Perform a correlational class analysis of the data, resulting in a partition of the data into separate modules.

This package produces descriptive interpretations of confidence intervals. Includes (extensible) support for various test types, specified as sets of interpretations dependent on where the lower and upper confidence limits sit. Provides plotting functions for graphical display of interpretations.

This package creates ggplot2 Cumulative Residual (CURE) plots to check the goodness-of-fit of a count model; or the tables to create a customized version. A dataset of crashes in Washington state is available for illustrative purposes.

Calculate some statistics aiming to help analyzing the clustering tendency of given data. In the first version, Hopkins statistic is implemented. See Hopkins and Skellam (1954) <doi:10.1093/oxfordjournals.aob.a083391>.

This package provides a header only, C++ interface to R with enhancements over cpp11'. Enforces copy-on-write semantics consistent with R behavior. Offers native support for ALTREP objects, UTF-8 string handling, modern C++11 features and idioms, and reduced memory requirements. Allows for vendoring, making it useful for restricted environments. Compared to cpp11', it adds support for converting C++ maps to R lists, Roxygen documentation directly in C++ code, proper handling of matrix attributes, support for nullable external pointers, bidirectional copy of complex number types, flexibility in type conversions, use of nullable pointers, and various performance optimizations.

An educational package providing intuitive functions for calculating confidence intervals (CI) for various statistical parameters. Designed primarily for teaching and learning about statistical inference (particularly confidence intervals). Offers user-friendly wrappers around established methods for proportions, means, and bootstrap-based intervals. Integrates seamlessly with Tidyverse workflows, making it ideal for classroom demonstrations and student exercises.

This package provides a collection of functions dedicated to simulating staggered entry platform trials whereby the treatment under investigation is a combination of two active compounds. In order to obtain approval for this combination therapy, superiority of the combination over the two active compounds and superiority of the two active compounds over placebo need to be demonstrated. A more detailed description of the design can be found in Meyer et al. <DOI:10.1002/pst.2194> and a manual in Meyer et al. <arXiv:2202.02182>.

In searching for research articles, we often want to obtain lists of references from across studies, and also obtain lists of articles that cite a particular study. In systematic reviews, this supplementary search technique is known as citation chasing': forward citation chasing looks for all records citing one or more articles of known relevance; backward citation chasing looks for all records referenced in one or more articles. Traditionally, this process would be done manually, and the resulting records would need to be checked one-by-one against included studies in a review to identify potentially relevant records that should be included in a review. This package contains functions to automate this process by making use of the Lens.org API. An input article list can be used to return a list of all referenced records, and/or all citing records in the Lens.org database (consisting of PubMed, PubMed Central, CrossRef, Microsoft Academic Graph and CORE; <https://www.lens.org>).

Calculate agrometeorological variables for crops including growing degree days (McMaster, GS & Wilhelm, WW (1997) <doi:10.1016/S0168-1923(97)00027-0>), cumulative rainfall, number of stress days and cumulative or mean radiation and evaporation. Convert dates to day of year and vice versa. Also, download curated and interpolated Australian weather data from the Queensland Government DES longpaddock website <https://www.longpaddock.qld.gov.au/>. This data is freely available under the Creative Commons 4.0 licence.

Implementation of the d/p/q/r family of functions for a continuous analog to the standard discrete beta-binomial with continuous size parameter and continuous support with x in [0, size + 1].

Computes a confidence interval for a specified linear combination of the regression parameters in a linear regression model with iid normal errors with unknown variance when there is uncertain prior information that a distinct specified linear combination of the regression parameters takes a specified number. This confidence interval, found by numerical nonlinear constrained optimization, has the required minimum coverage and utilizes this uncertain prior information through desirable expected length properties. This confidence interval is proposed by Kabaila, P. and Giri, K. (2009) <doi:10.1016/j.jspi.2009.03.018>.

An efficient cross-validated approach for covariance matrix estimation, particularly useful in high-dimensional settings. This method relies upon the theory of high-dimensional loss-based covariance matrix estimator selection developed by Boileau et al. (2022) <doi:10.1080/10618600.2022.2110883> to identify the optimal estimator from among a prespecified set of candidates.

