An R wrapper around the API of TheyWorkForYou, a parliamentary monitoring site that scrapes and repackages Hansard (the UK's parliamentary record) and augments it with information from the Register of Members Interests, election results, and voting records to provide a unified source of information about UK legislators and their activities. See <http://www.theyworkforyou.com> for details.

Temporal disaggregation methods are used to disaggregate and interpolate a low frequency time series to a higher frequency series, where either the sum, the mean, the first or the last value of the resulting high frequency series is consistent with the low frequency series. Temporal disaggregation can be performed with or without one or more high frequency indicator series. Contains the methods of Chow-Lin, Santos-Silva-Cardoso, Fernandez, Litterman, Denton and Denton-Cholette, summarized in Sax and Steiner (2013) <doi:10.32614/RJ-2013-028>. Supports most R time series classes.

Snapshots for unit tests using the tinytest framework for R. Includes expectations to test base R and ggplot2 plots as well as console output from print().

Calculate time intelligence metrics for financial planning and analysis. ti provides functions for period-over-period comparisons (year-over-year, month-over-month), period-to-date calculations (YTD, MTD, QTD), and customer segmentation (ABC analysis, cohorts). Supports standard and retail calendars (4-4-5, 4-5-4, 5-4-4) with both in-memory and database backends via dbplyr'.

Reconstructs animal tracks from magnetometer, accelerometer, depth and optional speed data. Designed primarily using data from Wildlife Computers Daily Diary tags deployed on northern fur seals.

Computation of t-year survival probabilities and t-year risks with right censored survival data. The Kaplan-Meier estimator is used to provide estimates for data without competing risks and the Aalen-Johansen estimator is used when there are competing risks. Confidence intervals and p-values are obtained using either usual Wald-type inference or empirical likelihood inference, as described in Thomas and Grunkemeier (1975) <doi:10.1080/01621459.1975.10480315> and Blanche (2020) <doi:10.1007/s10985-018-09458-6>. Functions for both one-sample and two-sample inference are provided. Unlike Wald-type inference, empirical likelihood inference always leads to consistent conclusions, in terms of statistical significance, when comparing two risks (or survival probabilities) via either a ratio or a difference.

The ToxCast Data Analysis Pipeline ('tcpl') is an R package that manages, curve-fits, plots, and stores ToxCast data to populate its linked MySQL database, invitrodb'. The package was developed for the chemical screening data curated by the US EPA's Toxicity Forecaster (ToxCast) program, but tcpl can be used to support diverse chemical screening efforts.

An aid for text mining in R, with a syntax that should be familiar to experienced R users. Provides a wrapper for several topic models that take similarly-formatted input and give similarly-formatted output. Has additional functionality for analyzing and diagnostics for topic models.

This package provides a plug-in for the tm text mining framework providing mail handling functionality.

With the objective of including data from RSS feeds into your analysis, tidyRSS parses RSS, Atom and JSON feeds and returns a tidy data frame.

Tabu search algorithm for binary configurations. A basic version of the algorithm as described by Fouskakis and Draper (2007) <doi:10.1111/j.1751-5823.2002.tb00174.x>.

This package contains functions for calculating the Federal Highway Administration (FHWA) Transportation Performance Management (TPM) performance measures. Currently, the package provides methods for the System Reliability and Freight (PM3) performance measures calculated from travel time data provided by The National Performance Management Research Data Set (NPMRDS), including Level of Travel Time Reliability (LOTTR), Truck Travel Time Reliability (TTTR), and Peak Hour Excessive Delay (PHED) metric scores for calculating statewide reliability performance measures. Implements <https://www.fhwa.dot.gov/tpm/guidance/pm3_hpms.pdf>.

High-resolution movement-sensor tags typically include accelerometers to measure body posture and sudden movements or changes in speed, magnetometers to measure direction of travel, and pressure sensors to measure dive depth in aquatic or marine animals. The sensors in these tags usually sample many times per second. Some tags include sensors for speed, turning rate (gyroscopes), and sound. This package provides software tools to facilitate calibration, processing, and analysis of such data. Tools are provided for: data import/export; calibration (from raw data to calibrated data in scientific units); visualization (for example, multi-panel time-series plots); data processing (such as event detection, calculation of derived metrics like jerk and dynamic acceleration, dive detection, and dive parameter calculation); and statistical analysis (for example, track reconstruction, a rotation test, and Mahalanobis distance analysis).

