United is a software tool which can be downloaded at the following website <http://www.schroepl.net/pbm/software/united/>. In general, it is a virtual manager game for football teams. This package contains helpful functions for determining an optimal formation for a virtual match in United. E.g. knowing that the opponent has a strong defensive it is advisable to beat him in the midfield. Furthermore, this package contains functions for computing the optimal usage of hardness in a game.

This package provides functions for estimating uncertainty in the number of fatalities in the Uppsala Conflict Data Program (UCDP) data. The package implements a parametric reported-value Gumbel mixture distribution that accounts for the uncertainty in the number of fatalities in the UCDP data. The model is based on information from a survey on UCDP coders and how they view the uncertainty of the number of fatalities from UCDP events. The package provides functions for making random draws of fatalities from the mixture distribution, as well as to estimate percentiles, quantiles, means, and other statistics of the distribution. Full details on the survey and estimation procedure can be found in Vesco et al (2024).

Uniform sampling on various geometric shapes, such as spheres, ellipsoids, simplices.

This package provides a set of general functions that I have used in various projects and other R packages. Miscellaneous operations on data frames, matrices and vectors, ROC and PR statistics.

This package provides an algorithm to detect and characterize disturbances (start, end dates, intensity) that can occur at different hierarchical levels by studying the dynamics of longitudinal observations at the unit level and group level based on Nadaraya-Watson's smoothing curves, but also a shiny app which allows to visualize the observations and the detected disturbances. Finally the package provides a dataframe mimicking a pig farming system subsected to disturbances simulated according to Le et al.(2022) <doi:10.1016/j.animal.2022.100496>.

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 implements Minimum Torsion for portfolio diversification as described in Meucci, Attilio (2013) <doi:10.2139/ssrn.2276632>.

Model data with a suspected clustering structure (either in co-variate space, regression space or both) using a Bayesian product model with a logistic regression likelihood. Observations are represented graphically and clusters are formed through various edge removals or additions. Cluster quality is assessed through the log Bayesian evidence of the overall model, which is estimated using either a Sequential Monte Carlo sampler or a suitable transformation of the Bayesian Information Criterion as a fast approximation of the former. The internal Iterated Batch Importance Sampling scheme (Chopin (2002 <doi:10.1093/biomet/89.3.539>)) is made available as a free standing function.

This package provides easy access to a curated selection of pre-processed data sets relevant to the COVID-19 outbreak in the UK for teaching and demonstration purposes.

This package provides a fast and simple URL parser package for R'. This package provides functions to parse URLs into their components, such as scheme, user, password, host, port, path, query, and fragment.

User-friendly maximum likelihood estimation (Fisher (1921) <doi:10.1098/rsta.1922.0009>) of univariate densities.

Download and explore datasets from UCSC Xena data hubs, which are a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others. Databases are normalized so they can be combined, linked, filtered, explored and downloaded.

This package provides a method for estimating log-normalizing constants (or free energies) and expectations from multiple distributions (such as multiple generalized ensembles).

Offers tools for parsing and analyzing URL datasets, extracting key components and identifying common patterns. It aids in examining website architecture and identifying SEO issues, helping users optimize web presence and content strategy.

This package provides a diverse collection of U.S. datasets encompassing various fields such as crime, economics, education, finance, energy, healthcare, and more. It serves as a valuable resource for researchers and analysts seeking to perform in-depth analyses and derive insights from U.S.-specific data.

Intended to be used by the United States Copyright Office Product Management Division Business Analysts. Include algorithms for the United States Copyright Office Product Management Division SR Audit Data dataset. The algorithm takes in the SR Audit Data excel file and reformat the spreadsheet such that the values and variables fit the format of the online database. Support functions in this package include clean_str(), which cleans instances of variable AUDIT_LOG; clean_data_to_excel(), which cleans and output the reorganized SR Audit Data dataset in excel format; clean_data_to_dataframe(), which cleans and stores the reorganized SR Audit Data data set to a data frame; format_from_excel(), which reads in the outputted excel file from the clean_data_to_excel() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys. format_from_dataframe(), which reads in the outputted data frame from the clean_data_to_dataframe() function and formats and returns the data as a dictionary that uses FIELD types as keys and NON-FIELD types as the values of those keys; support_function(), which takes in the dictionary outputted either from the format_from_dataframe() or format_from_excel() function and returns the data as a formatted data frame according to the original U.S. Copyright Office SR Audit Data online database. The main function of this package is clean_format_all(), which takes in an excel file and returns the formatted data into a new excel and text file according to the format from the U.S. Copyright Office SR Audit Data online database.

This package provides a ggplot2 theme and color palettes following the United Nations High Commissioner for Refugees (UNHCR) Data Visualization Guidelines recommendations.

The Unmanned Aerial Vehicle Mission Planner provides an easy to use work flow for planning autonomous obstacle avoiding surveys of ready to fly unmanned aerial vehicles to retrieve aerial or spot related data. It creates either intermediate flight control files for the DJI-Litchi supported series or ready to upload control files for the pixhawk-based flight controller. Additionally it contains some useful tools for digitizing and data manipulation.

This package provides tools for formatting and summarizing data for outcomes research.

Allows users to access live UK energy market information via various APIs.

Uniform Error Index is the weighted average of different error measures. Uniform Error Index utilizes output from different error function and gives more robust and stable error values. This package has been developed to compute Uniform Error Index from ten different loss function like Error Square, Square of Square Error, Quasi Likelihood Error, LogR-Square, Absolute Error, Absolute Square Error etc. The weights are determined using Principal Component Analysis (PCA) algorithm of Yeasin and Paul (2024) <doi:10.1007/s11227-023-05542-3>.

Estimate ambient vitamin D-effective or erythemal dose using ultraviolet radiation (UV) data from the TEMIS database, based on date and geographical location.

Separate a data frame in two based on key columns. The function unjoin() provides an inside-out version of a nested data frame. This is used to identify duplication and normalize it (in the database sense) by linking two tables with the redundancy removed. This is a basic requirement for detecting topology within spatial structures that has motivated the need for this package as a building block for workflows within more applied projects.

When updating major or minor R versions all packages should be re-installed. The utilities in this package assist in getting a user up-and-running again by installing all previously installed R packages. The package uses renv to install; immediately replenishing your renv package cache.