This package provides a collection of novel tools for generating species distribution and abundance models (SDM) that are dynamic through both space and time. These highly flexible functions incorporate spatial and temporal aspects across key SDM stages; including when cleaning and filtering species occurrence data, generating pseudo-absence records, assessing and correcting sampling biases and autocorrelation, extracting explanatory variables and projecting distribution patterns. Throughout, functions utilise Google Earth Engine and Google Drive to minimise the computing power and storage demands associated with species distribution modelling at high spatio-temporal resolution.

Templates and data files to support "Discrete Choice Analysis with R", Páez, A. and Boisjoly, G. (2023) <doi:10.1007/978-3-031-20719-8>.

Detection and attribution of climate change using methods including optimal fingerprinting via generalized total least squares or an estimating equation approach (Li et al., 2025, <doi:10.1175/JCLI-D-24-0193.1>; Ma et al., 2023, <doi:10.1175/JCLI-D-22-0681.1>). Provides shrinkage estimators for the covariance matrix following Ledoit and Wolf (2004, <doi:10.1016/S0047-259X(03)00096-4>) and Ledoit and Wolf (2017, <doi:10.2139/ssrn.2383361>).

Generalised model for population dynamics of invasive Aedes mosquitoes. Rationale and model structure are described here: Da Re et al. (2021) <doi:10.1016/j.ecoinf.2020.101180> and Da Re et al. (2022) <doi:10.1101/2021.12.21.473628>.

This package contains Data frames and functions used in the book "Design and Analysis of Experiments with R", Lawson(2015) ISBN-13:978-1-4398-6813-3.

This package implements the distribution-free goodness-of-fit regression test for the mean structure of parametric models introduced in Khmaladze (2021) <doi:10.1007/s10463-021-00786-3>. The test is implemented for general functions with minimal distributional assumptions as well as common models (e.g., lm, glm) with the usual assumptions.

Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.

Could be used to obtain spatial depths, spatial ranks and outliers of multivariate random variables. Could also be used to visualize DD-plots (a multivariate generalization of QQ-plots).

Implementations of the multiple testing procedures for discrete tests described in the paper Döhler, Durand and Roquain (2018) "New FDR bounds for discrete and heterogeneous tests" <doi:10.1214/18-EJS1441>. The main procedures of the paper (HSU and HSD), their adaptive counterparts (AHSU and AHSD), and the HBR variant are available and are coded to take as input the results of a test procedure from package DiscreteTests', or a set of observed p-values and their discrete support under their nulls. A shortcut function to obtain such p-values and supports is also provided, along with a wrapper allowing to apply discrete procedures directly to data.

Dynamic Reservoir Simulation Model (DYRESM) and Computational Aquatic Ecosystem Dynamics Model (CAEDYM) model development, including assisting with calibrating selected model parameters and visualising model output through time series plot, profile plot, contour plot, and scatter plot. For more details, see Yu et al. (2023) <https://journal.r-project.org/articles/RJ-2023-008/>.

This package provides tools to estimate and manage empirical distributions, which should work with survey data. One of the main features is the possibility to create data cubes of estimated statistics, that include all the combinations of the variables of interest (see for example functions dcc5() and dcc6()).

Lightweight utility functions used for the R package development infrastructure inside the data integration centers ('DIZ') to standardize and facilitate repetitive tasks such as setting up a database connection or issuing notification messages and to avoid redundancy.

Computes the double bootstrap as discussed in McKnight, McKean, and Huitema (2000) <doi:10.1037/1082-989X.5.1.87>. The double bootstrap method provides a better fit for a linear model with autoregressive errors than ARIMA when the sample size is small.

Implementation of the Dual Feature Reduction (DFR) approach for the Sparse Group Lasso (SGL) and the Adaptive Sparse Group Lasso (aSGL) (Feser and Evangelou (2024) <doi:10.48550/arXiv.2405.17094>). The DFR approach is a feature reduction approach that applies strong screening to reduce the feature space before optimisation, leading to speed-up improvements for fitting SGL (Simon et al. (2013) <doi:10.1080/10618600.2012.681250>) and aSGL (Mendez-Civieta et al. (2020) <doi:10.1007/s11634-020-00413-8> and Poignard (2020) <doi:10.1007/s10463-018-0692-7>) models. DFR is implemented using the Adaptive Three Operator Splitting (ATOS) (Pedregosa and Gidel (2018) <doi:10.48550/arXiv.1804.02339>) algorithm, with linear and logistic SGL models supported, both of which can be fit using k-fold cross-validation. Dense and sparse input matrices are supported.

