This package provides tools to derive species-level phylogenies from large synthesis mega-trees for a wide range of taxonomic groups, including plants, birds, mammals, amphibians, reptiles, fish, bees, butterflies, and sharks. When a queried species is absent from the mega-tree, it is grafted onto the tree using one of two placement strategies: attachment at the basal node of the most closely related genus or family ('at_basal_node'), or random attachment below that basal node with probability proportional to branch length ('random_below_basal'). See Li (2023) <doi:10.1111/ecog.06643> for details. Multiple species from a genus not represented in the mega-tree are placed as a polytomy to preserve clade coherence. The package interfaces with the megatrees data package, which bundles or downloads on demand curated mega-trees. Users can also provide their own mega-trees.

Description of the tables, both grouped and not grouped, with some associated data management actions, such as sorting the terms of the variables and deleting terms with zero numbers.

Converts data to STL (stereolithography) files that can be used to feed a 3-dimensional printer. The 3-dimensional output from a function can be materialized into a solid surface in a plastic material, therefore allowing more detailed examination. There are many possible uses for this new tool, such as to examine mathematical expressions with very irregular shapes, to aid teaching people with impaired vision, to create raised relief maps from digital elevation maps (DEMs), to bridge the gap between mathematical tools and rapid prototyping, and many more. Ian Walker created the function r2stl() and Jose Gama assembled the package.

Computation of one-, two- and three-dimensional pseudo-observations based on recurrent events and terminal events. Generalised linear models are fitted using generalised estimating equations. Technical details on the bivariate procedure can be found in "Bivariate pseudo-observations for recurrent event analysis with terminal events" (Furberg et al., 2021) <doi:10.1007/s10985-021-09533-5>.

Reads river network shape files and computes network distances. Also included are a variety of computation and graphical tools designed for fisheries telemetry research, such as minimum home range, kernel density estimation, and clustering analysis using empirical k-functions with a bootstrap envelope. Tools are also provided for editing the river networks, meaning there is no reliance on external software.

This package provides a model agnostic tool for white-box model trained on features extracted from a black-box model. For more information see: Gosiewska et al. (2020) <doi:10.1016/j.dss.2021.113556>.

We introduce a robust matrix factor model that explicitly incorporates tail behavior and employs a mean-shift term to avoid efficiency losses through pre-centering of observed matrices. More details on the methods related to our paper are currently under submission. A full reference to the paper will be provided in future versions once the paper is published.

Helper functions to accompany the Blair, Coppock, and Humphreys (2022) "Research Design in the Social Sciences: Declaration, Diagnosis, and Redesign" <https://book.declaredesign.org>. rdss includes datasets, helper functions, and plotting components to enable use and replication of the book.

This package performs both classical and robust panel clustering by applying Principal Component Analysis (PCA) for dimensionality reduction and clustering via standard K-Means or Trimmed K-Means. The method is designed to ensure stable and reliable clustering, even in the presence of outliers. Suitable for analyzing panel data in domains such as economic research, financial time-series, healthcare analytics, and social sciences. The package allows users to choose between classical K-Means for standard clustering and Trimmed K-Means for robust clustering, making it a flexible tool for various applications. For this package, we have benefited from the studies Rencher (2003), Wang and Lu (2021) <DOI:10.25236/AJBM.2021.031018>, Cuesta-Albertos et al. (1997) <https://www.jstor.org/stable/2242558?seq=1>.

This package provides a collection of functions to compute the Rao-Stirling diversity index (Porter and Rafols, 2009) <DOI:10.1007/s11192-008-2197-2> and its extension to acknowledge missing data (i.e., uncategorized references) by calculating its interval of uncertainty using mathematical optimization as proposed in Calatrava et al. (2016) <DOI:10.1007/s11192-016-1842-4>. The Rao-Stirling diversity index is a well-established bibliometric indicator to measure the interdisciplinarity of scientific publications. Apart from the obligatory dataset of publications with their respective references and a taxonomy of disciplines that categorizes references as well as a measure of similarity between the disciplines, the Rao-Stirling diversity index requires a complete categorization of all references of a publication into disciplines. Thus, it fails for a incomplete categorization; in this case, the robust extension has to be used, which encodes the uncertainty caused by missing bibliographic data as an uncertainty interval. Classification / ACM - 2012: Information systems ~ Similarity measures, Theory of computation ~ Quadratic programming, Applied computing ~ Digital libraries and archives.

