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>.

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 provides methods for retrieving United States Geological Survey (USGS) water data using sequential and parallel processing (Bengtsson, 2022 <doi:10.32614/RJ-2021-048>). In addition to parallel methods, data wrangling and additional statistical attributes are provided.

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.

Additional options for making graphics in the context of analyzing high-throughput data are available here. This includes automatic segmenting of the current device (eg window) to accommodate multiple new plots, automatic checking for optimal location of legends in plots, small histograms to insert as legends, histograms re-transforming axis labels to linear when plotting log2-transformed data, a violin-plot <doi:10.1080/00031305.1998.10480559> function for a wide variety of input-formats, principal components analysis (PCA) <doi:10.1080/14786440109462720> with bag-plots <doi:10.1080/00031305.1999.10474494> to highlight and compare the center areas for groups of samples, generic MA-plots (differential- versus average-value plots) <doi:10.1093/nar/30.4.e15>, staggered count plots and generation of mouse-over interactive html pages.

This package provides a set of functions to make tracking the hidden movements of the Jack player easier. By tracking every possible path Jack might have traveled from the point of the initial murder including special movement such as through alleyways and via carriages, the police can more accurately narrow the field of their search. Additionally, by tracking all possible hideouts from round to round, rounds 3 and 4 should have a vastly reduced field of search.

This package provides a comprehensive suite of functions for processing, analyzing, and visualizing textual data from tweets is offered. Users can clean tweets, analyze their sentiments, visualize data, and examine the correlation between sentiments and environmental data such as weather conditions. Main features include text processing, sentiment analysis, data visualization, correlation analysis, and synthetic data generation. Text processing involves cleaning and preparing tweets by removing textual noise and irrelevant words. Sentiment analysis extracts and accurately analyzes sentiments from tweet texts using advanced algorithms. Data visualization creates various charts like word clouds and sentiment polarity graphs for visual representation of data. Correlation analysis examines and calculates the correlation between tweet sentiments and environmental variables such as weather conditions. Additionally, random tweets can be generated for testing and evaluating the performance of analyses, empowering users to effectively analyze and interpret Twitter data for research and commercial purposes.

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>.

Generate data frames from templates.

Wavelet routines that calculate single sets of wavelet multiple regressions and correlations, and cross-regressions and cross-correlations from a multivariate time series. Dynamic versions of the routines allow the wavelet local multiple (cross-)regressions and (cross-)correlations to evolve over time.

This package provides function, wget_set(), to change the method (default to wget -c') using in download.file(). Using wget -c allowing continued downloading, which is especially useful for slow internet connection and for downloading large files. User can run wget_unset() to restore previous setting.

This package provides maximum likelihood estimation methods for eight modified Weibull-type distributions. It returns parameter estimates, log-likelihood, AIC, and BIC, and also supports model fitting, validation, and comparison across different distributional forms. These methods can be applied to reliability, survival, and lifetime data analysis, making the package useful for researchers and practitioners in statistics, engineering, and medicine. The following distributions are included: Rangoli2023, Peng2014, Lai2003, Xie1996, Sarhan2009, Rangoli2025, Mustafa2012, and Alwasel2009.

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>.

Create reproducible and transparent research projects in R'. This package is based on the Workflow for Open Reproducible Code in Science (WORCS), a step-by-step procedure based on best practices for Open Science. It includes an RStudio project template, several convenience functions, and all dependencies required to make your project reproducible and transparent. WORCS is explained in the tutorial paper by Van Lissa, Brandmaier, Brinkman, Lamprecht, Struiksma, & Vreede (2021). <doi:10.3233/DS-210031>.

Taxonomic information from Wikipedia', Wikicommons', Wikispecies', and Wikidata'. Functions included for getting taxonomic information from each of the sources just listed, as well performing taxonomic search.

This package provides a collection of implementations of classical and novel algorithms for weighted sampling without replacement. Implementations include the algorithm of Efraimidis and Spirakis (2006) <doi:10.1016/j.ipl.2005.11.003> and Wong and Easton (1980) <doi:10.1137/0209009>.

Simulates the results of completed randomized controlled trials, as if they had been conducted as adaptive Multi-Arm Bandit (MAB) trials instead. Augmented inverse probability weighted estimation (AIPW), outlined by Hadad et al. (2021) <doi:10.1073/pnas.2014602118>, is used to robustly estimate the probability of success for each treatment arm under the adaptive design. Provides customization options to simulate perfect/imperfect information, stationary/non-stationary bandits, blocked treatment assignments, along with control augmentation, and other hybrid strategies for assigning treatment arms. The methods used in simulation were inspired by Offer-Westort et al. (2021) <doi:10.1111/ajps.12597>.

Entropy weighted k-means (ewkm) by Liping Jing, Michael K. Ng and Joshua Zhexue Huang (2007) <doi:10.1109/TKDE.2007.1048> is a weighted subspace clustering algorithm that is well suited to very high dimensional data. Weights are calculated as the importance of a variable with regard to cluster membership. The two-level variable weighting clustering algorithm tw-k-means (twkm) by Xiaojun Chen, Xiaofei Xu, Joshua Zhexue Huang and Yunming Ye (2013) <doi:10.1109/TKDE.2011.262> introduces two types of weights, the weights on individual variables and the weights on variable groups, and they are calculated during the clustering process. The feature group weighted k-means (fgkm) by Xiaojun Chen, Yunminng Ye, Xiaofei Xu and Joshua Zhexue Huang (2012) <doi:10.1016/j.patcog.2011.06.004> extends this concept by grouping features and weighting the group in addition to weighting individual features.

Weighted Piecewise Kernel Density Estimation for large data.

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.

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.

Estimate and plot wavelet quantile correlations(Kumar and Padakandla,2022) between two time series. Wavelet quantile correlation is used to capture the dependency between two time series across quantiles and different frequencies. This method is useful in identifying potential hedges and safe-haven instruments for investment purposes. See Kumar and Padakandla(2022) <doi:10.1016/j.frl.2022.102707> for further details.

Establishes and maintains vertex pool topology for geometry handled by wk'. Segments are the atomic unit, vertices are shared via integer references into a pool. Topology is made discoverable via coincident vertex detection while not requiring modification of the input data. Topological data models follow principles described in Worboys and Duckham (2004, ISBN:978-0415283755). The edge-based topology geometry decomposed into vertices and directed edge pairs is a simplification of the quad-edge case in Guibas & Stolfi (1985) <doi:10.1145/282918.282923>.

Estimates the standard and weighted Elo (WElo, Angelini et al., 2022 <doi:10.1016/j.ejor.2021.04.011>) rates. The current version provides Elo and WElo rates for tennis, according to different systems of weights (games or sets) and scale factors (constant, proportional to the number of matches, with more weight on Grand Slam matches or matches played on a specific surface). Moreover, the package gives the possibility of estimating the (bootstrap) standard errors for the rates. Finally, the package includes betting functions that automatically select the matches on which place a bet.