Download and plot education specific demographic data from the Wittgenstein Centre for Demography and Human Capital Data Explorer <https://dataexplorer.wittgensteincentre.org/>.

Search and download data from the World Bank Data API. Includes support for mutliple languages, access to annual, quarterly, and monthly data.

Heuristic methods to solve the routing problems in a warehouse management. Package includes several heuristics such as the Midpoint, Return, S-Shape and Semi-Optimal Heuristics for designation of the pickerâ s route in order picking. The heuristics aim to provide the acceptable travel distances while considering warehouse layout constraints such as aisles and shelves. It also includes implementation of the COPRAS (COmplex PRoportional ASsessment) method for supporting selection of locations to be visited by the picker in shared storage systems. The package is designed to facilitate more efficient warehouse routing and logistics operations. see: Bartholdi, J. J., Hackman, S. T. (2019). "WAREHOUSE & DISTRIBUTION SCIENCE. Release 0.98.1." The Supply Chain & Logistics Institute. H. Milton Stewart School of Industrial and Systems Engineering. Georgia Institute of Technology. <https://www.warehouse-science.com/book/editions/wh-sci-0.98.1.pdf>.

This package provides a prediction model is calibrated if, roughly, for any percentage x we can expect that x subjects out of 100 experience the event among all subjects that have a predicted risk of x%. A calibration plot provides a simple, yet useful, way of assessing the calibration assumption. The Wally plot consists of a sequence of usual calibration plots. Among the plots contained within the sequence, one is the actual calibration plot which has been obtained from the data and the others are obtained from similar simulated data under the calibration assumption. It provides the investigator with a direct visual understanding of the shape and sampling variability that are common under the calibration assumption. The original calibration plot from the data is included randomly among the simulated calibration plots, similarly to a police lineup. If the original calibration plot is not easily identified then the calibration assumption is not contradicted by the data. The method handles the common situations in which the data contain censored observations and occurrences of competing events.

This package provides a set of tools for processing and analyzing data developed in the context of the "Who Has Eaten the Planet" (WHEP) project, funded by the European Research Council (ERC). For more details on multi-regional inputâ output model "Food and Agriculture Biomass Inputâ Output" (FABIO) see Bruckner et al. (2019) <doi:10.1021/acs.est.9b03554>.

World Flora Online is an online flora of all known plants, available from <https://www.worldfloraonline.org/>. Methods are provided of matching a list of plant names (scientific names, taxonomic names, botanical names) against a static copy of the World Flora Online Taxonomic Backbone data that can be downloaded from the World Flora Online website. The World Flora Online Taxonomic Backbone is an updated version of The Plant List (<http://www.theplantlist.org/>), a working list of plant names that has become static since 2013.

Estimates high-dimensional multivariate normal copula regression models with the weighted composite likelihood estimating equations in Nikoloulopoulos (2023) <doi:10.1016/j.csda.2022.107654>. It provides autoregressive moving average correlation structures and binary, ordinal, Poisson, and negative binomial regressions.

Data from the United Nation's World Population Prospects 2008.

Client for World Register of Marine Species (<https://www.marinespecies.org/>). Includes functions for each of the API methods, including searching for names by name, date and common names, searching using external identifiers, fetching synonyms, as well as fetching taxonomic children and taxonomic classification.

This package provides functions aiming to facilitate the analysis of the structure of animal acoustic signals in R'. warbleR makes use of the basic sound analysis tools from the packages tuneR and seewave', and offers new tools for exploring and quantifying acoustic signal structure. The package allows to organize and manipulate multiple sound files, create spectrograms of complete recordings or individual signals in different formats, run several measures of acoustic structure, and characterize different structural levels in acoustic signals (Araya-Salas et al 2016 <doi:10.1111/2041-210X.12624>).

Predicts individual race/ethnicity using surname, first name, middle name, geolocation, and other attributes, such as gender and age. The method utilizes Bayes Rule (with optional measurement error correction) to compute the posterior probability of each racial category for any given individual. The package implements methods described in Imai and Khanna (2016) "Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration Records" Political Analysis <DOI:10.1093/pan/mpw001> and Imai, Olivella, and Rosenman (2022) "Addressing census data problems in race imputation via fully Bayesian Improved Surname Geocoding and name supplements" <DOI:10.1126/sciadv.adc9824>. The package also incorporates the data described in Rosenman, Olivella, and Imai (2023) "Race and ethnicity data for first, middle, and surnames" <DOI:10.1038/s41597-023-02202-2>.

