This package performs Wilcoxon-Mann-Whitney test in the presence of missing data with controlled Type I error regardless of the values of missing data.

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.

Displays geospatial data on an interactive 3D globe in the web browser.

Download daily weather data recorded at airport weather stations using the National Centers for Environmental Information (NCEI) API <https://www.ncei.noaa.gov/support/access-search-service-api-user-documentation>.

An R frontend for the WhiteboxTools library, which is an advanced geospatial data analysis platform developed by Prof. John Lindsay at the University of Guelph's Geomorphometry and Hydrogeomatics Research Group. WhiteboxTools can be used to perform common geographical information systems (GIS) analysis operations, such as cost-distance analysis, distance buffering, and raster reclassification. Remote sensing and image processing tasks include image enhancement (e.g. panchromatic sharpening, contrast adjustments), image mosaicing, numerous filtering operations, simple classification (k-means), and common image transformations. WhiteboxTools also contains advanced tooling for spatial hydrological analysis (e.g. flow-accumulation, watershed delineation, stream network analysis, sink removal), terrain analysis (e.g. common terrain indices such as slope, curvatures, wetness index, hillshading; hypsometric analysis; multi-scale topographic position analysis), and LiDAR data processing. Suggested citation: Lindsay (2016) <doi:10.1016/j.cageo.2016.07.003>.

This package provides tools to download data from the online World Inequality Database directly into R. The World Inequality Database is an extensive source on the historical evolution of the distribution of income and wealth both within and between countries. It relies on the combined effort of an international network of over a hundred researchers covering more than seventy countries from all continents.

This package provides a collection of implementations of classical and novel algorithms for weighted sampling without replacement.

This package creates interactive web maps using the JavaScript Leaflet library with base layers of The National Map ('TNM'). TNM services provide access to base geospatial information that describes the landscape of the United States and its territories. This package is dependent on, and intended to be used with, the leaflet package.

This package provides methods for estimating profit, profit-maximizing price, demand and consumer surplus of Word-of-Mouth-campaigns on mean-field networks.

This package implements a functional approximation of the four panel cointegration tests developed by Westerlund (2007) <doi:10.1111/j.1468-0084.2007.00477.x>. The tests are based on structural rather than residual dynamics and allow for heterogeneity in both the long-run cointegrating relationship and the short-run dynamics. The package includes logic for automated lag and lead selection via AIC/BIC, Bartlett kernel long-run variance estimation, and a bootstrap procedure to handle cross-sectional dependence. It also includes a bootstrapping distribution visualization function for diagnostic purposes.

This package provides efficient implementation of the Wild Binary Segmentation and Binary Segmentation algorithms for estimation of the number and locations of multiple change-points in the piecewise constant function plus Gaussian noise model.

The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions. Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc. Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM) for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates). A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc. Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values. Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples. Many times large experimental datasets need some additional filtering, adequate functions are provided. Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of limma is provided, too. Batch reading (or writing) of sets of files and combining data to arrays is supported, too.

Students learning both econometrics and R may find the introduction to both challenging. The wooldridge data package aims to lighten the task by efficiently loading any data set found in the text with a single command. Data sets have been compressed to a fraction of their original size. Documentation files contain page numbers, the original source, time of publication, and notes from the author suggesting avenues for further analysis and research. If one needs an introduction to R model syntax, a vignette contains solutions to examples from chapters of the text. Data sets are from the 7th edition (Wooldridge 2020, ISBN-13 978-1-337-55886-0), and are backwards compatible with all previous versions of the text.

Book is "Linear Mixed Models: A Practical Guide Using Statistical Software" published in 2006 by Chapman Hall / CRC Press.

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.

Computationally easy modeling, interpolation, forecasting of massive temporal-spacial data.

This package provides tools for a wavelet-based approach to analyzing spatial synchrony, principally in ecological data. Some tools will be useful for studying community synchrony. See, for instance, Sheppard et al (2016) <doi: 10.1038/NCLIMATE2991>, Sheppard et al (2017) <doi: 10.1051/epjnbp/2017000>, Sheppard et al (2019) <doi: 10.1371/journal.pcbi.1006744>.

Imports variables from ReaderBench (Dascalu et al., 2018)<doi:10.1007/978-3-319-66610-5_48>, Coh-Metrix (McNamara et al., 2014)<doi:10.1017/CBO9780511894664>, and/or GAMET (Crossley et al., 2019) <doi:10.17239/jowr-2019.11.02.01> output files; downloads predictive scoring models described in Mercer & Cannon (2022)<doi:10.31244/jero.2022.01.03> and Mercer et al.(2021)<doi:10.1177/0829573520987753>; and generates predicted writing quality and curriculum-based measurement (McMaster & Espin, 2007)<doi:10.1177/00224669070410020301> scores.

Obtain information on peak flow data from the National River Flow Archive (NRFA) in the United Kingdom, either from the Peak Flow Dataset files <https://nrfa.ceh.ac.uk/data/peak-flow-dataset> once these have been downloaded to the user's computer or using the NRFA's API. These files are in a format suitable for direct use in the WINFAP software, hence the name of the package.

This package provides data from the United Nation's World Population Prospects 2015.

In the course of a genome-wide association study, the situation often arises that some phenotypes are known with greater precision than others. It could be that some individuals are known to harbor more micro-environmental variance than others. In the case of inbred strains of model organisms, it could be the case that more organisms were observed from some strains than others, so the strains with more organisms have better-estimated means. Package wISAM handles this situation by allowing for weighting of each observation according to residual variance. Specifically, the weight parameter to the function conduct_scan() takes the precision of each observation (one over the variance).

Lets you temporarily execute an expression or a local block with a different here() root in the here package. This is useful for sourcing code in other projects which expect the root directory of here() to be the project directory of those projects. This may be the case with git submodules for example.

Shows the relationship between an independent and dependent variable through Weight of Evidence and Information Value.

The weighted scores method and composite likelihood information criteria as an intermediate step for variable/correlation selection for longitudinal ordinal and count data in Nikoloulopoulos, Joe and Chaganty (2011) <doi:10.1093/biostatistics/kxr005>, Nikoloulopoulos (2016) <doi:10.1002/sim.6871> and Nikoloulopoulos (2017) <arXiv:1510.07376>.