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 a toolkit to set up an R data package in a consistent structure. Automates tasks like tidy data export, data dictionary documentation, README and website creation, and citation management.

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.

An implementation of the Weighted Portmanteau Tests described in "New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing" published by the Journal of the American Statistical Association, Volume 107, Issue 498, pages 777-787, 2012.

Fits the combination of Wavelet-GARCH model for time series forecasting using algorithm by Paul (2015) <doi:10.3233/MAS-150328>.

Evaluation of prediction performance of smaller regions of spectra for Chemometrics. Segmentation of spectra, evolving dimensions regions and sliding windows as selection methods. Election of the best model among those computed based on error metrics. Chen et al.(2017) <doi:10.1007/s00216-017-0218-9>.

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.

Read from, interogate, and write to Wikidata <https://www.wikidata.org> - the multilingual, interdisciplinary, semantic knowledgebase. Includes functions to: read from wikidata (single items, properties, or properties); query wikidata (retrieving all items that match a set of criterial via Wikidata SPARQL query service); write to Wikidata (adding new items or statements via QuickStatements); and handle and manipulate Wikidata objects (as lists and tibbles). Uses the Wikidata and Quickstatements APIs.

The distributions of the weight of evidence (log Bayes factor) favouring case over noncase status in a test dataset (or test folds generated by cross-validation) can be used to quantify the performance of a diagnostic test (McKeigue (2019), <doi:10.1177/0962280218776989>). The package can be used with any test dataset on which you have observed case-control status and have computed prior and posterior probabilities of case status using a model learned on a training dataset. To quantify how the predictor will behave as a risk stratifier, the quantiles of the distributions of weight of evidence in cases and controls can be calculated and plotted.

Search and download data from the World Bank Data API.

This package provides data to be used by the wordpiece algorithm in order to tokenize text into somewhat meaningful chunks. Included vocabularies were retrieved from <https://huggingface.co/bert-base-cased/resolve/main/vocab.txt> and <https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt> and parsed into an R-friendly format.

This package provides unified syntax to write data from lazy dplyr tbl or dplyr sql query or a dataframe to a database table with modes such as create, append, insert, update, upsert, patch, delete, overwrite, overwrite_schema.

Spatial data are generally auto-correlated, meaning that if two units selected are close to each other, then it is likely that they share the same properties. For this reason, when sampling in the population it is often needed that the sample is well spread over space. A new method to draw a sample from a population with spatial coordinates is proposed. This method is called wave (Weakly Associated Vectors) sampling. It uses the less correlated vector to a spatial weights matrix to update the inclusion probabilities vector into a sample. For more details see Raphaël Jauslin and Yves Tillé (2019) <doi:10.1007/s13253-020-00407-1>.

This package provides functions for easily creating interactive web pages using R Markdown that students can use in self-guided learning.

An enhanced implementation of Whittaker-Henderson smoothing for the graduation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. WH is based on the methods described in Biessy (2025) <doi:10.48550/arXiv.2306.06932>. Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.

Computes Bayesian wavelet shrinkage credible intervals for nonparametric regression. The method uses cumulants to derive Bayesian credible intervals for wavelet regression estimates. The first four cumulants of the posterior distribution of the estimates are expressed in terms of the observed data and integer powers of the mother wavelet functions. These powers are closely approximated by linear combinations of wavelet scaling functions at an appropriate finer scale. Hence, a suitable modification of the discrete wavelet transform allows the posterior cumulants to be found efficiently for any data set. Johnson transformations then yield the credible intervals themselves. Barber, S., Nason, G.P. and Silverman, B.W. (2002) <doi:10.1111/1467-9868.00332>.

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.

For a given Sentence-Aligned Parallel Corpus, it aligns words for each sentence pair. It considers one-to-many and symmetrization alignments. Moreover, it evaluates the quality of word alignment based on this package and some other software. It also builds an automatic dictionary of two languages based on given parallel corpus.

This package provides functions for finding and pulling data from the Wisconsin Department of Natural Resources ArcGIS REST APIs <https://dnrmaps.wi.gov/arcgis/rest/services> and <https://dnrmaps.wi.gov/arcgis2/rest/services>.

Run mixed-effects models that include weights at every level. The WeMix package fits a weighted mixed model, also known as a multilevel, mixed, or hierarchical linear model (HLM). The weights could be inverse selection probabilities, such as those developed for an education survey where schools are sampled probabilistically, and then students inside of those schools are sampled probabilistically. Although mixed-effects models are already available in R, WeMix is unique in implementing methods for mixed models using weights at multiple levels. Both linear and logit models are supported. Models may have up to three levels. Random effects are estimated using the PIRLS algorithm from lme4pureR (Walker and Bates (2013) <https://github.com/lme4/lme4pureR>).

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 functions are collected to analyse weather data for agriculture purposes including to read weather records in multiple formats, calculate extreme climate index. Demonstration data are included the SILO daily climate data (licensed under CC BY 4.0, <https://www.longpaddock.qld.gov.au/silo/>).

This package provides a comprehensive data analysis framework for NIH-funded research that streamlines workflows for both data cleaning and preparing NIH Data Archive ('NDA') submission templates. Provides unified access to multiple data sources ('REDCap', MongoDB', Qualtrics') through interfaces to their APIs, with specialized functions for data cleaning, filtering, merging, and parsing. Features automatic validation, field harmonization, and memory-aware processing to enhance reproducibility in multi-site collaborative research as described in Mittal et al. (2021) <doi:10.20900/jpbs.20210011>.

The BACON algorithms are methods for multivariate outlier nomination (detection) and robust linear regression by Billor, Hadi, and Velleman (2000) <doi:10.1016/S0167-9473(99)00101-2>. The extension to weighted problems is due to Beguin and Hulliger (2008) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X200800110616>; see also <doi:10.21105/joss.03238>.