Here, a function has been developed to generate parameters of the input designs, as well as incidence matrices. This is a general function that can be used to investigate the characterization properties of any block design.

It is used to identify dysregulated pathways based on a pre-ranked gene pair list. A fast algorithm is used to make the computation really fast. The data in package DysPIAData is needed.

This package provides Python-based extensions to enhance data analytics workflows, particularly for tasks involving data preprocessing and predictive modeling. Includes tools for data sampling, transformation, feature selection, balancing strategies (e.g., SMOTE), and model construction. These capabilities leverage Python libraries via the reticulate interface, enabling seamless integration with a broader machine learning ecosystem. Supports instance selection and hybrid workflows that combine R and Python functionalities for flexible and reproducible analytical pipelines. The architecture is inspired by the Experiment Lines approach, which promotes modularity, extensibility, and interoperability across tools. More information on Experiment Lines is available in Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.

This package provides a drop-in replacement for dplyr', powered by DuckDB for performance. Offers convenient utilities for working with in-memory and larger-than-memory data while retaining full dplyr compatibility.

Validate dataset by columns and rows using convenient predicates inspired by assertr package. Generate good looking HTML report or print console output to display in logs of your data processing pipeline.

Gives access to data visualisation methods that are relevant from the data scientist's point of view. The flagship idea of DataVisualizations is the mirrored density plot (MD-plot) for either classified or non-classified multivariate data published in Thrun, M.C. et al.: "Analyzing the Fine Structure of Distributions" (2020), PLoS ONE, <DOI:10.1371/journal.pone.0238835>. The MD-plot outperforms the box-and-whisker diagram (box plot), violin plot and bean plot and geom_violin plot of ggplot2. Furthermore, a collection of various visualization methods for univariate data is provided. In the case of exploratory data analysis, DataVisualizations makes it possible to inspect the distribution of each feature of a dataset visually through a combination of four methods. One of these methods is the Pareto density estimation (PDE) of the probability density function (pdf). Additionally, visualizations of the distribution of distances using PDE, the scatter-density plot using PDE for two variables as well as the Shepard density plot and the Bland-Altman plot are presented here. Pertaining to classified high-dimensional data, a number of visualizations are described, such as f.ex. the heat map and silhouette plot. A political map of the world or Germany can be visualized with the additional information defined by a classification of countries or regions. By extending the political map further, an uncomplicated function for a Choropleth map can be used which is useful for measurements across a geographic area. For categorical features, the Pie charts, slope charts and fan plots, improved by the ABC analysis, become usable. More detailed explanations are found in the book by Thrun, M.C.: "Projection-Based Clustering through Self-Organization and Swarm Intelligence" (2018) <DOI:10.1007/978-3-658-20540-9>.

Shiny modules to import data into an application or addin from various sources, and to manipulate them after that.

Flexible and efficient cleaning of data with interactivity. datacleanr facilitates best practices in data analyses and reproducibility with built-in features and by translating interactive/manual operations to code. The package is designed for interoperability, and so seamlessly fits into reproducible analyses pipelines in R'.

This package provides wrapper of various machine learning models. In applied machine learning, there is a strong belief that we need to strike a balance between interpretability and accuracy. However, in field of the interpretable machine learning, there are more and more new ideas for explaining black-box models, that are implemented in R'. DALEXtra creates DALEX Biecek (2018) <doi:10.48550/arXiv.1806.08915> explainer for many type of models including those created using python scikit-learn and keras libraries, and java h2o library. Important part of the package is Champion-Challenger analysis and innovative approach to model performance across subsets of test data presented in Funnel Plot.

Estimation of the total population size from capture-recapture data efficiently and with low bias implementing the methods from Das M, Kennedy EH, and Jewell NP (2021) <arXiv:2104.14091>. The estimator is doubly robust against errors in the estimation of the intermediate nuisance parameters. Users can choose from the flexible estimation models provided in the package, or use any other preferred model.