R interface to access the web services of the ICES Stock Assessment Graphs database <https://sg.ices.dk>.

This package provides functions for evaluating and testing asset pricing models, including estimation and testing of factor risk premia, selection of "strong" risk factors (factors having nonzero population correlation with test asset returns), heteroskedasticity and autocorrelation robust covariance matrix estimation and testing for model misspecification and identification. The functions for estimating and testing factor risk premia implement the Fama-MachBeth (1973) <doi:10.1086/260061> two-pass approach, the misspecification-robust approaches of Kan-Robotti-Shanken (2013) <doi:10.1111/jofi.12035>, and the approaches based on tradable factor risk premia of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683>. The functions for selecting the "strong" risk factors are based on the Oracle estimator of Quaini-Trojani-Yuan (2023) <doi:10.2139/ssrn.4574683> and the factor screening procedure of Gospodinov-Kan-Robotti (2014) <doi:10.2139/ssrn.2579821>. The functions for evaluating model misspecification implement the HJ model misspecification distance of Kan-Robotti (2008) <doi:10.1016/j.jempfin.2008.03.003>, which is a modification of the prominent Hansen-Jagannathan (1997) <doi:10.1111/j.1540-6261.1997.tb04813.x> distance. The functions for testing model identification specialize the Kleibergen-Paap (2006) <doi:10.1016/j.jeconom.2005.02.011> and the Chen-Fang (2019) <doi:10.1111/j.1540-6261.1997.tb04813.x> rank test to the regression coefficient matrix of test asset returns on risk factors. Finally, the function for heteroskedasticity and autocorrelation robust covariance estimation implements the Newey-West (1994) <doi:10.2307/2297912> covariance estimator.

Simplifies the generation of customized R Markdown PDF templates. A template may include an individual logo, typography, geometry or color scheme. The package provides a skeleton with detailed instructions for customizations. The skeleton can be modified by changing defaults in the YAML header, by adding additional LaTeX commands or by applying dynamic adjustments in R. Individual corporate design elements, such as a title page, can be added as R functions that produce LaTeX code.

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 a monthly summary of Iowa liquor (class E) sales from January 2015 to October 2020. See the package website for more information, documentation and examples. Data source: Iowa Data portal <https://data.iowa.gov/resource/m3tr-qhgy.csv>.

This package provides functions to support the computations carried out in `An Introduction to Statistical Modeling of Extreme Values by Stuart Coles. The functions may be divided into the following groups; maxima/minima, order statistics, peaks over thresholds and point processes.

The IntCal20 radiocarbon calibration curves (Reimer et al. 2020 <doi:10.1017/RDC.2020.68>) are provided here in a single data package, together with previous IntCal curves (IntCal13, IntCal09, IntCal04, IntCal98) and postbomb curves. Also provided are functions to copy the curves into memory, and to plot the curves and their underlying data, as well as functions to calibrate radiocarbon dates.

When added to an existing shiny app, users may subset any developer-chosen R data.frame on the fly. That is, users are empowered to slice & dice data by applying multiple (order specific) filters using the AND (&) operator between each, and getting real-time updates on the number of rows effected/available along the way. Thus, any downstream processes that leverage this data source (like tables, plots, or statistical procedures) will re-render after new filters are applied. The shiny moduleâ s user interface has a minimalist aesthetic so that the focus can be on the data & other visuals. In addition to returning a reactive (filtered) data.frame, IDEAFilter as also returns dplyr filter statements used to actually slice the data.

Calculation of informative simultaneous confidence intervals for graphical described multiple test procedures and given information weights. Bretz et al. (2009) <doi:10.1002/sim.3495> and Brannath et al. (2024) <doi:10.48550/arXiv.2402.13719>. Furthermore, exploration of the behavior of the informative bounds in dependence of the information weights. Comparisons with compatible bounds are possible. Strassburger and Bretz (2008) <doi:10.1002/sim.3338>.

Fits large-scale regression models with a penalty that restricts the maximum number of non-zero regression coefficients to a prespecified value. While Chu et al (2020) <doi:10.1093/gigascience/giaa044> describe the basic algorithm, this package uses Cyclops for an efficient implementation.

This package implements likelihood based methods for mediation analysis.

