Implementation of Johansen's general formulation of Welch-James's statistic with Approximate Degrees of Freedom, which makes it suitable for testing any linear hypothesis concerning cell means in univariate and multivariate mixed model designs when the data pose non-normality and non-homogeneous variance. Some improvements, namely trimmed means and Winsorized variances, and bootstrapping for calculating an empirical critical value, have been added to the classical formulation. The code departs from a previous SAS implementation by L.M. Lix and H.J. Keselman, available at <http://supp.apa.org/psycarticles/supplemental/met_13_2_110/SAS_Program.pdf> and published in Keselman, H.J., Wilcox, R.R., and Lix, L.M. (2003) <DOI:10.1111/1469-8986.00060>.

This package provides a hierarchy of classes and methods for manipulating matrices formed implicitly from the sums of the inverses of other matrices, a situation commonly encountered in spatial statistics and related fields. Enables easy use of the Woodbury matrix identity and the matrix determinant lemma to allow computation (e.g., solving linear systems) without having to form the actual matrix. More information on the underlying linear algebra can be found in Harville, D. A. (1997) <doi:10.1007/b98818>.

This package provides efficient implementations of weighted dependence measures and related asymptotic tests for independence. Implemented measures are the Pearson correlation, Spearman's rho, Kendall's tau, Blomqvist's beta, and Hoeffding's D; see, e.g., Nelsen (2006) <doi:10.1007/0-387-28678-0> and Hollander et al. (2015, ISBN:9780470387375).

Mixed effects modeling with warping for functional data using B- spline. Warping coefficients are considered as random effects, and warping functions are general functions, parameters representing the projection onto B- spline basis of a part of the warping functions. Warped data are modelled by a linear mixed effect functional model, the noise is Gaussian and independent from the warping functions.

Calculates the minimal sample size for the Wilcoxon-Mann-Whitney test that is needed for a given power and two sided type I error rate. The method works for metric data with and without ties, count data, ordered categorical data, and even dichotomous data. But data is needed for the reference group to generate synthetic data for the treatment group based on a relevant effect. See Happ et al. (2019, <doi:10.1002/sim.7983>) for details.

Create dense vector representation of words and documents using quanteda'. Currently implements Word2vec (Mikolov et al., 2013) <doi:10.48550/arXiv.1310.4546> and Latent Semantic Analysis (Deerwester et al., 1990) <doi:10.1002/(SICI)1097-4571(199009)41:6%3C391::AID-ASI1%3E3.0.CO;2-9>.

Calculate magnetic field at a given location and time according to the World Magnetic Model (WMM). Both the main field and secular variation components are returned. This functionality is useful for physicists and geophysicists who need orthogonal components from WMM. Currently, this package supports annualized time inputs between 2000 and 2025. If desired, users can specify which WMM version to use, e.g., the original WMM2015 release or the recent out-of-cycle WMM2015 release. Methods used to implement WMM, including the Gauss coefficients for each release, are described in the following publications: Chulliat et al (2020) <doi:10.25923/ytk1-yx35>, Chulliat et al (2019) <doi:10.25921/xhr3-0t19>, Chulliat et al (2015) <doi:10.7289/V5TB14V7>, Maus et al (2010) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/WMM2010_Report.pdf>, McLean et al (2004) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/TRWMM_2005.pdf>, and Macmillian et al (2000) <https://www.ngdc.noaa.gov/geomag/WMM/data/WMMReports/wmm2000.pdf>.

Robust and reliable functions to return informative outputs to console with the run or source location of a command. This can be from the RScript'/R terminal commands or RStudio console, source editor, Rmarkdown document and a Shiny application.

This package provides a parallel implementation of Weighted Subspace Random Forest. The Weighted Subspace Random Forest algorithm was proposed in the International Journal of Data Warehousing and Mining by Baoxun Xu, Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye (2012) <DOI:10.4018/jdwm.2012040103>. The algorithm can classify very high-dimensional data with random forests built using small subspaces. A novel variable weighting method is used for variable subspace selection in place of the traditional random variable sampling.This new approach is particularly useful in building models from high-dimensional data.

