Generation of Box-Cox based ROC curves and several aspects of inferences and hypothesis testing. Can be used when inferences for one biomarker (Bantis LE, Nakas CT, Reiser B. (2018)<doi:10.1002/bimj.201700107>) are of interest or when comparisons of two correlated biomarkers (Bantis LE, Nakas CT, Reiser B. (2021)<doi:10.1002/bimj.202000128>) are of interest. Provides inferences and comparisons around the AUC, the Youden index, the sensitivity at a given specificity level (and vice versa), the optimal operating point of the ROC curve (in the Youden sense), and the Youden based cutoff.

Computation of the International Roughness Index (IRI) given a longitudinal road profile. The IRI can be calculated for a single road segment or for a sequence of segments with a fixed length (e. g. 100m). For the latter, an overlap of the segments can be selected. The IRI and likewise the algorithms for its determination are defined in Sayers, Michael W; Gillespie, Thomas D; Queiroz, Cesar A.V. 1986. The International Road Roughness Experiment (IRRE) : establishing correlation and a calibration standard for measurements. World Bank technical paper; no. WTP 45. Washington, DC : The World Bank. (ISBN 0-8213-0589-1) available from <http://documents.worldbank.org/curated/en/326081468740204115>.

Casting metadata for REDCap database creation and handling of castellated data using repeated instruments and longitudinal projects in REDCap'. Keeps a focused data export approach, by allowing to only export required data from the database. Also for casting new REDCap databases based on datasets from other sources. Originally forked from the R part of REDCapRITS by Paul Egeler. See <https://github.com/pegeler/REDCapRITS>. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources (Harris et al (2009) <doi:10.1016/j.jbi.2008.08.010>; Harris et al (2019) <doi:10.1016/j.jbi.2019.103208>).

This package provides a trimmed down copy of the "kent-core source tree" turned into a C library for manipulation of .2bit files. See <https://genome.ucsc.edu/FAQ/FAQformat.html#format7> for a quick overview of the 2bit format. The "kent-core source tree" can be found here: <https://github.com/ucscGenomeBrowser/kent-core/>. Only the .c and .h files from the source tree that are related to manipulation of .2bit files were kept. Note that the package is primarily useful to developers of other R packages who wish to use the 2bit C library in their own C'/'C++ code.

Ports the Ripser <doi:10.48550/arXiv.1908.02518> and Cubical Ripser <doi:10.48550/arXiv.2005.12692> persistent homology calculation engines from C++. Can be used as a rapid calculation tool in topological data analysis pipelines.

Sends texts to the <https://www.receptiviti.com> API to be scored, and facilitates the creation of custom norms and local results databases.

Since the early 1970s eyewitness testimony researchers have recognised the importance of estimating properties such as lineup bias (is the lineup biased against the suspect, leading to a rate of choosing higher than one would expect by chance?), and lineup size (how many reasonable choices are in fact available to the witness? A lineup is supposed to consist of a suspect and a number of additional members, or foils, whom a poor-quality witness might mistake for the perpetrator). Lineup measures are descriptive, in the first instance, but since the earliest articles in the literature researchers have recognised the importance of reasoning inferentially about them. This package contains functions to compute various properties of laboratory or police lineups, and is intended for use by researchers in forensic psychology and/or eyewitness testimony research. Among others, the r4lineups package includes functions for calculating lineup proportion, functional size, various estimates of effective size, diagnosticity ratio, homogeneity of the diagnosticity ratio, ROC curves for confidence x accuracy data and the degree of similarity of faces in a lineup.

This package provides a simple approach to configuring R projects with different parameter values. Configurations are specified using a reduced subset of base R and parsed accordingly.

Differences in differences is a methodology to measure the impact of a treatment by comparing a control group, which remains untreated at two different time periods, with a treatment group, which receives the treatment at the later date. In many cases, this approach is used in situations where the intervention doesn't fully apply the treatment to the entire treatment group but rather increases the treatment rate. In response to such fuzzy scenarios, de Chaisemartin and D'Haultfoeuille (2018) <doi:10.1093/restud/rdx049> introduce estimands capable of identifying local average and quantile treatment effects under various assumptions. This R package computes the estimators they are proposing.

This package provides helper functions for authenticating and retrieving data from your ODK-X Sync Endpoint'. This is an early release intended for testing and feedback.

This package provides a task-oriented R interface to the RDKit <https://www.rdkit.org> library through its Python API via reticulate'. The package offers high-level cheminformatics functionality, including molecule parsing, descriptor calculation, and fingerprint generation without replicating the native structure of RDKit'.