This package provides a convenient data set, a set of helper functions, and a benchmark function for economically (profit) driven wind farm layout optimization. This enables researchers in the field of the NP-hard (non-deterministic polynomial-time hard) problem of wind farm layout optimization to focus on their optimization methodology contribution and also provides a realistic benchmark setting for comparability among contributions. See Croonenbroeck, Carsten & Hennecke, David (2020) <doi:10.1016/j.energy.2020.119244>.

Import WIG data into R in long format.

Conducts a goodness-of-fit test for the Weibull distribution (referred to as the weibullness test) and furnishes parameter estimations for both the two-parameter and three-parameter Weibull distributions. Notably, the threshold parameter is derived through correlation from the Weibull plot. Additionally, this package conducts goodness-of-fit assessments for the exponential, Gumbel, and inverse Weibull distributions, accompanied by parameter estimations. For more details, see Park (2017) <doi:10.23055/ijietap.2017.24.4.2848>, Park (2018) <doi:10.1155/2018/6056975>, and Park (2023) <doi:10.3390/math11143156>. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (No. 2022R1A2C1091319).

This package provides a user-friendly factor-like interface for converting strings of text into numeric vectors and rectangular data structures.

This package implements the Whale Optimization Algorithm(WOA) for k-medoids clustering, providing tools for effective and efficient cluster analysis in various data sets. The methodology is based on "The Whale Optimization Algorithm" by Mirjalili and Lewis (2016) <doi:10.1016/j.advengsoft.2016.01.008>.

Treemaps are a visually appealing graphical representation of numerical data using a space-filling approach. A plane or map is subdivided into smaller areas called cells. The cells in the map are scaled according to an underlying metric which allows to grasp the hierarchical organization and relative importance of many objects at once. This package contains two different implementations of treemaps, Voronoi treemaps and Sunburst treemaps. The Voronoi treemap function subdivides the plot area in polygonal cells according to the highest hierarchical level, then continues to subdivide those parental cells on the next lower hierarchical level, and so on. The Sunburst treemap is a computationally less demanding treemap that does not require iterative refinement, but simply generates circle sectors that are sized according to predefined weights. The Voronoi tesselation is based on functions from Paul Murrell (2012) <https://www.stat.auckland.ac.nz/~paul/Reports/VoronoiTreemap/voronoiTreeMap.html>.

Graphical data analysis of accelerated life tests. Methods derived from Wayne Nelson (1990, ISBN: 9780471522775), William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6).

Makes research involving EMDAT and related datasets easier. These Datasets are manually filled and have several formatting and compatibility issues. Weed aims to resolve these with its functions.

An implementation of the 1-Sample Wilcoxon Sign rank test for medians. It includes 2 functions, W_stat(), which computes the exact probabilities of the Wilcoxon Sign Rank Test Statistic, W. The second function, Wilcox.m.test() allows the user to conduct the 1-Sample Wilcoxon Sign Rank hypothesis test for medians, this also allows the user to conduct the hypothesis test for the normal approximation, based on the techniques of Bickel and Doksum (1973, ISBN:013850363X).

This package provides tools for generating simulated sawn timber strength grading data with a main focus on statistical simulation based on covariance matrices. Simulation data for Norway spruce sawn timber from Austria and reference values of means and standard deviations of grade determining properties from literature for a number of European countries are provided, as well.

This package provides a WebSocket client interface for R. WebSocket is a protocol for low-overhead real-time communication: <https://en.wikipedia.org/wiki/WebSocket>.

An easy-to-use interface for interacting with WebDAV servers, including OwnCloud'. It simplifies the use of WebDAV methods such as COPY, MOVE, DELETE and others. With built-in authentication and request handling, it allows for easy management of files and directories over the WebDAV protocol.

Computes exact observation weights for the Kalman filter and smoother, following Koopman and Harvey (2003) <www.sciencedirect.com/science/article/pii/S0165188902000611>. The package provides tools for analyzing linear Gaussian state-space models, allowing users to quantify the contribution of individual observations to filtered and smoothed state estimates. These weights can be used for interpretation, decomposition, and diagnostic analysis in time series models, including applications such as dynamic factor models. See the README for examples.