Apply Wordpiece (<arXiv:1609.08144>) tokenization to input text, given an appropriate vocabulary. The BERT (<arXiv:1810.04805>) tokenization conventions are used by default.

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.

It generates the roster of turn for an outlet which is flowing (water) 24X7 or 168 hours towards the area under command or agricutural area (to be irrigated). The area under command is differentially owned by different individual farmers. The Outlet runs for free of cost to irrigate the area under command 24X7. So, flow time of the outlet has to be divided based on an area owned by an individual farmer and the location of his land or farm. This roster is known as warabandi and its generation in agriculture practices is a very tedious task. Calculations of time in microseconds are more error-prone, especially whenever it is performed by hands. That division of flow time for an individual farmer can be calculated by warabandi'. However, it generates a full publishable report for an outlet and all the farmers who have farms subjected to be irrigated. It reduces error risk and makes a more reproducible roster. For more details about warabandi system you can found elsewhere in Bandaragoda DJ(1995) <https://publications.iwmi.org/pdf/H_17571i.pdf>.

Noise in the time-series data significantly affects the accuracy of the ARIMA model. Wavelet transformation decomposes the time series data into subcomponents to reduce the noise and help to improve the model performance. The wavelet-ARIMA model can achieve higher prediction accuracy than the traditional ARIMA model. This package provides Wavelet-ARIMA model for time series forecasting based on the algorithm by Aminghafari and Poggi (2012) and Paul and Anjoy (2018) <doi:10.1142/S0219691307002002> <doi:10.1007/s00704-017-2271-x>.

This package implements a spatiotemporal boundary detection model with a dissimilarity metric for areal data with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and spatial correlation is introduced at each time point through a conditional autoregressive (CAR) prior. Temporal correlation is introduced through a hierarchical structure and can be specified as exponential or first-order autoregressive. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in "Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method", by Berchuck et al (2019) <doi:10.1080/01621459.2018.1537911>.

Assortativity coefficients, centrality measures, and clustering coefficients for weighted and directed networks. Rewiring unweighted networks with given assortativity coefficients. Generating general preferential attachment networks.

Conducts single coefficient tests and multiple-contrast hypothesis tests of meta-regression models using cluster wild bootstrapping, based on methods examined in Joshi, Pustejovsky, and Beretvas (2022) <DOI:10.1002/jrsm.1554>.

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.

This package provides the functions to perform a Welch's one-way Anova with fixed effects based on summary statistics (sample size, means, standard deviation) and the Games-Howell post hoc test for multiple comparisons and provides the effect size estimator adjusted omega squared. In addition sample size estimation can be computed based on Levy's method, and a Monte Carlo simulation is included to bootstrap residual normality and homoscedasticity Welch, B. L. (1951) <doi:10.1093/biomet/38.3-4.330> Kirk, R. E. (1996) <doi:10.1177/0013164496056005002> Carroll, R. M., & Nordholm, L. A. (1975) <doi:10.1177/001316447503500304> Albers, C., & Lakens, D. (2018) <doi:10.1016/j.jesp.2017.09.004> Games, P. A., & Howell, J. F. (1976) <doi:10.2307/1164979> Levy, K. J. (1978a) <doi:10.1080/00949657808810246> Show-Li, J., & Gwowen, S. (2014) <doi:10.1111/bmsp.12006>.

This package implements the whitening methods (ZCA, PCA, Cholesky, ZCA-cor, and PCA-cor) discussed in Kessy, Lewin, and Strimmer (2018) "Optimal whitening and decorrelation", <doi:10.1080/00031305.2016.1277159>, as well as the whitening approach to canonical correlation analysis allowing negative canonical correlations described in Jendoubi and Strimmer (2019) "A whitening approach to probabilistic canonical correlation analysis for omics data integration", <doi:10.1186/s12859-018-2572-9>. The package also offers functions to simulate random orthogonal matrices, compute (correlation) loadings and explained variation. It also contains four example data sets (extended UCI wine data, TCGA LUSC data, nutrimouse data, extended pitprops data).

Detects perceptions of warmth and competence in American English self-presentation language. Using trained elastic net regression models, this package provides a numerical representation of warmth and competence perceptions. Methods are described here:<https://github.com/bushraguenoun/warmthcompetence/tree/master/paper>.

