Duplicated data can exist in different rows and columns and user may need to treat observations (rows) connected by duplicated data as one observation, e.g. companies can belong to one family (and thus: be one company) by sharing some telephone numbers. This package allows to find connected rows based on data on chosen columns and collapse it into one row.

Phase I/II adaptive dose-finding design for single-agent Molecularly Targeted Agent (MTA), according to the paper "Phase I/II Dose-Finding Design for Molecularly Targeted Agent: Plateau Determination using Adaptive Randomization", Riviere Marie-Karelle et al. (2016) <doi:10.1177/0962280216631763>.

Real life data is muddy, fuzzy and unpredictable. This makes data manipulations tedious and bringing the data to right shape alone is a major chunk of work. Functions in this package help us get an understanding of dataframes to dramatically reduces data coding time.

This package provides a collection of widely used univariate data sets of various applied domains on applications of distribution theory. The functions allow researchers and practitioners to quickly, easily, and efficiently access and use these data sets. The data are related to different applied domains and as follows: Bio-medical, survival analysis, medicine, reliability analysis, hydrology, actuarial science, operational research, meteorology, extreme values, quality control, engineering, finance, sports and economics. The total 100 data sets are documented along with associated references for further details and uses.

An easy-to-use yet powerful system for plotting grouped data effect sizes. Various types of effect size can be estimated, then plotted together with a representation of the original data. Select from many possible data representations (box plots, violin plots, raw data points etc.), and combine as desired. Durga plots are implemented in base R, so are compatible with base R methods for combining plots, such as layout()'. See Khan & McLean (2023) <doi:10.1101/2023.02.06.526960>.

Computation of dendrometric and structural parameters from forest inventory data. The objective is to provide a user-friendly R package for researchers, ecologists, foresters, statisticians, loggers and other persons who deal with forest inventory data. The package includes advanced distribution fitting capabilities with multiple estimation methods (Maximum Likelihood, Maximum Product Spacing with ties correction methods following Cheng & Amin (1983), and Method of Moments) for probability distributions commonly used in forestry. Visualization tools with confidence bands using delta method and parametric bootstrap are provided for three-parameter Weibull distribution fitting to diameter data. Useful conversion of angle value from degree to radian, conversion from angle to slope (in percentage) and their reciprocals as well as principal angle determination are also included. Position and dispersion parameters usually found in forest studies are implemented. The package contains Fibonacci series, its extensions and the Golden Number computation. Useful references are Arcadius Y. J. Akossou, Soufianou Arzouma, Eloi Y. Attakpa, Noël H. Fonton and Kouami Kokou (2013) <doi:10.3390/d5010099>, W. Bonou, R. Glele Kakaï, A.E. Assogbadjo, H.N. Fonton, B. Sinsin (2009) <doi:10.1016/j.foreco.2009.05.032>, R. C. H. Cheng and N. A. K. Amin (1983) <doi:10.1111/j.2517-6161.1983.tb01268.x>, and R. C. H. Cheng and M. A. Stephens (1989) <doi:10.1093/biomet/76.2.385>.

Estimation of functional linear mixed models for densely sampled data based on functional principal component analysis.

Dynamic model averaging for binary and continuous outcomes.

Deep compositional spatial models are standard spatial covariance models coupled with an injective warping function of the spatial domain. The warping function is constructed through a composition of multiple elemental injective functions in a deep-learning framework. The package implements two cases for the univariate setting; first, when these warping functions are known up to some weights that need to be estimated, and, second, when the weights in each layer are random. In the multivariate setting only the former case is available. Estimation and inference is done using `tensorflow`, which makes use of graphics processing units. For more details see Zammit-Mangion et al. (2022) <doi:10.1080/01621459.2021.1887741>, Vu et al. (2022) <doi:10.5705/ss.202020.0156>, Vu et al. (2023) <doi:10.1016/j.spasta.2023.100742>, and Shao et al. (2025) <doi:10.48550/arXiv.2505.12548>.

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 performs cluster analysis using an ensemble clustering framework, Chiu & Talhouk (2018) <doi:10.1186/s12859-017-1996-y>. Results from a diverse set of algorithms are pooled together using methods such as majority voting, K-Modes, LinkCluE, and CSPA. There are options to compare cluster assignments across algorithms using internal and external indices, visualizations such as heatmaps, and significance testing for the existence of clusters.

