This package provides an interface to PDFMiner <https://github.com/pdfminer/pdfminer.six> a Python package for extracting information from PDF'-files. PDFMiner has the goal to get all information available in a PDF'-file, position of the characters, font type, font size and informations about lines. Which makes it the perfect starting point for extracting tables from PDF'-files. More information can be found in the package README'-file.

These functions use data augmentation and Bayesian techniques for the assessment of single-member and incomplete ensembles of climate projections. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates. - Evin, G., B. Hingray, J. Blanchet, N. Eckert, S. Morin, and D. Verfaillie. (2019) <doi:10.1175/JCLI-D-18-0606.1>.

This package provides a set of functions of increasing complexity allows users to (1) convert QuadKey-identified datasets, based on Microsoft's Bing Maps Tile System', into Simple Features data frames, (2) transform Simple Features data frames into rasters, and (3) process multiple Meta ('Facebook') QuadKey-identified human mobility files directly into raster files. For more details, see Dâ Andrea et al. (2024) <doi:10.21105/joss.06500>.

This package provides a fast implementation of the weighted information similarity aggregation (WISE) test for detecting serial dependence, particularly suited for high-dimensional and non-Euclidean time series. Includes functions for constructing similarity matrices and conducting hypothesis testing. Users can use different similarity measures and define their own weighting schemes. For more details see Q Zhu, M Liu, Y Han, D Zhou (2025) <doi:10.48550/arXiv.2509.05678>.

Includes built-in methods for generating long SQL CASE statements, and other SQL statements that may otherwise be arduous to construct by hand.The generated statement can easily be concatenated to string literals to form queries to SQL'-like databases, such as when using the RODBC package. The current methods include casewhen() for building CASE statements, inlist() for building IN statements, and updatetable() for building UPDATE statements.

This package provides a general framework for statistical simulation, which allows researchers to make use of a wide range of simulation designs with minimal programming effort. The package provides functionality for drawing samples from a distribution or a finite population, for adding outliers and missing values, as well as for visualization of the simulation results. It follows a clear object-oriented design and supports parallel computing to increase computational performance.

This package provides a novel spatial topic model to integrate both cell type and spatial information to identify the complex spatial tissue architecture on multiplexed tissue images without human intervention. The Package implements a collapsed Gibbs sampling algorithm for inference. SpaTopic is scalable to large-scale image datasets without extracting neighborhood information for every single cell. For more details on the methodology, see <https://xiyupeng.github.io/SpaTopic/>.

Provide estimation and data generation tools for the skew-unit family discussed based on Mukhopadhyay and Brani (1995) <doi:10.2307/2348710>. The family contains extensions for popular distributions such as the ArcSin discussed in Arnold and Groeneveld (1980) <doi:10.1080/01621459.1980.10477449>, triangular, U-quadratic and Johnson-SB proposed in Cortina-Borja (2006) <doi:10.1111/j.1467-985X.2006.00446_12.x> distributions, among others.

This package provides a very nice interface to Princeton's WordNet without rJava dependency. WordNet data is not included. Princeton University makes WordNet available to research and commercial users free of charge provided the terms of their license (<https://wordnet.princeton.edu/license-and-commercial-use>) are followed, and proper reference is made to the project using an appropriate citation (<https://wordnet.princeton.edu/citing-wordnet>).

Finding an optimal Bayesian experimental design involves maximizing an objective function given by the expectation of some appropriately chosen utility function with respect to the joint distribution of unknown quantities (including responses). This objective function is usually not available in closed form and the design space can be continuous and of high dimensionality. This package uses Approximate Coordinate Exchange (ACE) to maximise an approximation to the expectation of the utility function.

This package provides a multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The Galgo framework combines the advantages of clustering algorithms for grouping heterogeneous omics data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high.

