Hospitals, hospital systems, and even trauma systems that provide care to injured patients may not be aware of robust metrics that can help gauge the efficacy of their programs in saving the lives of injured patients. traumar provides robust functions driven by the academic literature to automate the calculation of relevant metrics to individuals desiring to measure the performance of their trauma center or even a trauma system. traumar also provides some helper functions for the data analysis journey. Users can refer to the following publications for descriptions of the methods used in traumar'. TRISS methodology, including probability of survival, and the W, M, and Z Scores - Flora (1978) <doi:10.1097/00005373-197810000-00003>, Boyd et al. (1987, PMID:3106646), Llullaku et al. (2009) <doi:10.1186/1749-7922-4-2>, Singh et al. (2011) <doi:10.4103/0974-2700.86626>, Baker et al. (1974, PMID:4814394), and Champion et al. (1989) <doi:10.1097/00005373-198905000-00017>. For the Relative Mortality Metric, see Napoli et al. (2017) <doi:10.1080/24725579.2017.1325948>, Schroeder et al. (2019) <doi:10.1080/10903127.2018.1489021>, and Kassar et al. (2016) <doi:10.1177/00031348221093563>. For more information about methods to calculate over- and under-triage in trauma hospital populations and samples, please see the following publications - Peng & Xiang (2016) <doi:10.1016/j.ajem.2016.08.061>, Beam et al. (2022) <doi:10.23937/2474-3674/1510136>, Roden-Foreman et al. (2017) <doi:10.1097/JTN.0000000000000283>.

Accompanies the book Rainer Schlittgen and Cristina Sattarhoff (2020) <https://www.degruyter.com/view/title/575978> "Angewandte Zeitreihenanalyse mit R, 4. Auflage" . The package contains the time series and functions used therein. It was developed over many years teaching courses about time series analysis.

This package provides tools for modelling and forecasting epidemic trajectories using a dynamic Gompertz model within a state space framework, with the Kalman filter for robust estimation of non-linear growth. Includes a reinitialization feature to adapt to new waves, and a leading-indicator extension that uses a related series moving ahead of the variable of interest (e.g. cases ahead of hospitalisations) to improve short-horizon forecasts, with model and lag selection via rolling-origin cross-validation. Applicable to data at daily, monthly, quarterly, or annual frequency, and to non-epidemic trajectories with similar dynamics, such as innovation diffusion and product adoption. Includes functions for data preprocessing, model fitting, forecast visualization, and accuracy evaluation using standard error measures. Methods are described in Harvey and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman (2021) <doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, Tang, and Thamotheram (2024) <https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.

This package provides a system built on tidymodels for generating synthetic tabular data. We provide tools for ordering a sequential synthesis, feature and target engineering, sampling, hyperparameter tuning, enforcing constraints, and adding extra noise during a synthesis.

Translate R control flow expressions into Tensorflow graphs.

This package provides a tm Source to create corpora from articles exported from the Europresse content provider as HTML files. It is able to read both text content and meta-data information (including source, date, title, author and pages).

Consolidates and calculates different sets of time-series features from multiple R and Python packages including Rcatch22 Henderson, T. (2021) <doi:10.5281/zenodo.5546815>, feasts O'Hara-Wild, M., Hyndman, R., and Wang, E. (2021) <https://CRAN.R-project.org/package=feasts>, tsfeatures Hyndman, R., Kang, Y., Montero-Manso, P., Talagala, T., Wang, E., Yang, Y., and O'Hara-Wild, M. (2020) <https://CRAN.R-project.org/package=tsfeatures>, tsfresh Christ, M., Braun, N., Neuffer, J., and Kempa-Liehr A.W. (2018) <doi:10.1016/j.neucom.2018.03.067>, TSFEL Barandas, M., et al. (2020) <doi:10.1016/j.softx.2020.100456>, and Kats Facebook Infrastructure Data Science (2021) <https://facebookresearch.github.io/Kats/>.

Test functions are often used to test computer code. They are used in optimization to test algorithms and in metamodeling to evaluate model predictions. This package provides test functions that can be used for any purpose.

Uses read counts for biallelic single nucleotide polymorphisms (SNPs) to compare the likelihoods for the observed read counts given that a sample is either diploid or triploid. It allows parameters to be specified to account for sequencing error rates and allelic bias. For details of the algorithm, please see Delomas (2019) <doi:10.1111/1755-0998.13073>.

Fit, compare, and visualize Bayesian graphical vector autoregressive (GVAR) network models using Stan'. These models are commonly used in psychology to represent temporal and contemporaneous relationships between multiple variables in intensive longitudinal data. Fitted models can be compared with a test based on matrix norm differences of posterior point estimates to quantify the differences between two estimated networks. See also Siepe, Kloft & Heck (2024) <doi:10.31234/osf.io/uwfjc>.

Uses thresholded partial least squares algorithm to create a regression or classification model. For more information, see Lee, Bradlow, and Kable <doi:10.1016/j.crmeth.2022.100227>.

This package provides a modular package for simulating phylogenetic trees and species traits jointly. Trees can be simulated using modular birth-death parameters (e.g. changing starting parameters or algorithm rules). Traits can be simulated in any way designed by the user. The growth of the tree and the traits can influence each other through modifiers objects providing rules for affecting each other. Finally, events can be created to modify both the tree and the traits under specific conditions ( Guillerme, 2024 <DOI:10.1111/2041-210X.14306>).