The rerddapUtils package is an R package that is a set of four main functions designed to work with and extend the rerddap package. These functions includes one for restricting by season, one for splitting large requests, and two for working with projected datasets. There are also two utility functions that provide estimates of the size of a proposed rerddap::griddap() request.

This package provides an Rcmdr "plug-in" based on the TeachingDemos package, and is primarily for illustrative purposes.

This package provides a lightweight wrapper around the RSQLite package for streamlined loading of data from tabular files (i,e. delimited text files like Comma Separated Values and Tab Separated Values, Microsoft Excel, and Arrow Inter-process Communication files) in SQLite databases. Includes helper functions for inspecting the structure of the input files, and some functions to simplify activities on the SQLite tables.

Loads Axon Binary Files (both ABF and ABF2') created by Axon Instruments/Molecular Devices software such as pClamp'.

This package provides functions and methods for manipulating SNOMED CT concepts. The package contains functions for loading the SNOMED CT release into a convenient R environment, selecting SNOMED CT concepts using regular expressions, and navigating the SNOMED CT ontology. It provides the SNOMEDconcept S3 class for a vector of SNOMED CT concepts (stored as 64-bit integers) and the SNOMEDcodelist S3 class for a table of concepts IDs with descriptions. The package can be used to construct sets of SNOMED CT concepts for research (<doi:10.1093/jamia/ocac158>). For more information about SNOMED CT visit <https://www.snomed.org/>.

This package provides functions to reconstruct sessions from web log or other user trace data and calculate various metrics around them, producing tabular, output that is compatible with dplyr or data.table centered processes.

Test Statistics for Independence in High-Dimensional Datasets. This package consists of two functions to perform the complete independence test based on test statistics proposed by Bulut (unpublished yet) and suggested by Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. The Bulut's statistic is not sensitive to outliers in high-dimensional data, unlike one of Najarzadeh (2021) <doi: 10.1080/03610926.2019.1702699>. So, the Bulut's statistic can be performed robustly by using RDnp function.

Implementation of the RESTK algorithm based on Markov's Inequality from Vilardell, Sergi, Serra, Isabel, Mezzetti, Enrico, Abella, Jaume, Cazorla, Francisco J. and Del Castillo, J. (2022). "Using Markov's Inequality with Power-Of-k Function for Probabilistic WCET Estimation". In 34th Euromicro Conference on Real-Time Systems (ECRTS 2022). Leibniz International Proceedings in Informatics (LIPIcs) 231 20:1-20:24. <doi:10.4230/LIPIcs.ECRTS.2022.20>. This work has been supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No. 772773).

This package provides robust parameter tuning and model training for predictive models applied across data sources where the data distribution varies slightly from source to source. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the RobustTuneC method. External tuning includes a conservative option where parameters are tuned internally on the training data and validating on an external dataset, providing a slightly pessimistic estimate. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. Currently, only binary classification is supported. The response variable must be the first column of the dataset and a factor with exactly two levels. The tuning methods are based on the paper by Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung (2021) "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning" <doi:10.1007/s00357-020-09368-z>.

Implementations for several robust procedures that allow for (online) extraction of the signal of univariate or multivariate time series by applying robust regression techniques to a moving time window are provided. Included are univariate filtering procedures based on repeated-median regression as well as hybrid and trimmed filters derived from it; see Schettlinger et al. (2006) <doi:10.1515/BMT.2006.010>. The adaptive online repeated median by Schettlinger et al. (2010) <doi:10.1002/acs.1105> and the slope comparing adaptive repeated median by Borowski and Fried (2013) <doi:10.1007/s11222-013-9391-7> choose the width of the moving time window adaptively. Multivariate versions are also provided; see Borowski et al. (2009) <doi:10.1080/03610910802514972> for a multivariate online adaptive repeated median and Borowski (2012) <doi:10.17877/DE290R-14393> for a multivariate slope comparing adaptive repeated median. Furthermore, a repeated-median based filter with automatic outlier replacement and shift detection is provided; see Fried (2004) <doi:10.1080/10485250410001656444>.

This package provides a set of functions to build simple GUI controls for R functions. These are built on the tcltk package. Uses could include changing a parameter on a graph by animating it with a slider or a "doublebutton", up to more sophisticated control panels. Some functions for specific graphical tasks, referred to as cartoons', are provided.

Mixed Treatment Comparison is a methodology to compare directly and/or indirectly health strategies (drugs, treatments, devices). This package provides an Rcmdr plugin to perform Mixed Treatment Comparison for binary outcome using BUGS code from Bristol University (Lu and Ades).

This package provides a full-featured RethinkDB <https://rethinkdb.com/> client for R; allows users to store JSON-serialised data in a robust, distributed no-SQL system, use rich queries and react to data changes in real-time.