This package provides a toolbox of common robust statistical tests, including robust descriptives, robust t-tests, and robust ANOVA. It is also available as a module for jamovi (see <https://www.jamovi.org> for more information). Walrus is based on the WRS2 package by Patrick Mair, which is in turn based on the scripts and work of Rand Wilcox. These analyses are described in depth in the book Introduction to Robust Estimation & Hypothesis Testing'.

This package provides a collection of color palettes that were extracted from various books on my sons(Wren) bookshelf. Also included are a number of functions and wrappers to utilize them, as well as to subset the palettes to desired number/specific colors.

Efficiently read and write Waveform (WAV) audio files <https://en.wikipedia.org/wiki/WAV>. Support for unsigned 8 bit Pulse-code modulation (PCM), signed 12, 16, 24 and 32 bit PCM and other encodings.

Application to estimate statistical values using properties provided by a group of individuals to describe concepts using shiny'. It estimates the underlying distribution to generate new descriptive words Canessa et al. (2023) <doi:10.3758/s13428-022-01811-w>, applies a new clustering model, and uses simulations to estimate the probability that two persons describe the same words based on their descriptions Canessa et al. (2022) <doi:10.3758/s13428-022-02030-z>.

Generate data frames from templates.

Non- and semiparametric regression for generalized additive, partial linear, and varying coefficient models as well as their combinations via smoothed backfitting. Based on Roca-Pardinas J and Sperlich S (2010) <doi:10.1007/s11222-009-9130-2>; Mammen E, Linton O and Nielsen J (1999) <doi:10.1214/aos/1017939138>; Lee YK, Mammen E, Park BU (2012) <doi:10.1214/12-AOS1026>.

This package performs a sensitivity analysis using weighted rank tests in observational studies with I blocks of size J; see Rosenbaum (2024) <doi:10.1080/01621459.2023.2221402>. The package can perform adaptive inference in block designs; see Rosenbaum (2012) <doi:10.1093/biomet/ass032>. The package can increase design sensitivity using the conditioning tactic in Rosenbaum (2025) <doi:10.1093/jrsssb/qkaf007>. The main functions are wgtRank(), wgtRankCI(), wgtRanktt() and wgtRankC().

Data from the United Nation's World Population Prospects 2012.

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

The continuous wavelet transform enables the observation of transient/non-stationary cyclicity in time-series. The goal of cyclostratigraphic studies is to define frequency/period in the depth/time domain. By conducting the continuous wavelet transform on cyclostratigraphic data series one can observe and extract cyclic signals/signatures from signals. These results can then be visualized and interpreted enabling one to identify/interpret cyclicity in the geological record, which can be used to construct astrochronological age-models and identify and interpret cyclicity in past and present climate systems. The WaverideR R package builds upon existing literature and existing codebase. The list of articles which are relevant can be grouped in four subjects; cyclostratigraphic data analysis,example data sets,the (continuous) wavelet transform and astronomical solutions. References for the cyclostratigraphic data analysis articles are: Stephen Meyers (2019) <doi:10.1016/j.earscirev.2018.11.015>. Mingsong Li, Linda Hinnov, Lee Kump (2019) <doi:10.1016/j.cageo.2019.02.011> Stephen Meyers (2012)<doi:10.1029/2012PA002307> Mingsong Li, Lee R. Kump, Linda A. Hinnov, Michael E. Mann (2018) <doi:10.1016/j.epsl.2018.08.041>. Wouters, S., Crucifix, M., Sinnesael, M., Da Silva, A.C., Zeeden, C., Zivanovic, M., Boulvain, F., Devleeschouwer, X. (2022) <doi:10.1016/j.earscirev.2021.103894>. Wouters, S., Da Silva, A.-C., Boulvain, F., and Devleeschouwer, X. (2021) <doi:10.32614/RJ-2021-039>. Huang, Norden E., Zhaohua Wu, Steven R. Long, Kenneth C. Arnold, Xianyao Chen, and Karin Blank (2009) <doi:10.1142/S1793536909000096>. Cleveland, W. S. (1979)<doi:10.1080/01621459.1979.10481038> Hurvich, C.M., Simonoff, J.S., and Tsai, C.L. (1998) <doi:10.1111/1467-9868.00125>, Golub, G., Heath, M. and Wahba, G. (1979) <doi:10.2307/1268518>. References for the example data articles are: Damien Pas, Linda Hinnov, James E. (Jed) Day, Kenneth Kodama, Matthias Sinnesael, Wei Liu (2018) <doi:10.1016/j.epsl.2018.02.010>. Steinhilber, Friedhelm, Abreu, Jacksiel, Beer, Juerg , Brunner, Irene, Christl, Marcus, Fischer, Hubertus, HeikkilA, U., Kubik, Peter, Mann, Mathias, Mccracken, K. , Miller, Heinrich, Miyahara, Hiroko, Oerter, Hans , Wilhelms, Frank. (2012 <doi:10.1073/pnas.1118965109>. Christian Zeeden, Frederik Hilgen, Thomas Westerhold, Lucas Lourens, Ursula Röhl, Torsten Bickert (2013) <doi:10.1016/j.palaeo.2012.11.009>. References for the (continuous) wavelet transform articles are: Morlet, Jean, Georges Arens, Eliane Fourgeau, and Dominique Glard (1982a) <doi:10.1190/1.1441328>. J. Morlet, G. Arens, E. Fourgeau, D. Giard (1982b) <doi:10.1190/1.1441329>. Torrence, C., and G. P. Compo (1998)<https://paos.colorado.edu/research/wavelets/bams_79_01_0061.pdf>, Gouhier TC, Grinsted A, Simko V (2021) <https://github.com/tgouhier/biwavelet>. Angi Roesch and Harald Schmidbauer (2018) <https://CRAN.R-project.org/package=WaveletComp>. Russell, Brian, and Jiajun Han (2016)<https://www.crewes.org/Documents/ResearchReports/2016/CRR201668.pdf>. Gabor, Dennis (1946) <http://genesis.eecg.toronto.edu/gabor1946.pdf>. J. Laskar, P. Robutel, F. Joutel, M. Gastineau, A.C.M. Correia, and B. Levrard, B. (2004) <doi:10.1051/0004-6361:20041335>. Laskar, J., Fienga, A., Gastineau, M., Manche, H. (2011a) <doi:10.1051/0004-6361/201116836>. References for the astronomical solutions articles are: Laskar, J., Gastineau, M., Delisle, J.-B., Farres, A., Fienga, A. (2011b <doi:10.1051/0004-6361/201117504>. J. Laskar (2019) <doi:10.1016/B978-0-12-824360-2.00004-8>. Zeebe, Richard E (2017) <doi:10.3847/1538-3881/aa8cce>. Zeebe, R. E. and Lourens, L. J. (2019) <doi:10.1016/j.epsl.2022.117595>. Richard E. Zeebe Lucas J. Lourens (2022) <doi:10.1126/science.aax0612>.