This package provides a multivariate weather generator for daily climate variables based on weather-states (Flecher et al. (2010) <doi:10.1029/2009WR008098>). It uses a Markov chain for modeling the succession of weather states. Conditionally to the weather states, the multivariate variables are modeled using the family of Complete Skew-Normal distributions. Parameters are estimated on measured series. Must include the variable Rain and can accept as many other variables as desired.

It shows the connections between selected clusters from the latest time point and the clusters from all the previous time points. The transition matrices between time point t and t+1 are obtained from Waddington-OT analysis <https://github.com/ScialdoneLab/WOTPLY>.

Infectious disease surveillance requires early outbreak detection. This package provides statistical tools for analyzing time-series monitoring data through three core methods: a) EWMA (Exponentially Weighted Moving Average) b) Modified-CUSUM (Modified Cumulative Sum) c) Adjusted-Serfling models Methodologies are based on: - Wang et al. (2010) <doi:10.1016/j.jbi.2009.08.003> - Wang et al. (2015) <doi:10.1371/journal.pone.0119923> Designed for epidemiologists and public health researchers working with disease surveillance systems.

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.

Use various regression models for the analysis of win loss endpoints adjusting for non-binary and multivariate covariates.

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.

This package provides a clean syntax for vectorising the use of Non-Standard Evaluation (NSE), for example in ggplot2', dplyr', or data.table'.

Assess Water Quality Trends for Long-Term Monitoring Data in Estuaries using Generalized Additive Models following Wood (2017) <doi:10.1201/9781315370279> and Error Propagation with Mixed-Effects Meta-Analysis following Sera et al. (2019) <doi:10.1002/sim.8362>. Methods are available for model fitting, assessment of fit, annual and seasonal trend tests, and visualization of results.

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.

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 a conditional independence test that can be applied both to univariate and multivariate random variables. The test is based on a weighted form of the sample covariance of the residuals after a nonlinear regression on the conditioning variables. Details are described in Scheidegger, Hoerrmann and Buehlmann (2022) "The Weighted Generalised Covariance Measure" <http://jmlr.org/papers/v23/21-1328.html>. The test is a generalisation of the Generalised Covariance Measure (GCM) implemented in the R package GeneralisedCovarianceMeasure by Jonas Peters and Rajen D. Shah based on Shah and Peters (2020) "The Hardness of Conditional Independence Testing and the Generalised Covariance Measure" <doi:10.1214/19-AOS1857>.

This package provides tools for simulating the biophysical effects of vessel-strikes on whales. The aim is to support the evaluation of marine policies limiting ship speeds through regions in which whales reside. This is important because ship strikes are a major source of lethality for several whale species, including the critically endangered North Atlantic right whale. In this analysis, whales are modelled with a four-layer system comprising skin, blubber, sub-layer (muscle or organ) and bone. Reasonable values for the material properties of these layers, along with other factors such as whale surface area and mass, are provided for a variety of whale species. Similarly, key values are provided for several ship types. The collision is modelled according to Newtonian dynamics, with stresses and strains within the whale layers being simulated over time. The simulation results are analyzed in the context of whale-strike data, to develop a Lethality Index for the whale in the modelled collision. For the underlying science, see Kelley and other "Assessing the Lethality of Ship Strikes on Whales Using Simple Biophysical Models." (2021) <doi:10.1111/mms.12745>. For more on the R code, see Kelley "`whalestrike`: An R package for simulating ship strikes on whales" (2024) <doi:10.21105/joss.06473>.

This package provides estimation and inference for while-alive regression models targeting the while-alive loss rate for composite endpoints that include recurrent events and a terminal event. The implementation supports flexible time-varying covariate effects through user-selected time bases, including B-splines, natural splines, M-splines, step functions, truncated linear bases, interval-local bases, and piecewise polynomials. Inference can be performed using cluster-robust variance estimators for cluster-randomized trials, with subject-level (IID) variance as a special case. The package includes prediction and plotting utilities and K-fold cross-validation for selecting basis and tuning parameters. Methodology is based on Fang et al. (2025) <doi:10.1093/biostatistics/kxaf047>.