This package provides a scaling method to obtain a standardized Moran's I measure. Moran's I is a measure for the spatial autocorrelation of a data set, it gives a measure of similarity between data and its surrounding. The range of this value must be [-1,1], but this does not happen in practice. This package scale the Moran's I value and map it into the theoretical range of [-1,1]. Once the Moran's I value is rescaled, it facilitates the comparison between projects, for instance, a researcher can calculate Moran's I in a city in China, with a sample size of n1 and area of interest a1. Another researcher runs a similar experiment in a city in Mexico with different sample size, n2, and an area of interest a2. Due to the differences between the conditions, it is not possible to compare Moran's I in a straightforward way. In this version of the package, the spatial autocorrelation Moran's I is calculated as proposed in Chen(2013) <arXiv:1606.03658>.

This package provides a unified data layer for single-cell, spatial and bulk T-cell and B-cell immune receptor repertoire data. Think AnnData or SeuratObject, but for AIRR data, a.k.a. Adaptive Immune Receptor Repertoire, VDJ-seq, RepSeq, or VDJ sequencing data.

Carries out integrative clustering analysis using multiple types of genomic dataset using integrative Non-negative Matrix factorization.

This package provides functions and classes to compute, handle and visualise incidence from dated events for a defined time interval. Dates can be provided in various standard formats. The class incidence is used to store computed incidence and can be easily manipulated, subsetted, and plotted. In addition, log-linear models can be fitted to incidence objects using fit'. This package is part of the RECON (<https://www.repidemicsconsortium.org/>) toolkit for outbreak analysis.

Estimation and diagnostic tools for instrumental variables designs, which implements the guidelines proposed in Lal et al. (2023) <arXiv:2303.11399>, including bootstrapped confidence intervals, effective F-statistic, Anderson-Rubin test, valid-t ratio test, and local-to-zero tests.

This software does Multi-Reader, Multi-Case (MRMC) analyses of data from imaging studies where clinicians (readers) evaluate patient images (cases). What does this mean? ... Many imaging studies are designed so that every reader reads every case in all modalities, a fully-crossed study. In this case, the data is cross-correlated, and we consider the readers and cases to be cross-correlated random effects. An MRMC analysis accounts for the variability and correlations from the readers and cases when estimating variances, confidence intervals, and p-values. The functions in this package can treat arbitrary study designs and studies with missing data, not just fully-crossed study designs. An overview of this software, including references presenting details on the methods, can be found here: <https://www.fda.gov/medical-devices/science-and-research-medical-devices/imrmc-software-do-multi-reader-multi-case-statistical-analysis-reader-studies>.

Fit Spatial Econometrics models using Bayesian model averaging on models fitted with INLA. The INLA package can be obtained from <https://www.r-inla.org>.

This package provides a system for submitting multiple IP information queries to IP2Location.io'â s IP Geolocation API and storing the resulting data in a dataframe. You provide a vector of IP addresses and your IP2Location.io API key. The package returns a dataframe with one row per IP address and a column for each available data field (data fields not included in your API plan will contain NAs). This is the second submission of the package to CRAN.

Multiple Imputation for Informative Censoring. This package implements two methods. Gamma Imputation described in <DOI:10.1002/sim.6274> and Risk Score Imputation described in <DOI:10.1002/sim.3480>.

This package provides composable invertible transforms for (sparse) matrices.

Instrumental variable estimation for linear models by two-stage least-squares (2SLS) regression or by robust-regression via M-estimation (2SM) or MM-estimation (2SMM). The main ivreg() model-fitting function is designed to provide a workflow as similar as possible to standard lm() regression. A wide range of methods is provided for fitted ivreg model objects, including extensive functionality for computing and graphing regression diagnostics in addition to other standard model tools.

This package provides user-friendly and configurable print debugging via a single function, ic(). Wrap an expression in ic() to print the expression, its value and (where available) its source location. Debugging output can be toggled globally without modifying code.

An implementation of the Invariance Partial Pruning (IVPP) approach described in Du, X., Johnson, S. U., Epskamp, S. (2025) The Invariance Partial Pruning Approach to The Network Comparison in Longitudinal Data. IVPP is a two-step method that first test for global network structural difference with invariance test and then inspect specific edge difference with partial pruning. The package also allows you to compute centrality measures and use radar chart to plot. Analysis of bridge centralities by community pairs is also possible (e.g., the bridge strength from depression to anxiety, and from depression to panic disorder).