This package provides a weather generator to simulate precipitation and temperature for regions with seasonality. Users input training data containing precipitation, temperature, and seasonality (up to 26 seasons). Including weather season as a training variable allows users to explore the effects of potential changes in season duration as well as average start- and end-time dates due to phenomena like climate change. Data for training should be a single time series but can originate from station data, basin averages, grid cells, etc. Bearup, L., Gangopadhyay, S., & Mikkelson, K. (2021). "Hydroclimate Analysis Lower Santa Cruz River Basin Study (Technical Memorandum No ENV-2020-056)." Bureau of Reclamation. Gangopadhyay, S., Bearup, L. A., Verdin, A., Pruitt, T., Halper, E., & Shamir, E. (2019, December 1). "A collaborative stochastic weather generator for climate impacts assessment in the Lower Santa Cruz River Basin, Arizona." Fall Meeting 2019, American Geophysical Union. <https://ui.adsabs.harvard.edu/abs/2019AGUFMGC41G1267G>.

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.

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>.

R clients to the Web of Science and InCites <https://clarivate.com/products/data-integration/> APIs, which allow you to programmatically download publication and citation data indexed in the Web of Science and InCites databases.

This package provides a flexible method for modeling cumulative effects of time-varying exposures, weighted according to their relative proximity in time, and represented by time-dependent covariates. The current implementation estimates the weight function in the Cox proportional hazards model. The function that assigns weights to doses taken in the past is estimated using cubic regression splines.

This package provides data from the United Nation's World Population Prospects 2019.

Process GPS and accelerometry data to generate walk bouts. A walk bout is a period of activity with accelerometer movement matching the patterns of walking with corresponding GPS measurements that confirm travel. The inputs of the walkboutr package are individual-level accelerometry and GPS data. The outputs of the model are walk bouts with corresponding times, duration, and summary statistics on the sample population, which collapse all personally identifying information. These bouts can be used to measure walking both as an outcome of a change to the built environment or as a predictor of health outcomes such as a cardioprotective behavior. Kang B, Moudon AV, Hurvitz PM, Saelens BE (2017) <doi:10.1016/j.trd.2017.09.026>.

Formal implementation of White test of heteroskedasticity and a bootstrapped version of it, developed under the methodology of Jeong, J., Lee, K. (1999) <https://yonsei.pure.elsevier.com/en/publications/bootstrapped-whites-test-for-heteroskedasticity-in-regression-mod>.

This package provides two functions frameableWidget()', and frameWidget()'. The frameableWidget() is used to add extra code to a htmlwidget which allows is to be rendered correctly inside a responsive iframe'. The frameWidget() is a htmlwidget which displays content of another htmlwidget inside a responsive iframe'. These functions allow for easier embedding of htmlwidgets in content management systems such as wordpress', blogger etc. They also allow for separation of widget content from main HTML content where CSS of the main HTML could interfere with the widget.

This package provides tools for fitting and simulating mixtures of Watson distributions. The package is described in Sablica, Hornik and Leydold (2026) <doi:10.18637/jss.v115.i04>. The random sampling scheme of the package offers two sampling algorithms that are based of the results of Sablica, Hornik and Leydold (2022) <doi:10.1080/10618600.2024.2416521>. What is more, the package offers a smart tool to combine these two methods, and based on the selected parameters, it approximates the relative sampling speed for both methods and picks the faster one. In addition, the package offers a fitting function for the mixtures of Watson distribution, that uses the expectation-maximization (EM) algorithm. Special features are the possibility to use multiple variants of the E-step and M-step, sparse matrices for the data representation and state of the art methods for numerical evaluation of needed special functions using the results of Sablica and Hornik (2022) <doi:10.1090/mcom/3690> and Sablica and Hornik (2024) <doi:10.1016/j.jmaa.2024.128262>.

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.

Data from the United Nation's World Population Prospects 2010.

Fetch and clean data from the World Database on Protected Areas (WDPA) and the World Database on Other Effective Area-Based Conservation Measures (WDOECM). Data is obtained from Protected Planet <https://www.protectedplanet.net/en>. To augment data cleaning procedures, users can install the prepr R package (available at <https://github.com/prioritizr/prepr>). For more information on this package, see Hanson (2022) <doi:10.21105/joss.04594>.

This package provides functions for subject/instance weighted support vector machines (SVM). It uses a modified version of libsvm and is compatible with package e1071'. It also allows user defined kernel matrix.