Metrics of difference for comparing pairs of variables or pairs of maps representing real or categorical variables at original and multiple resolutions.

Query database tables over a DBI connection using data.table syntax. Attach database schemas to the search path. Automatically merge using foreign key constraints.

Diffusion Weighted Imaging (DWI) is a Magnetic Resonance Imaging modality, that measures diffusion of water in tissues like the human brain. The package contains R-functions to process diffusion-weighted data. The functionality includes diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), modeling for high angular resolution diffusion weighted imaging (HARDI) using Q-ball-reconstruction and tensor mixture models, several methods for structural adaptive smoothing including POAS and msPOAS, and a streamline fiber tracking for tensor and tensor mixture models. The package provides functionality to manipulate and visualize results in 2D and 3D.

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)".

The DWD provides gridded radar data for Germany in binary format. dwdradar reads these files and enables a fast conversion into numerical format.

Empirical Bayes methods for learning prior distributions from data. An unknown prior distribution (g) has yielded (unobservable) parameters, each of which produces a data point from a parametric exponential family (f). The goal is to estimate the unknown prior ("g-modeling") by deconvolution and Empirical Bayes methods. Details and examples are in the paper by Narasimhan and Efron (2020, <doi:10.18637/jss.v094.i11>).

Consider ambiguity in probabilistic descriptions by replacing a parametric probabilistic description of uncertainty by a non-parametric set of probability distributions in the form of a Density Ratio Class. This is of particular interest in Bayesian inference. The Density Ratio Class is particularly suited for this purpose as it is invariant under Bayesian inference, marginalization, and propagation through a deterministic model. Here, invariant means that the result of the operation applied to a Density Ratio Class is again a Density Ratio Class. In particular the invariance under Bayesian inference thus enables iterative learning within the same framework of Density Ratio Classes. The use of imprecise probabilities in general, and Density Ratio Classes in particular, lead to intervals of characteristics of probability distributions, such as cumulative distribution functions, quantiles, and means. The package is based on a sample of the distribution proportional to the upper bound of the class. Typically this will be a sample from the posterior in Bayesian inference. Based on such a sample, the package provides functions to calculate lower and upper class boundaries and lower and upper bounds of cumulative distribution functions, and quantiles. Rinderknecht, S.L., Albert, C., Borsuk, M.E., Schuwirth, N., Kuensch, H.R. and Reichert, P. (2014) "The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction." Environmental Modelling & Software. 62, 300-315, 2014. <doi:10.1016/j.envsoft.2014.08.020>. Sriwastava, A. and Reichert, P. "Robust Bayesian Estimation of Value Function Parameters using Imprecise Priors." Submitted. <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4973574>.

This package implements an anomaly detection algorithm based on mutual reachability minimum spanning trees: deadwood trims protruding tree segments and marks small debris as outliers; see Gagolewski (2026) <https://deadwood.gagolewski.com/>. More precisely, the use of a mutual reachability distance pulls peripheral points farther away from each other. Tree edges with weights beyond the detected elbow point are removed. All the resulting connected components whose sizes are smaller than a given threshold are deemed anomalous. The Python version of deadwood is available via PyPI'.

This package provides functions to manage databases: select, update, insert, and delete records, list tables, backup tables as CSV files, and import CSV files as tables.

This package provides functions for (1) ranking, selecting, and prioritising genes, proteins, and metabolites from high dimensional biology experiments, (2) multivariate hit calling in high content screens, and (3) combining data from diverse sources.

This package provides tools to create and manipulate probability distributions using S3. Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.

This package provides a comprehensive framework for early epidemic detection through school absenteeism surveillance. The package offers three core functionalities: (1) simulation of population structures, epidemic spread, and resulting school absenteeism patterns; (2) implementation of surveillance models that generate alerts for impending epidemics based on absenteeism data and (3) evaluation of alert timeliness and accuracy through alert time quality metrics to optimize model parameters. These tools enable public health officials and researchers to develop and assess early warning systems before implementation. Methods are based on research published in Vanderkruk et al. (2023) <doi:10.1186/s12889-023-15747-z> and Ward et al. (2019) <doi:10.1186/s12889-019-7521-7>.

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>.