This package provides convenience functions for user experience research with an emphasis on quantitative user experience testing and reporting. The functions are designed to translate statistical approaches to applied user experience research.

Complete work flow for the analysis of pharmacokinetic pharmacodynamic (PKPD), physiologically-based pharmacokinetic (PBPK) and systems pharmacology models including: creation of ordinary differential equation-based models, pooled parameter estimation, individual/population based simulations, rule-based simulations for clinical trial design and modeling assays, deployment with a customizable Shiny app, and non-compartmental analysis. System-specific analysis templates can be generated and each element includes integrated reporting with PowerPoint and Word'.

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 streamlined workflow for UK Biobank cloud-based analysis on the Research Analysis Platform (RAP). Includes tools for phenotype extraction and decoding, variable derivation, survival and association analysis, genetic risk score computation, and publication-quality visualization. For details on the UK Biobank resource, see Bycroft et al. (2018) <doi:10.1038/s41586-018-0579-z>.

Density, distribution function, quantile function, and random generating function of the Unit-Garima distribution based on Ayuyuen, S., & Bodhisuwan, W. (2024)<doi:10.18187/pjsor.v20i1.4307>.

Predicts a smooth and continuous (individual) utility function from utility points, and computes measures of intensity for risk and higher-order risk measures (or any other measure computed with user-written function) based on this utility function and its derivatives according to the method introduced in Schneider (2017) <http://hdl.handle.net/21.11130/00-1735-0000-002E-E306-0>.

Comprehensive analysis and forecasting of univariate time series using automatic time series models of many kinds. Harvey AC (1989) <doi:10.1017/CBO9781107049994>. Pedregal DJ and Young PC (2002) <doi:10.1002/9780470996430>. Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>. Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) <doi:10.1007/978-3-540-71918-2>. Gómez V, Maravall A (2000) <doi:10.1002/9781118032978>. Pedregal DJ, Trapero JR and Holgado E (2024) <doi:10.1016/j.ijforecast.2023.09.004>.

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.

The "ussher" data set is drawn from original chronological textual historic events. Commonly known as James Ussher's Annals of the World, the source text was originally written in Latin in 1650, and published in English translation in 1658.The data are classified by index, year, epoch (or one of the 7 ancient "Ages of the World"), Biblical source book if referenced (rarely), as well as alternate dating mechanisms, such as "Anno Mundi" (age of the world) or "Julian Period" (dates based upon the Julian calendar). Additional file "usshfull" includes variables that may be of further interest to historians, such as Southern Kingdom and Northern Kingdom discrepant dates, and the original amalgamated dating mechanic used by Ussher in the original text. The raw data can also be called using "usshraw", as described in: Ussher, J. (1658) <https://archive.org/stream/AnnalsOfTheWorld/Annals_djvu.txt>.

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.

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.

Updated versions of the 1970s "US State Facts and Figures" objects from the datasets package included with R. The new data is compiled from a number of sources, primarily from the United States Census Bureau or the relevant federal agency. Modern tidy tibbles provide richer state-level data including identifiers, geography, capitals, demographics, and socioeconomic statistics. Convenience vectors parallel the base datasets state objects but extend coverage to all 51 jurisdictions: the 50 states and the District of Columbia.

Assess the significance of identified clusters and estimates the true number of clusters by comparing the explained variation due to the clustering from the original data to that produced by clustering a unimodal reference distribution which preserves the covariance structure in the data. The reference distribution is generated using kernel density estimation and a Gaussian copula framework. A dimension reduction strategy and sparse covariance estimation optimize this method for the high-dimensional, low-sample size setting. This method is described in Helgeson, Vock, and Bair (2021) <doi:10.1111/biom.13376>.

Fetch United States Congressional Records from their API <https://api.govinfo.gov/docs/> such as congressional speeches, speaker names, and metadata about congressional sessions, and detailed granule records. Optional parameters allow users to specify congressional sessions, and the maximum number of speeches to retrieve. Data is parsed, cleaned, and returned in a structured dataframe for analysis.

An educational toolkit for learning statistical concepts through interactive exploration. Provides functions for basic statistics (mean, variance, etc.) and probability distributions with step-by-step explanations and interactive learning modes. Each function can be used for simple calculations, detailed learning with explanations, or interactive practice with feedback.

This package provides a simple interface to the Geographic Header information from the "2010 US Census Summary File 2". The entire Summary File 2 is described at <https://catalog.data.gov/dataset/census-2000-summary-file-2-sf2>, but note that this package only provides access to parts of the geographic header ('geoheader') of the file. In particular, only the first 101 columns of the geoheader are included and, more importantly, only rows with summary levels (SUMLEVs) 010 through 050 (nation down through county level) are included. In addition to access to (part of) the geoheader, the package also provides a decode function that takes a column name and value and, for certain columns, returns "the meaning" of that column (i.e., a "SUMLEV" value of 40 means "State"); without a value, the decode function attempts to describe the column itself.

If a procedure consists of several stages and there are several models that can be selected for each stage, uncertainty of the procedure can be decomposed by stages or models. This package includes the ANOVA-based method, the cumulative uncertainty-based method, and the balanced decomposition method. Yongdai Kim et al. (2019) <doi:10.1016/j.hydroa.2019.100024> is a related paper which is accessible via the URL below.

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.

Centers of population (centroid) data for census areas in the United States.

This package provides functions to query the UniTCM API (<https://unitcm.qfxulab.com>), covering herb exploration, compound/ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) data, disease-formula associations, Traditional Chinese Medicine (TCM) ontology, transcriptomics, and gene-disease analysis (MIDAS, Mining Integrated Disease Association Sources).

Verb-like functions to work with messy data, often derived from spreadsheets or parsed PDF tables. Includes functions for unwrapping values broken up across rows, relocating embedded grouping values, and to annotate meaningful formatting in spreadsheet files.

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.

Despite there being a section in RFC 7231 <https://datatracker.ietf.org/doc/html/rfc7231#section-5.5.3> defining a suggested structure for User-Agent headers this data is notoriously difficult to parse consistently. Tools are provided that will take in user agent strings and return structured R objects. This is a V8'-backed package based on the ua-parser project <https://github.com/ua-parser>.

The boundaries for geographical units in the United States of America contained in this package include state, county, congressional district, and zip code tabulation area. Contemporary boundaries are provided by the U.S. Census Bureau (public domain). Historical boundaries for the years from 1629 to 2000 are provided form the Newberry Library's Atlas of Historical County Boundaries (licensed CC BY-NC-SA). Additional data is provided in the USAboundariesData package; this package provides an interface to access that data.