This package provides an extension to the Partial Credit Model and Generalized Partial Credit Models which allows for an additional person parameter that characterizes the uncertainty of the person. The method was originally proposed by Tutz and Schauberger (2020) <doi:10.1177/0146621620920932>.

This package provides a suite of utilities for working with the UK Biobank <https://www.ukbiobank.ac.uk/> Nuclear Magnetic Resonance spectroscopy (NMR) metabolomics data <https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=220>. Includes functions for extracting biomarkers from decoded UK Biobank field data, removing unwanted technical variation from biomarker concentrations, computing an extended set of lipid, fatty acid, and cholesterol fractions, and for re-deriving composite biomarkers and ratios after adjusting data for unwanted biological variation. For further details on methods see Ritchie SC et al. Sci Data (2023) <doi:10.1038/s41597-023-01949-y>.

UNIfied Cross-Omics deconvolution (Unico) deconvolves standard 2-dimensional bulk matrices of samples by features into a 3-dimensional tensors representing samples by features by cell types. Unico stands out as the first principled model-based deconvolution method that is theoretically justified for any heterogeneous genomic data. For more details see Chen and Rahmani et al. (2024) <doi:10.1101/2024.01.27.577588>.

Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers â Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association <doi:10.1080/01621459.2023.2259030>".

An R API providing easy access to a relational database with macroeconomic, financial and development related time series data for Uganda. Overall more than 5000 series at varying frequency (daily, monthly, quarterly, annual in fiscal or calendar years) can be accessed through the API. The data is provided by the Bank of Uganda, the Ugandan Ministry of Finance, Planning and Economic Development, the IMF and the World Bank. The database is being updated once a month.

By gaining the property of emergence through self-organization, the enhancement of SOMs(self organizing maps) is called Emergent SOM (ESOM). The result of the projection by ESOM is a grid of neurons which can be visualised as a three dimensional landscape in form of the Umatrix. Further details can be found in the referenced publications (see url). This package offers tools for calculating and visualising the ESOM as well as Umatrix, Pmatrix and UStarMatrix. All the functionality is also available through graphical user interfaces implemented in shiny'. Based on the recognized data structures, the method can be used to generate new data.

This natural language processing toolkit provides language-agnostic tokenization', parts of speech tagging', lemmatization and dependency parsing of raw text. Next to text parsing, the package also allows you to train annotation models based on data of treebanks in CoNLL-U format as provided at <https://universaldependencies.org/format.html>. The techniques are explained in detail in the paper: Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at <doi:10.18653/v1/K17-3009>. The toolkit also contains functionalities for commonly used data manipulations on texts which are enriched with the output of the parser. Namely functionalities and algorithms for collocations, token co-occurrence, document term matrix handling, term frequency inverse document frequency calculations, information retrieval metrics (Okapi BM25), handling of multi-word expressions, keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) sentiment scoring and semantic similarity analysis.

An implementation of Lind and Mehlum's (2010) <doi:10.1111/j.1468-0084.2009.00569.x> Utest to test for the presence of a U shaped or inverted U shaped relationship between variables in (generalized) linear models. It also implements a test of upward/downward sloping relationships at the lower and upper boundary of the data range.

This package provides a generic reference Bayesian analysis of unidimensional mixture distributions obtained by a location-scale parameterisation of the model is implemented. The including functions simulate and summarize posterior samples for location-scale mixture models using a weakly informative prior. There is no need to define priors for scale-location parameters except two hyperparameters in which are associated with a Dirichlet prior for weights and a simplex.

This package provides a collection of parametric quantile regression models for bounded data. At present, the package provides 13 parametric quantile regression models. It can specify regression structure for any quantile and shape parameters. It also provides several S3 methods to extract information from fitted model, such as residual analysis, prediction, plotting, and model comparison. For more computation efficient the [dpqr]'s, likelihood, score and hessian functions are written in C++. For further details see Mazucheli et. al (2022) <doi:10.1016/j.cmpb.2022.106816>.

Clustering and classification inference for high dimension low sample size (HDLSS) data with U-statistics. The package contains implementations of nonparametric statistical tests for sample homogeneity, group separation, clustering, and classification of multivariate data. The methods have high statistical power and are tailored for data in which the dimension L is much larger than sample size n. See Gabriela B. Cybis, Marcio Valk and SÃ lvia RC Lopes (2018) <doi:10.1080/00949655.2017.1374387>, Marcio Valk and Gabriela B. Cybis (2020) <doi:10.1080/10618600.2020.1796398>, Debora Z. Bello, Marcio Valk and Gabriela B. Cybis (2021) <arXiv:2106.09115>.

Variance approximations for the Horvitz-Thompson total estimator in Unequal Probability Sampling using only first-order inclusion probabilities. See Matei and Tillé (2005) and Haziza, Mecatti and Rao (2008) for details.

S3 classes and methods for manipulation with georeferenced raster data: reading/writing, processing, multi-panel visualization.

This package provides functions for converting between UK and US spellings of English words.

Implement a shrinkage estimation for the univariate normal mean based on a preliminary test (pretest) estimator. This package also provides the confidence interval based on pivoting the cumulative density function. The methodologies are published in Taketomi et al.(2024) <doi:10.1007/s42081-023-00221-2> and Taketomi et al.(2024-)(under review).

Supervised classification methods, which (if asked) can provide step-by-step explanations of the algorithms used, as described in PK Josephine et. al., (2021) <doi:10.59176/kjcs.v1i1.1259>; and datasets to test them on, which highlight the strengths and weaknesses of each technique.

This package provides tools for converting data from complex or irregular layouts to a columnar structure. For example, tables with multilevel column or row headers, or spreadsheets. Header and data cells are selected by their contents and position, as well as formatting and comments where available, and are associated with one other by their proximity in given directions. Functions for data frames and HTML tables are provided.

The Ultimate Microrray Prediction, Reality and Inference Engine (UMPIRE) is a package to facilitate the simulation of realistic microarray data sets with links to associated outcomes. See Zhang and Coombes (2012) <doi:10.1186/1471-2105-13-S13-S1>. Version 2.0 adds the ability to simulate realistic mixed-typed clinical data.

This package provides a tool for checking how much information is disclosed when reporting summary statistics.

This package provides implementations of some of the most important outlier detection algorithms. Includes a tutorial mode option that shows a description of each algorithm and provides a step-by-step execution explanation of how it identifies outliers from the given data with the specified input parameters. References include the works of Azzedine Boukerche, Lining Zheng, and Omar Alfandi (2020) <doi:10.1145/3381028>, Abir Smiti (2020) <doi:10.1016/j.cosrev.2020.100306>, and Xiaogang Su, Chih-Ling Tsai (2011) <doi:10.1002/widm.19>.

An alternative for downloading various United States Department of Agriculture (USDA) data from <https://quickstats.nass.usda.gov/> through R. You must sign up for an API token from the mentioned website in order for this package to work.

Core functions necessary for using The Globe and Mail's R data journalism template, startr', along with utilities for day-to-day data journalism tasks, such as reading and writing files, producing graphics and cleaning up datasets.

Interface to easily access data via the United States Department of Agriculture (USDA)'s Livestock Mandatory Reporting ('LMR') Data API at <https://mpr.datamart.ams.usda.gov/>. The downloaded data can be saved for later off-line use. Also provide relevant information and metadata for each of the input variables needed for sending the data inquiry.

Full listing of UK baby names occurring more than three times per year between 1974 and 2020, and rankings of baby name popularity by decade from 1904 to 1994.