring, and early warning applications in Sri Lanka. The publicly available data were converted to tidy data format for easy analysis. The package processes weather data, flood data and situation report data (families affected, etc.). The package also includes functions for analyzing river level progression and load dashboard visualizations to enhance situational awareness. This is also developed for educational purposes to support learning in data wrangling, visualization, and disaster analytics.

This package provides a fully native R deep learning framework for constructing, training, evaluating, and inspecting Deep Dynamic Ensemble Self Organizing Neural Networks at research scale. The core engine is an object oriented R6 class-based implementation with explicit control over layer layout, dimensional flow, forward propagation, back propagation, and transparent optimizer state updates. The framework does not rely on external deep learning back ends, enabling direct inspection of model state, reproducible numerical behavior, and fine grained architectural control without requiring compiled dependencies or graphics processing unit specific run times. Users can define dimension agnostic single layer or deep multi-layer networks without hard coded architecture limits, with per layer configuration vectors for activation functions, derivatives, dropout behavior, and initialization strategies automatically aligned to network depth through controlled replication or truncation. Reproducible workflows can be executed through high level helpers for fit, run, and predict across binary classification, multi-class classification, and regression modes. Training pipelines support optional self organization, adaptive learning rate behavior, and structured ensemble orchestration in which candidate models are evaluated under user specified performance metrics and selectively promoted or pruned to refine a primary ensemble, enabling controlled ensemble evolution over successive runs. Ensemble evaluation includes fused prediction strategies in which member outputs may be combined through weighted averaging, arithmetic averaging, or voting mechanisms to generate consolidated metrics for research level comparison and reproducible per-seed assessment. The framework supports multiple optimization approaches, including stochastic gradient descent, adaptive moment estimation, and look ahead methods, alongside configurable regularization controls such as L1, L2, and mixed penalties with separate weight and bias update logic. Evaluation features provide threshold tuning, relevance scoring, receiver operating characteristic and precision recall curve generation, area under curve computation, regression error diagnostics, and report ready metric outputs. The package also includes artifact path management, debug state utilities, structured run level metadata persistence capturing seeds, configuration states, thresholds, metrics, ensemble transitions, fused evaluation artifacts, and model identifiers, as well as reproducible scripts and vignettes documenting end to end experiments. Kingma and Ba (2015) <doi:10.48550/arXiv.1412.6980> "Adam: A Method for Stochastic Optimization". Hinton et al. (2012) <https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf> "Neural Networks for Machine Learning (RMSprop lecture notes)". Duchi et al. (2011) <https://jmlr.org/papers/v12/duchi11a.html> "Adaptive Subgradient Methods for Online Learning and Stochastic Optimization". Zeiler (2012) <doi:10.48550/arXiv.1212.5701> "ADADELTA: An Adaptive Learning Rate Method". Zhang et al. (2019) <doi:10.48550/arXiv.1907.08610> "Lookahead Optimizer: k steps forward, 1 step back". You et al. (2019) <doi:10.48550/arXiv.1904.00962> "Large Batch Optimization for Deep Learning: Training BERT in 76 minutes (LAMB)". McMahan et al. (2013) <https://research.google.com/pubs/archive/41159.pdf> "Ad Click Prediction: a View from the Trenches (FTRL-Proximal)". Klambauer et al. (2017) <https://proceedings.neurips.cc/paper/6698-self-normalizing-neural-networks.pdf> "Self-Normalizing Neural Networks (SELU)". Maas et al. (2013) <https://ai.stanford.edu/~amaas/papers/relu_hybrid_icml2013_final.pdf> "Rectifier Nonlinearities Improve Neural Network Acoustic Models (Leaky ReLU / rectifiers)".

There are many different formats dates are commonly represented with: the order of day, month, or year can differ, different separators ("-", "/", or whitespace) can be used, months can be numerical, names, or abbreviations and year given as two digits or four. datefixR takes dates in all these different formats and converts them to R's built-in date class. If datefixR cannot standardize a date, such as because it is too malformed, then the user is told which date cannot be standardized and the corresponding ID for the row. datefixR also allows the imputation of missing days and months with user-controlled behavior.

This package provides functionality to infer trajectories from single-cell data, represent them into a common format, and adapt them. Other biological information can also be added, such as cellular grouping, RNA velocity and annotation. Saelens et al. (2019) <doi:10.1038/s41587-019-0071-9>.

It contains functions to apply blockmodeling of signed (positive and negative weights are assigned to the links), one-mode and valued one-mode and two-mode (two sets of nodes are considered, e.g. employees and organizations) networks (Brusco et al. (2019) <doi:10.1111/bmsp.12192>).

Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event).

Package including an interactive Shiny application for plotting common univariate distributions.

This package provides a facility to generate efficient designs for order-of-additions experiments under pair-wise-order model, see Dennis K. J. Lin and Jiayu Peng (2019)."Order-of-addition experiments: A review and some new thoughts". Quality Engineering, 31:1, 49-59, <doi:10.1080/08982112.2018.1548021>. It also provides a facility to generate component orthogonal arrays under component position model, see Jian-Feng Yang, Fasheng Sun & Hongquan Xu (2020): "A Component Position Model, Analysis and Design for Order-of-Addition Experiments". Technometrics, <doi:10.1080/00401706.2020.1764394>.

This package provides a common interface for applying dimensionality reduction methods, such as Principal Component Analysis ('PCA'), Independent Component Analysis ('ICA'), diffusion maps, Locally-Linear Embedding ('LLE'), t-distributed Stochastic Neighbor Embedding ('t-SNE'), and Uniform Manifold Approximation and Projection ('UMAP'). Has built-in support for sparse matrices.

This package contains data sets, examples and software from the book Design of Observational Studies by Paul R. Rosenbaum, New York: Springer, <doi:10.1007/978-1-4419-1213-8>, ISBN 978-1-4419-1212-1.

The Data Driven I-V Feature Extraction is used to extract Current-Voltage (I-V) features from I-V curves. I-V curves indicate the relationship between current and voltage for a solar cell or Photovoltaic (PV) modules. The I-V features such as maximum power point (Pmp), shunt resistance (Rsh), series resistance (Rs),short circuit current (Isc), open circuit voltage (Voc), fill factor (FF), current at maximum power (Imp) and voltage at maximum power(Vmp) contain important information of the performance for PV modules. The traditional method uses the single diode model to model I-V curves and extract I-V features. This package does not use the diode model, but uses data-driven a method which select different linear parts of the I-V curves to extract I-V features. This method also uses a sampling method to calculate uncertainties when extracting I-V features. Also, because of the partially shaded array, "steps" occurs in I-V curves. The "Segmented Regression" method is used to identify steps in I-V curves. This material is based upon work supported by the U.S. Department of Energyâ s Office of Energy Efficiency and Renewable Energy (EERE) under Solar Energy Technologies Office (SETO) Agreement Number DE-EE0007140. Further information can be found in the following paper. [1] Ma, X. et al, 2019. <doi:10.1109/JPHOTOV.2019.2928477>.

Collects a diverse range of symbolic data and offers a comprehensive set of functions that facilitate the conversion of traditional data into the symbolic data format.

This package provides a system for analyzing descriptive representation, especially for comparing the composition of a political body to the population it represents. Users can compute the expected degree of representation for a body under a random sampling model, the expected degree of representation variability, as well as representation scores from observed political bodies. The package is based on Gerring, Jerzak, and Oncel (2024) <doi:10.1017/S0003055423000680>.

Local linear hazard estimator and its multiplicatively bias correction, including three bandwidth selection methods: best one-sided cross-validation, double one-sided cross-validation, and standard cross-validation.