This package provides color schemes for maps and other graphics designed by CARTO as described at <https://carto.com/carto-colors/>. It includes four types of palettes: aggregation, diverging, qualitative, and quantitative.

Data for the examples and exercises in the book "R by Example". Jim Albert and Maria Rizzo (2012, ISBN 978-1-4614-1365-3).

JDemetra+ (<https://github.com/jdemetra/jdemetra-app>) is the seasonal adjustment software officially recommended to the members of the European Statistical System and the European System of Central Banks. Seasonal adjustment models performed with JDemetra+ can be stored into workspaces. JWSACruncher (<https://github.com/jdemetra/jwsacruncher/releases> for v2 and <https://github.com/jdemetra/jdplus-main/releases> for v3) is a console tool that re-estimates all the multi-processing defined in a workspace and to export the result. rjwsacruncher allows to launch easily the JWSACruncher'.

Wraps dozens of REDCap API endpoints into a standardized R6 object. Research Electronic Data Capture ('REDCap') is a survey and database web application software maintained by Vanderbilt University. It has a robust application programming interface (API) utilized by several R packages. REDCapSync uses redcapAPI and REDCapR behind-the-scenes to retrieve all metadata, data, and log details for a project. To minimize unnecessary server calls, the interim REDCap log is analyzed and used to only update necessary records. Furthermore, the user can define custom datasets that save to a directory. Those datasets continue to refresh when projects are synced. Having a secure, standardized, API-efficient, project-agnostic R object for REDCap projects, streamlines downstream use in scripts, functions, and shiny applications.

We utilize approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data. The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.

Administrative regions and other spatial objects of the Czech Republic.

This package provides a comprehensive tool for setting up seasonal data pipelines using JDemetra+ (version 3) and rjdverse'. This includes setting up a new working environment, creating and selecting calendar regressors, managing specifications (trading-days regressors and outliers) at the workspace level, making a workspace usable by the cruncher', removing insignificant outliers, and comparing workspaces.

Goldwin-Pierre correlogram. Research of critical periods in the past. Integrates a time series in a given window.

Systematically transform immunoassay data, evaluate if the data is normally distributed, and pick the right method for cut point determination based on that evaluation. This package can also produce plots that are needed for reports, so data analysis and visualization can be done easily.

This package performs model-free reinforcement learning in R. This implementation enables the learning of an optimal policy based on sample sequences consisting of states, actions and rewards. In addition, it supplies multiple predefined reinforcement learning algorithms, such as experience replay. Methodological details can be found in Sutton and Barto (1998) <ISBN:0262039249>.

This package provides an interface to access data from the International Union for Conservation of Nature (IUCN) Red List <https://api.iucnredlist.org/api-docs/index.html>. It allows users to retrieve up-to-date information on species conservation status, supporting biodiversity research and conservation efforts.

An interface to the Open Tree of Life API to retrieve phylogenetic trees, information about studies used to assemble the synthetic tree, and utilities to match taxonomic names to Open Tree identifiers'. The Open Tree of Life aims at assembling a comprehensive phylogenetic tree for all named species.

Calculates robust Matthews Correlation Coefficient (MCC) and robust F-Beta Scores, as introduced by Holzmann and Klar (2024) <doi:10.48550/arXiv.2404.07661>. These performance metrics are designed for imbalanced classification problems. Plots the receiver operating characteristic curve (ROC curve) together with the recall / 1-precision curve.

Rapidly estimates tree-topology from large allele frequency data using Root Distances Method, under a Brownian Motion Model. See Peng et al. (2021) <doi:10.1016/j.ympev.2021.107142>.