Create, store, read and manage structured collections of datasets and other objects using a workspace', then bundle it into a compressed archive. Using open and interoperable formats makes it possible to exchange bundled data from R to other languages such as Python or Julia'. Multiple formats are supported Parquet', JSON', yaml', spatial data and raster data are supported.

This package provides functions to assist in the processing and exploration of data from environmental monitoring programs. The package name stands for "water quality" and reflects the original focus on time series data for physical and chemical properties of water, as well as the biota. Intended for programs that sample approximately monthly, quarterly or annually at discrete stations, a feature of many legacy data sets. Most of the functions should be useful for analysis of similar-frequency time series regardless of the subject matter.

Access and analyze the World Bank's International Debt Statistics (IDS) <https://www.worldbank.org/en/programs/debt-statistics/ids>. IDS provides creditor-debtor relationships between countries, regions, and institutions. wbids enables users to download, process and work with IDS series across multiple geographies, counterparts, and time periods.

This package implements an automated binning of numeric variables and factors with respect to a dichotomous target variable. Two approaches are provided: An implementation of fine and coarse classing that merges granular classes and levels step by step. And a tree-like approach that iteratively segments the initial bins via binary splits. Both procedures merge, respectively split, bins based on similar weight of evidence (WOE) values and stop via an information value (IV) based criteria. The package can be used with single variables or an entire data frame. It provides flexible tools for exploring different binning solutions and for deploying them to (new) data.

This package contains functions for computing and plotting discrete wavelet transforms (DWT) and maximal overlap discrete wavelet transforms (MODWT), as well as their inverses. Additionally, it contains functionality for computing and plotting wavelet transform filters that are used in the above decompositions as well as multiresolution analyses.

This package provides a weighted selection probability to locate rare variants associated with multiple phenotypes.

This package provides survival analysis functions with support for time-dependent and subject-specific (e.g., propensity score) weighting. Implements weighted estimation for Cox models, Kaplan-Meier survival curves, and treatment differences with point-wise and simultaneous confidence bands. Includes restricted mean survival time (RMST) comparisons evaluated across all potential truncation times with both point-wise and simultaneous confidence bands. See Cole, S. R. & Hernán, M. A. (2004) <doi:10.1016/j.cmpb.2003.10.004> for methodological background.

This package provides scalogram based wavelet tools for time series analysis: wavelet power spectrum, scalogram, windowed scalogram, windowed scalogram difference (see Bolos et al. (2017) <doi:10.1016/j.amc.2017.05.046>), scale index and windowed scale index (Benitez et al. (2010) <doi:10.1016/j.camwa.2010.05.010>).

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

Access and analyze the World Bank's World Development Indicators (WDI) using the corresponding API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation>. WDI provides more than 24,000 country or region-level indicators for various contexts. wbwdi enables users to download, process and work with WDI series across multiple countries, aggregates, and time periods.

Search and download data from over 40 databases hosted by the World Bank, including the World Development Indicators ('WDI'), International Debt Statistics, Doing Business, Human Capital Index, and Sub-national Poverty indicators.

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.

Allow users to obtain clean and tidy football (soccer) game, team and player data. Data is collected from a number of popular sites, including FBref', transfer and valuations data from Transfermarkt'<https://www.transfermarkt.com/> and shooting location and other match stats data from Understat'<https://understat.com/> and fotmob'<https://www.fotmob.com/>. It gives users the ability to access data more efficiently, rather than having to export data tables to files before being able to complete their analysis.

MIME types are shorthand descriptors for file contents and can be determined from "magic" bytes in file headers, file contents or intuited from file extensions. Tools are provided to perform curated "magic" tests as well as mapping MIME types from a database of over 1,500 extension mappings.

Converts pathways from WikiPathways GPML format or KEGG KGML format into igraph objects. Includes tools to find all cycles in the resulting graphs and determine which ones involve negative feedback (inhibition).