The reliability of assessment tools is a crucial aspect of monitoring student performance in various educational settings. It ensures that the assessment outcomes accurately reflect a student's true level of performance. However, when assessments are combined, determining composite reliability can be challenging, especially for naturalistic and unbalanced datasets in nested design as is often the case for Workplace-Based Assessments. This package is designed to estimate composite reliability in nested designs using multivariate generalizability theory and enhance the analysis of assessment data. The package allows for the inclusion of weight per assessment type and produces extensive G- and D-study results with graphical interpretations, and options to find the set of weights that maximizes the composite reliability or minimizes the standard error of measurement (SEM).

The Satellite Application Facility on Climate Monitoring (CM SAF) is a ground segment of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and one of EUMETSATs Satellite Application Facilities. The CM SAF contributes to the sustainable monitoring of the climate system by providing essential climate variables related to the energy and water cycle of the atmosphere (<https://www.cmsaf.eu>). It is a joint cooperation of eight National Meteorological and Hydrological Services. The cmsaf R-package includes a shiny based interface for an easy application of the cmsafops and cmsafvis packages - the CM SAF R Toolbox. The Toolbox offers an easy way to prepare, manipulate, analyse and visualize CM SAF NetCDF formatted data. Other CF conform NetCDF data with time, longitude and latitude dimension should be applicable, but there is no guarantee for an error-free application. CM SAF climate data records are provided for free via (<https://wui.cmsaf.eu/safira>). Detailed information and test data are provided on the CM SAF webpage (<http://www.cmsaf.eu/R_toolbox>).

An implementation of Fan plots for cytometry data in ggplot2'. For reference see Britton, E.; Fisher, P. & J. Whitley (1998) The Inflation Report Projections: Understanding the Fan Chart <https://www.bankofengland.co.uk/quarterly-bulletin/1998/q1/the-inflation-report-projections-understanding-the-fan-chart>).

Modeling periodic mortality (or other time-to event) processes from right-censored data. Given observations of a process with a known period (e.g. 365 days, 24 hours), functions determine the number, intensity, timing, and duration of peaks of periods of elevated hazard within a period. The underlying model is a mixed wrapped Cauchy function fitted using maximum likelihoods (details in Gurarie et al. (2020) <doi:10.1111/2041-210X.13305>). The development of these tools was motivated by the strongly seasonal mortality patterns observed in many wild animal populations. Thus, the respective periods of higher mortality can be identified as "mortality seasons".

Fit continuous-time correlated random walk models with time indexed covariates to animal telemetry data. The model is fit using the Kalman-filter on a state space version of the continuous-time stochastic movement process.

This package provides a comprehensive set of functions designed for multivariate mean monitoring using the Critical-to-X Control Chart. These functions enable the determination of optimal control limits based on a specified in-control Average Run Length (ARL), the calculation of out-of-control ARL for a given control limit, and post-signal analysis to identify the specific variable responsible for a detected shift in the mean. This suite of tools provides robust support for precise and effective process monitoring and analysis.

Retrieves crypto currency information and historical prices as well as information on the exchanges they are listed on. Historical data contains daily open, high, low and close values for all crypto currencies. All data is scraped from <https://coinmarketcap.com> via their web-api'.

Cancer RADAR is a project which aim is to develop an infrastructure that allows quantifying the risk of cancer by migration background across Europe. This package contains a set of functions cancer registries partners should use to reshape 5 year-age group cancer incidence data into a set of summary statistics (see Boyle & Parkin (1991, ISBN:978-92-832-1195-2)) in lines with Cancer RADAR data protections rules.

Isotonic regression (IR) and its improvement: centered isotonic regression (CIR). CIR is recommended in particular with small samples. Also, interval estimates for both, and additional utilities such as plotting dose-response data. For dev version and change history, see GitHub assaforon/cir.

Light weight implementation of the standard distribution functions for the chi distribution, wrapping those for the chi-squared distribution in the stats package.

Collection of indices and tools relating to clinical research that aid epidemiological cohort or retrospective chart review with big data. All indices and tools take commonly used lab values, patient demographics, and clinical measurements to compute various risk and predictive values for survival or further classification/stratification. References to original literature and validation contained in each function documentation. Includes all commonly available calculators available online.

Data package for the supplementary data in Prem et al. (2017) <doi:10.1371/journal.pcbi.1005697> and Prem et al. <doi:10.1371/journal.pcbi.1009098>. Provides easy access to contact data for 177 countries, for use in epidemiological, demographic or social sciences research.