This package implements a wide range of metrics for measuring glucose control and glucose variability based on continuous glucose monitoring data. The list of implemented metrics is summarized in Rodbard (2009) <doi:10.1089/dia.2009.0015>. Additional visualization tools include time-series plots, lasagna plots and ambulatory glucose profile report.

This package provides the dataset and an implementation of the method illustrated in Friel, N., Rastelli, R., Wyse, J. and Raftery, A.E. (2016) <DOI:10.1073/pnas.1606295113>.

This package implements a Shiny Item Analysis module and functions for computing false positive rate and other binary classification metrics from inter-rater reliability based on Bartoš & Martinková (2024) <doi:10.1111/bmsp.12343>.

This package provides a port of Python's excellent itertools module to R for efficient looping.

This package provides a runtime type system, allowing users to define and implement interfaces, enums, typed data.frame/data.table, as well as typed functions. This package enables stricter type checking and validation, improving code structure, robustness and reliability.

Calculates various intraclass correlation coefficients used to quantify inter-rater and intra-rater reliability. The assumption here is that the raters produced quantitative ratings. Most of the statistical procedures implemented in this package are described in details in Gwet, K.L. (2014, ISBN:978-0970806284): "Handbook of Inter-Rater Reliability," 4th edition, Advanced Analytics, LLC.

This package implements the procedures suggested in Esarey and Sumner (2017) <http://justinesarey.com/interaction-overconfidence.pdf> for controlling the false discovery rate when constructing marginal effects plots for models with interaction terms.

The correction is achieved under the assumption that non-migrating cells of the essay approximately form a quadratic flow profile due to frictional effects, compare law of Hagen-Poiseuille for flow in a tube. The script fits a conical plane to give xyz-coordinates of the cells. It outputs the number of migrated cells and the new corrected coordinates.

Fast and multi-threaded implementation of isolation forest (Liu, Ting, Zhou (2008) <doi:10.1109/ICDM.2008.17>), extended isolation forest (Hariri, Kind, Brunner (2018) <doi:10.48550/arXiv.1811.02141>), SCiForest (Liu, Ting, Zhou (2010) <doi:10.1007/978-3-642-15883-4_18>), fair-cut forest (Cortes (2021) <doi:10.48550/arXiv.2110.13402>), robust random-cut forest (Guha, Mishra, Roy, Schrijvers (2016) <http://proceedings.mlr.press/v48/guha16.html>), and customizable variations of them, for isolation-based outlier detection, clustered outlier detection, distance or similarity approximation (Cortes (2019) <doi:10.48550/arXiv.1910.12362>), isolation kernel calculation (Ting, Zhu, Zhou (2018) <doi:10.1145/3219819.3219990>), and imputation of missing values (Cortes (2019) <doi:10.48550/arXiv.1911.06646>), based on random or guided decision tree splitting, and providing different metrics for scoring anomalies based on isolation depth or density (Cortes (2021) <doi:10.48550/arXiv.2111.11639>). Provides simple heuristics for fitting the model to categorical columns and handling missing data, and offers options for varying between random and guided splits, and for using different splitting criteria.

Multivariate smoothing using iterative bias reduction with kernel, thin plate splines, Duchon splines or low rank splines.

Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Traets F, Sanchez G, and Vandebroek M (2020) <doi:10.18637/jss.v096.i03>.

Combining genomic prediction with Monte Carlo simulation, three different strategies are implemented to select parental lines for multiple traits in plant breeding. The selection strategies include (i) GEBV-O considers only genomic estimated breeding values (GEBVs) of the candidate individuals; (ii) GD-O considers only genomic diversity (GD) of the candidate individuals; and (iii) GEBV-GD considers both GEBV and GD. The above method can be seen in Chung PY, Liao CT (2020) <doi:10.1371/journal.pone.0243159>. Multi-trait genomic best linear unbiased prediction (MT-GBLUP) model is used to simultaneously estimate GEBVs of the target traits, and then a selection index is adopted to evaluate the composite performance of an individual.

