This package provides functions for extracting tidy data from Bayesian treatment effect models, in particular BART, but extensions are possible. Functionality includes extracting tidy posterior summaries as in tidybayes <https://github.com/mjskay/tidybayes>, estimating (average) treatment effects, common support calculations, and plotting useful summaries of these.

An integrated set of extensions to the ergm package to analyze and simulate network evolution based on exponential-family random graph models (ERGM). tergm is a part of the statnet suite of packages for network analysis. See Krivitsky and Handcock (2014) <doi:10.1111/rssb.12014> and Carnegie, Krivitsky, Hunter, and Goodreau (2015) <doi:10.1080/10618600.2014.903087>.

Sometimes you need to split your data and work on the two chunks independently before bringing them back together. Taber allows you to do that with its two functions.

Some tools for cleaning up messy Excel files to be suitable for R. People who have been working with Excel for years built more or less complicated sheets with names, characters, formats that are not homogeneous. To be able to use them in R nowadays, we built a set of functions that will avoid the majority of importation problems and keep all the data at best.

An implementation of the time-series Susceptible-Infected-Recovered (TSIR) model using a number of different fitting options for infectious disease time series data. The manuscript based on this package can be found here <doi:10.1371/journal.pone.0185528>. The method implemented here is described by Finkenstadt and Grenfell (2000) <doi:10.1111/1467-9876.00187>.

Graphic interface for text analysis, implement a few methods such as biplots, correspondence analysis, co-occurrence, clustering, topic models, correlations and sentiments.

The Common Workflow Language <https://www.commonwl.org/> is an open standard for describing data analysis workflows. This package takes the raw Common Workflow Language workflows encoded in JSON or YAML and turns the workflow elements into tidy data frames or lists. A graph representation for the workflow can be constructed and visualized with the parsed workflow inputs, outputs, and steps. Users can embed the visualizations in their Shiny applications, and export them as HTML files or static images.

Enables all rstan functionality for a TMB model object, in particular MCMC sampling and chain visualization. Sampling can be performed with or without Laplace approximation for the random effects. This is demonstrated in Monnahan & Kristensen (2018) <DOI:10.1371/journal.pone.0197954>.

This package provides a tidy set of functions for summarising data, including descriptive statistics, frequency tables with normality testing, and group-wise significance testing. Designed for fast, readable, and easy exploration of both numeric and categorical data.

Estimates the weights and measure of robustness to treatment effect heterogeneity attached to two-way fixed effects regressions. Clément de Chaisemartin, Xavier D'HaultfŠuille (2020) <DOI: 10.1257/aer.20181169>.

This package provides pipeline audit trails and data diagnostics for tidyverse workflows. The audit trail system captures lightweight metadata snapshots at each step of a pipeline, building a structured record without storing the data itself. Operation-aware taps enrich snapshots with join match rates and filter drop statistics. Trails can be serialized to JSON or RDS and exported as self-contained HTML visualizations. Also includes diagnostic functions for interactive data analysis including frequency tables, string quality auditing, and data comparison.

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>.

This package provides an integrated user interface and workflow for the analysis of running, cycling and swimming data from GPS-enabled tracking devices through the trackeR <https://CRAN.R-project.org/package=trackeR> R package.

This package provides test statistics, p-value, and confidence intervals based on 9 hypothesis tests for dependence.

The Gene Expression Omnibus (<https://www.ncbi.nlm.nih.gov/geo/>) and The Cancer Genome Atlas (<https://portal.gdc.cancer.gov/>) are widely used medical public databases. Our platform integrates routine analysis and visualization tools for expression data to provide concise and intuitive data analysis and presentation.

This package provides functions are collected to analyse weather data for agriculture purposes including to read weather records in multiple formats, calculate extreme climate index. Demonstration data are included the SILO daily climate data (licensed under CC BY 4.0, <https://www.longpaddock.qld.gov.au/silo/>).

This package provides a wrapper for The Cancer Imaging Archive's REST API. The Cancer Imaging Archive (TCIA) hosts de-identified medical images of cancer available for public download, as well as rich metadata for each image series. TCIA provides a REST API for programmatic access to the data. This package provides simple functions to access each API endpoint. For more information, see <https://github.com/pamelarussell/TCIApathfinder> and TCIA's website.