The tmap package provides two plotting modes for static and interactive thematic maps. This package extends tmap with two additional modes based on Mapbox GL JS and MapLibre GL JS'. These modes feature interactive vector tiles, globe views, and other modern web-mapping capabilities, while maintaining a consistent tmap interface across all plotting modes.

Time series methods for intermittent demand forecasting. Includes Croston's method and its variants (Moving Average, SBA), and the TSB method. Users can obtain optimal parameters on a variety of loss functions, or use fixed ones (Kourenztes (2014) <doi:10.1016/j.ijpe.2014.06.007>). Intermittent time series classification methods and iMAPA that uses multiple temporal aggregation levels are also provided (Petropoulos & Kourenztes (2015) <doi:10.1057/jors.2014.62>).

The goal of trainR is to provide a simple interface to the National Rail Enquiries (NRE) systems. There are few data feeds available, the simplest of them is Darwin, which provides real-time arrival and departure predictions, platform numbers, delay estimates, schedule changes and cancellations. Other data feeds provide historical data, Historic Service Performance (HSP), and much more. trainR simplifies the data retrieval, so that the users can focus on their analyses. For more details visit <https://www.nationalrail.co.uk/46391.aspx>.

This package provides a specialization of dplyr data manipulation verbs that parse and build expressions which are ultimately evaluated by data.table', letting it handle all optimizations. A set of additional verbs is also provided to facilitate some common operations on a subset of the data.

This package provides a tidy toolkit for text that is written in Chinese, Japanese or Korean. Most text tooling in R assumes that words are separated by whitespace, which CJK writing does not use, so ordinary summaries of a text column either treat a sentence as one undifferentiated blob or split it into isolated characters. Word segmentation is therefore a pluggable engine that the caller names explicitly rather than a bundled dictionary, because where a word ends is a fact about a language and not about Unicode. tidycjk classifies characters by Unicode block, reports which script and which language a text is written in, measures how much of a text is CJK, and turns those measurements into tibbles that slot straight into a tidyverse workflow. It also measures display width in terminal columns, pads and truncates to a width rather than to a character count, and normalises fullwidth and halfwidth forms surgically -- including composing halfwidth katakana voiced marks into single code points -- without the collateral damage of a full NFKC pass. Language detection deliberately returns NA rather than guessing when a text is written in Han characters only, because Japanese written without kana cannot be distinguished from Chinese by script alone. Everything is derived from the Unicode specification; the package makes no network requests and needs no compiled code of its own.

This package provides a collection of functions for data analysis with two-by-two contingency tables. The package provides tools to compute measures of effect (odds ratio, risk ratio, and risk difference), calculate impact numbers and attributable fractions, and perform hypothesis testing. Statistical analysis methods are oriented towards epidemiological investigation of relationships between exposures and outcomes.

Use the <https://toggl.com> time tracker api through R.

This package contains R functions for simulating and estimating integer-valued trawl processes as described in the article Veraart (2019),"Modeling, simulation and inference for multivariate time series of counts using trawl processes", Journal of Multivariate Analysis, 169, pages 110-129, <doi:10.1016/j.jmva.2018.08.012> and for simulating random vectors from the bivariate negative binomial and the bi- and trivariate logarithmic series distributions.

This package provides a collection of methods to estimate parameters of different tempered stable distributions (TSD). Currently, there are seven different tempered stable distributions to choose from: Tempered stable subordinator distribution, classical TSD, generalized classical TSD, normal TSD, modified TSD, rapid decreasing TSD, and Kim-Rachev TSD. The package also provides functions to compute density and probability functions and tools to run Monte Carlo simulations. This package has already been used for the estimation of tempered stable distributions (Massing (2023) <arXiv:2303.07060>). The following references form the theoretical background for various functions in this package. References for each function are explicitly listed in its documentation: Bianchi et al. (2010) <doi:10.1007/978-88-470-1481-7_4> Bianchi et al. (2011) <doi:10.1137/S0040585X97984632> Carrasco (2017) <doi:10.1017/S0266466616000025> Feuerverger (1981) <doi:10.1111/j.2517-6161.1981.tb01143.x> Hansen et al. (1996) <doi:10.1080/07350015.1996.10524656> Hansen (1982) <doi:10.2307/1912775> Hofert (2011) <doi:10.1145/2043635.2043638> Kawai & Masuda (2011) <doi:10.1016/j.cam.2010.12.014> Kim et al. (2008) <doi:10.1016/j.jbankfin.2007.11.004> Kim et al. (2009) <doi:10.1007/978-3-7908-2050-8_5> Kim et al. (2010) <doi:10.1016/j.jbankfin.2010.01.015> Kuechler & Tappe (2013) <doi:10.1016/j.spa.2013.06.012> Rachev et al. (2011) <doi:10.1002/9781118268070>.

Creates, manipulates, queries and repairs vectors of parameter terms. Parameter terms are the labels used to reference values in vectors, matrices and arrays. They represent the names in coefficient tables and the column names in mcmc and mcmc.list objects.

Create highly customized tables with this simple and dependency-free package. Data frames can be converted to HTML', LaTeX', Markdown', Word', PNG', PDF', or Typst tables. The user interface is minimalist and easy to learn. The syntax is concise. HTML tables can be customized using the flexible Bootstrap framework, and LaTeX code with the tabularray package.

The goal of tidyplots is to streamline the creation of publication-ready plots for scientific papers. It allows to gradually add, remove and adjust plot components using a consistent and intuitive syntax.