This package provides a fragmentation spectra detection pipeline for high-throughput LC/HRMS data processing using peaklists generated by the IDSL.IPA workflow <doi:10.1021/acs.jproteome.2c00120>. The IDSL.CSA package can deconvolute fragmentation spectra from Composite Spectra Analysis (CSA), Data Dependent Acquisition (DDA) analysis, and various Data-Independent Acquisition (DIA) methods such as MS^E, All-Ion Fragmentation (AIF) and SWATH-MS analysis. The IDSL.CSA package was introduced in <doi:10.1021/acs.analchem.3c00376>.

Insurance datasets, which are often used in claims severity and claims frequency modelling. It helps testing new regression models in those problems, such as GLM, GLMM, HGLM, non-linear mixed models etc. Most of the data sets are applied in the project "Mixed models in ratemaking" supported by grant NN 111461540 from Polish National Science Center.

One function to read files. One function to write files. One function to direct plots to screen or file. Automatic file format inference and directory structure creation.

This package provides a simple wrapper around the ical.js library executing Javascript code via V8 (the Javascript engine driving the Chrome browser and Node.js and accessible via the V8 R package). This package enables users to parse iCalendar files ('.ics', .ifb', .iCal', .iFBf') into lists and data.frames to ultimately do statistics on events, meetings, schedules, birthdays, and the like.

This package provides tools for easily and flexibly creating ggplot2 maps with inset maps. One crucial feature of maps is that they have fixed coordinate ratios, i.e., they cannot be distorted, which makes it difficult to manually place inset maps. This package provides functions to automatically position inset maps based on user-defined parameters, making it extremely easy to create maps with inset maps with minimal code.

Computes and decomposes Gini, Bonferroni and Zenga 2007 point and synthetic concentration indexes. Decompositions are intended: by sources, by subpopulations and by sources and subpopulations jointly. References, Zenga M. M.(2007) <doi:10.1400/209575> Zenga M. (2015) <doi:10.1400/246627> Zenga M., Valli I. (2017) <doi:10.26350/999999_000005> Zenga M., Valli I. (2018) <doi:10.26350/999999_000011>.

This package provides analysis results and trial simulation functions for the I-SPY Acute Respiratory Disease Syndrome trial based on composite ranked outcomes. The composite ranked outcome is a hierarchical outcome where trial participants are ranked first by 28 day mortality, then ventilator days, then by advanced respiratory support days. A Bayesian win probability approach is used for analysis. Trial design options include group sequential looks for safety, superiority, futility, and adjustment of randomization probabilities.

Three-stage pipeline for detecting inattention episodes in long psychophysical tasks (200+ trials). Uses accuracy residuals and response pattern signals to locate, sharpen, and formally test candidate inattention regions at trial-level precision.

Calibration and risk-set calibration methods for fitting Cox proportional hazard model when a binary covariate is measured intermittently. Methods include functions to fit calibration models from interval-censored data and modified partial likelihood for the proportional hazard model, Nevo et al. (2018+) <arXiv:1801.01529>.

This toolbox makes working with oxygen, carbon, and clumped isotope data reproducible and straightforward. Use it to quickly calculate isotope fractionation factors, and apply paleothermometry equations.

Convert historical monetary values into their present-day equivalents using bundled CPI (Consumer Price Index) and GDP deflator data sourced from the World Bank Development Indicators. Supports British pounds (GBP), Australian dollars (AUD), US dollars (USD), Euro (EUR), Canadian dollars (CAD), Japanese yen (JPY), Chinese yuan (CNY), Swiss francs (CHF), New Zealand dollars (NZD), Indian rupees (INR), South Korean won (KRW), Brazilian reais (BRL), and Norwegian krone (NOK). Currency codes and country names are both accepted as input.

This package provides a comprehensive suite of tools for managing, processing, and analyzing data from the IFCB. I R FlowCytobot ('iRfcb') supports quality control, geospatial analysis, and preparation of IFCB data for publication in databases like <https://www.gbif.org>, <https://www.obis.org>, <https://emodnet.ec.europa.eu/en>, <https://shark.smhi.se/en/>, and <https://www.ecotaxa.org>. The package integrates with the MATLAB ifcb-analysis tool, which is described in Sosik and Olson (2007) <doi:10.4319/lom.2007.5.204>, and provides features for working with raw, manually classified, and machine learningâ classified image datasets. Key functionalities include image extraction, particle size distribution analysis, taxonomic data handling, and biomass concentration calculations, essential for plankton research.