This package provides a lightweight but powerful R interface to the Azure Resource Manager REST API. The package exposes a comprehensive class framework and related tools for creating, updating and deleting Azure resource groups, resources and templates. While AzureRMR can be used to manage any Azure service, it can also be extended by other packages to provide extra functionality for specific services. Part of the AzureR family of packages.

Implementation of the bunching estimator for kinks and notches. Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). It produces publication-ready plots in the style followed since Chetty et al. (2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.

Fit Bayesian models using brms'/'Stan with parsnip'/'tidymodels via bayesian <doi:10.5281/zenodo.4426836>. tidymodels is a collection of packages for machine learning; see Kuhn and Wickham (2020) <https://www.tidymodels.org>). The technical details of brms and Stan are described in Bürkner (2017) <doi:10.18637/jss.v080.i01>, Bürkner (2018) <doi:10.32614/RJ-2018-017>, and Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

This package provides functions to align curves and to compute mean curves based on the elastic distance defined in the square-root-velocity framework. For more details on this framework see Srivastava and Klassen (2016, <doi:10.1007/978-1-4939-4020-2>). For more theoretical details on our methods and algorithms see Steyer et al. (2023, <doi:10.1111/biom.13706>) and Steyer et al. (2023, <arXiv:2305.02075>).

Estimates and provides inference for quantities that assess high dimensional mediation and potential surrogate markers including the direct effect of treatment, indirect effect of treatment, and the proportion of treatment effect explained by a surrogate/mediator; details are described in Zhou et al (2022) <doi:10.1002/sim.9352> and Zhou et al (2020) <doi:10.1093/biomet/asaa016>. This package relies on the optimization software MOSEK', <https://www.mosek.com>.

The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.

This algorithm is described in detail in the paper "Hedging Forecast Combinations With an Application to the Random Forest" by Beck et al. (2024) <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5032102>. The package provides a function hedgedrf() that can be used to train a Hedged Random Forest model on a dataset, and a function predict.hedgedrf() that can be used to make predictions with the model.

The wiDB...() functions provide an interface to the public API of the wiDB <https://github.com/SPATIAL-Lab/isoWater/blob/master/Protocol.md>: build, check and submit queries, and receive and unpack responses. Data analysis functions support Bayesian inference of the source and source isotope composition of water samples that may have experienced evaporation. Algorithms adapted from Bowen et al. (2018, <doi:10.1007/s00442-018-4192-5>).

The app will calculate the ICER (incremental cost-effectiveness ratio) Rawlins (2012) <doi:10.1016/B978-0-7020-4084-9.00044-6> from the mean costs and quality-adjusted life years (QALY) Torrance and Feeny (2009) <doi:10.1017/S0266462300008461> for a set of treatment options, and draw the efficiency frontier in the costs-effectiveness plane. The app automatically identifies and excludes dominated and extended-dominated options from the ICER calculation.

Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying binary graph. This procedure comes with a control of the false discovery rate. The method is described in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, E. Roquain, F. Villers (2020) <arXiv:1907.10176>.

Offers a comprehensive collection of penguin-related datasets suitable for descriptive statistics, hypothesis testing, and experimental design. Derived from open ecological and biological sources such as Palmer Station studies, the package integrates datasets covering adult morphology, clutch size, blood isotope composition, and heart rate. It is designed for researchers, students, and educators to explore statistical methods including ANOVA, regression, multivariate analysis, and design of experiments in an accessible and reproducible context.

This package provides functions to compute split generalized linear models. The approach fits generalized linear models that split the covariates into groups. The optimal split of the variables into groups and the regularized estimation of the coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. Example applications can be found in Christidis et al. (2021) <doi:10.48550/arXiv.2102.08591>.

This package provides a Package for selecting variables for the joint modeling of mean and dispersion (including models for mixture experiments) based on hypothesis testing and the quality of model's fit. In each iteration of the selection process, a criterion for checking the goodness of fit is used as a filter for choosing the terms that will be evaluated by a hypothesis test. Pinto & Pereira (2021) <arXiv:2109.07978>.