This package provides functions for imputing missing item responses for dichotomous and polytomous test and assessment data. This package enables missing imputation methods that are suitable for test and assessment data, including: listwise (LW) deletion (see De Ayala et al. 2001 <doi:10.1111/j.1745-3984.2001.tb01124.x>), treating as incorrect (IN, see Lord, 1974 <doi: 10.1111/j.1745-3984.1974.tb00996.x>; Mislevy & Wu, 1996 <doi: 10.1002/j.2333-8504.1996.tb01708.x>; Pohl et al., 2014 <doi: 10.1177/0013164413504926>), person mean imputation (PM), item mean imputation (IM), two-way (TW) and response function (RF) imputation, (see Sijtsma & van der Ark, 2003 <doi: 10.1207/s15327906mbr3804_4>), logistic regression (LR) imputation, predictive mean matching (PMM), and expectationâ maximization (EM) imputation (see Finch, 2008 <doi: 10.1111/j.1745-3984.2008.00062.x>).

This package provides a version of the Titanic survival data tailored for people analytics demonstrations and practice. While another package, titanic', reproduces the Kaggle competition files with minimal preprocessing, tidytitanic combines the train and test datasets into the single dataset, passengers', for exploration and summary across all passengers. It also extracts personal identifiersâ such as first names, last names, and titles from the raw name field, enabling demographic analysis. The passengers data does not cover the crew, but this package also provides the more bare-bones, crew-containing datasets tidy_titanic and flat_titanic based on the Titanic data set from datasets for further exploration. This human-centered data package is designed to support exploratory data analysis, feature engineering, and pedagogical use cases.

Compose data for and extract, manipulate, and visualize posterior draws from Bayesian models ('JAGS', Stan', rstanarm', brms', MCMCglmm', coda', ...) in a tidy data format. Functions are provided to help extract tidy data frames of draws from Bayesian models and that generate point summaries and intervals in a tidy format. In addition, ggplot2 geoms and stats are provided for common visualization primitives like points with multiple uncertainty intervals, eye plots (intervals plus densities), and fit curves with multiple, arbitrary uncertainty bands.

Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.

This package provides methods for representations (i.e. dimensionality reduction, preprocessing, feature extraction) of time series to help more accurate and effective time series data mining. Non-data adaptive, data adaptive, model-based and data dictated (clipped) representation methods are implemented. Also various normalisation methods (min-max, z-score, Box-Cox, Yeo-Johnson), and forecasting accuracy measures are implemented.

Defines S3 vector data types for vectors of functional data (grid-based, spline-based or functional principal components-based) with all arithmetic and summary methods, derivation, integration and smoothing, plotting, data import and export, and data wrangling, such as re-evaluating, subsetting, sub-assigning, zooming into sub-domains, or extracting functional features like minima/maxima and their locations. The implementation allows including such vectors in data frames for joint analysis of functional and scalar variables.

Flexible simulation of time series using time series components, including seasonal, calendar and outlier effects. Main algorithm described in Ollech, D. (2021) <doi:10.1515/jtse-2020-0028>.

Perform two types of analysis: 1) checking the goodness-of-fit of tree models to your single-cell gene expression data; and 2) deciding which tree best fits your data.

This is a tidy implementation for heatmap. At the moment it is based on the (great) package ComplexHeatmap'. The goal of this package is to interface a tidy data frame with this powerful tool. Some of the advantages are: Row and/or columns colour annotations are easy to integrate just specifying one parameter (column names). Custom grouping of rows is easy to specify providing a grouped tbl. For example: df %>% group_by(...). Labels size adjusted by row and column total number. Default use of Brewer and Viridis palettes.

This queue is a data structure that lets parallel processes send and receive messages, and it can help coordinate the work of complicated parallel tasks. Processes can push new messages to the queue, pop old messages, and obtain a log of all the messages ever pushed. File locking preserves the integrity of the data even when multiple processes access the queue simultaneously.

This package provides functions for implementing the targeted gold standard (GS) testing. You provide the true disease or treatment failure status and the risk score, tell TGST the availability of GS tests and which method to use, and it returns the optimal tripartite rules. Please refer to Liu et al. (2013) <doi:10.1080/01621459.2013.810149> for more details.