This package provides a tm Source to create corpora from a corpus prepared in the format used by the Alceste application (i.e. a single text file with inline meta-data). It is able to import both text contents and meta-data (starred) variables.

This package provides tools for reading, parsing, indexing, and exporting LAS (Log ASCII Standard) well log files into tidy, analysis-ready tabular formats. The package separates LAS header information and log data into structured components, builds a searchable index across collections of LAS files, and enables reproducible subsetting of wells based on metadata or curve availability. Output tables can be written to CSV or Parquet formats to support large-scale statistical, machine learning, and earth science workflows. The tidy data structure follows Wickham (2014) <doi:10.18637/jss.v059.i10>. The LAS file structure follows the Canadian Well Logging Society LAS standard <https://www.cwls.org/wp-content/uploads/2017/02/Las2_Update_Jan2017.pdf>.

This package provides a flexible simulation tool for phylogenetic trees under a general model for speciation and extinction. Trees with a user-specified number of extant tips, or a user-specified stem age are simulated. It is possible to assume any probability distribution for the waiting time until speciation and extinction. Furthermore, the waiting times to speciation / extinction may be scaled in different parts of the tree, meaning we can simulate trees with clade-dependent diversification processes. At a speciation event, one species splits into two. We allow for two different modes at these splits: (i) symmetric, where for every speciation event new waiting times until speciation and extinction are drawn for both daughter lineages; and (ii) asymmetric, where a speciation event results in one species with new waiting times, and another that carries the extinction time and age of its ancestor. The symmetric mode can be seen as an vicariant or allopatric process where divided populations suffer equal evolutionary forces while the asymmetric mode could be seen as a peripatric speciation where a mother lineage continues to exist. Reference: O. Hagen and T. Stadler (2017). TreeSimGM: Simulating phylogenetic trees under general Bellman Harris models with lineage-specific shifts of speciation and extinction in R. Methods in Ecology and Evolution. <doi:10.1111/2041-210X.12917>.

Overall predictive performance is measured by a mean score (or loss), which decomposes into miscalibration, discrimination, and uncertainty components. The main focus is visualization of these distinct and complementary aspects in joint displays. See Dimitriadis, Gneiting, Jordan, Vogel (2024) <doi:10.1016/j.ijforecast.2023.09.007>.

This package provides tools for working with temporal discounting data, designed for behavioural researchers to simplify data cleaning/scoring and model fitting. The package implements widely used methods such as computing indifference points from adjusting amount task (Frye et al., 2016, <doi:10.3791/53584>), testing for non-systematic discounting per the criteria of Johnson & Bickel (2008, <doi:10.1037/1064-1297.16.3.264>), scoring questionnaires according to the methods of Kirby et al. (1999, <doi:10.1037//0096-3445.128.1.78>) and Wileyto et al (2004, <doi:10.3758/BF03195548>), Bayesian model selection using a range of discount functions (Franck et al., 2015, <doi:10.1002/jeab.128>), drift diffusion models of discounting (Peters & D'Esposito, 2020, <doi:10.1371/journal.pcbi.1007615>), and model-agnostic measures of discounting such as area under the curve (Myerson et al., 2001, <doi:10.1901/jeab.2001.76-235>) and ED50 (Yoon & Higgins, 2008, <doi:10.1016/j.drugalcdep.2007.12.011>).

Utilities for rapidly loading specified rows and/or columns of data from large tab-separated value (tsv) files (large: e.g. 1 GB file of 10000 x 10000 matrix). tsvio is an R wrapper to C code that creates an index file for the rows of the tsv file, and uses that index file to collect rows and/or columns from the tsv file without reading the whole file into memory.

Generalized estimating equations (GEE) are a popular choice for analyzing longitudinal binary outcomes. This package provides an interface for fitting GEE, currently for logistic regression, within the tern <https://cran.r-project.org/package=tern> framework (Zhu, Sabanés Bové et al., 2023) and tabulate results easily using rtables <https://cran.r-project.org/package=rtables> (Becker, Waddell et al., 2023). It builds on geepack <doi:10.18637/jss.v015.i02> (Højsgaard, Halekoh and Yan, 2006) for the actual GEE model fitting.