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>.

Contain code to work with a C struct, in short cgeneric, to define a Gaussian Markov random (GMRF) model. The cgeneric contain code to specify GMRF elements such as the graph and the precision matrix, and also the initial and prior for its parameters, useful for model inference. It can be accessed from a C program and is the recommended way to implement new GMRF models in the INLA package (<https://www.r-inla.org>). The INLAtools implement functions to evaluate each one of the model specifications from R. The implemented functionalities leverage the use of cgeneric models and provide a way to debug the code as well to work with the prior for the model parameters and to sample from it. A very useful functionality is the Kronecker product method that creates a new model from multiple cgeneric models. It also works with the rgeneric, the R version of the cgeneric intended to easy try implementation of new GMRF models. The Kronecker between two cgeneric models was used in Sterrantino et. al. (2024) <doi:10.1007/s10260-025-00788-y>, and can be used to build the spatio-temporal intrinsic interaction models for what the needed constraints are automatically set.

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>.

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

To implement a general framework to quantitatively infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis, abbreviated as iCAMP (Ning et al 2020) <doi:10.1038/s41467-020-18560-z>. It can quantitatively assess the relative importance of different community assembly processes, such as selection, dispersal, and drift, for both communities and each phylogenetic group ('bin'). Each bin usually consists of different taxa from a family or an order. The package also provides functions to implement some other published methods, including neutral taxa percentage (Burns et al 2016) <doi:10.1038/ismej.2015.142> based on neutral theory model and quantifying assembly processes based on entire-community null models ('QPEN', Stegen et al 2013) <doi:10.1038/ismej.2013.93>. It also includes some handy functions, particularly for big datasets, such as phylogenetic and taxonomic null model analysis at both community and bin levels, between-taxa niche difference and phylogenetic distance calculation, phylogenetic signal test within phylogenetic groups, midpoint root of big trees, etc. Version 1.3.x mainly improved the function for QPEN and added function icamp.cate() to summarize iCAMP results for different categories of taxa (e.g. core versus rare taxa).

Fits the (randomized drift) inverse Gaussian distribution to survival data. The model is described in Aalen OO, Borgan O, Gjessing HK. Survival and Event History Analysis. A Process Point of View. Springer, 2008. It is based on describing time to event as the barrier hitting time of a Wiener process, where drift towards the barrier has been randomized with a Gaussian distribution. The model allows covariates to influence starting values of the Wiener process and/or average drift towards a barrier, with a user-defined choice of link functions.

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/>, 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.

API wrapper that contains functions to retrieve data from the IsoMemo partnership databases. Web services for API: <https://isomemodb.com/api/v1/iso-data>.

This package implements an S7 class for estimates based on influence functions, with forward mode automatic differentiation defined for standard arithmetic operations.

Plot idiograms of karyotypes, plasmids, circular chr. having a set of data.frames for chromosome data and optionally mark data. Two styles of chromosomes can be used: without or with visible chromatids. Supports micrometers, cM and Mb or any unit. Three styles of centromeres are available: triangle, rounded and inProtein; and six styles of marks are available: square (squareLeft), dots, cM (cMLeft), cenStyle, upArrow (downArrow), exProtein (inProtein); its legend (label) can be drawn inline or to the right of karyotypes. Idiograms can also be plotted in concentric circles. It is possible to calculate chromosome indices by Levan et al. (1964) <doi:10.1111/j.1601-5223.1964.tb01953.x>, karyotype indices of Watanabe et al. (1999) <doi:10.1007/PL00013869> and Romero-Zarco (1986) <doi:10.2307/1221906> and classify chromosomes by morphology Guerra (1986) and Levan et al. (1964).

We provide data sets used in the textbook "Introduction to Sports Analytics using R" by Elmore and Urbaczweski (2025).

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>.

Utility functions that implement and automate common sets of validation tasks. These functions are particularly useful to validate inputs, intermediate objects and output values in user-defined functions, resulting in tidier and less verbose functions.

Synthesize images into characteristic features for time-series analysis or machine learning applications. The package was originally intended for monitoring volcanic eruptions in video data by highlighting and extracting regions above the vent associated with plume activity. However, the functions within are general and have wide applications for image processing, analyzing, filtering, and plotting.

This package provides tools to download, process, and analyze data from the International Monetary Fund's World Economic Outlook (WEO) database <https://www.imf.org/en/Publications/SPROLLs/world-economic-outlook-databases>. Functions support downloading complete WEO releases, accessing specific economic indicators for selected countries, and listing available data.

Utilities to work with data from the Internal Displacement Monitoring Centre (IDMC) (<https://www.internal-displacement.org/>), with convenient functions for loading events data from the IDMC API and transforming events data to daily displacement estimates.

Make empirical Bayes incidence curves from reported case data using a specified delay distribution.

An interval-valued extension of ordinary and simple kriging. Optimization of the function is based on a generalized interval distance. This creates a non-differentiable cost function that requires a differentiable approximation to the absolute value function. This differentiable approximation is optimized using a Newton-Raphson algorithm with a penalty function to impose the constraints. Analyses in the package are driven by the intsp and intgrd classes, which are interval-valued extensions of SpatialPointsDataFrame and SpatialPixelsDataFrame respectively. The package includes several wrappers to functions in the gstat and sp packages.

Graphical User Interface allowing to determine the concentration in the sample in CFU per mL or in number of copies per mL provided to qPCR results after with or without PMA treatment. This package is simply to use because no knowledge in R commands is necessary. A graphic represents the standard curve, and a table containing the result for each sample is created.

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.

Models, analyzes, and forecasts financial intraday signals. This package currently supports a univariate state-space model for intraday trading volume provided by Chen (2016) <doi:10.2139/ssrn.3101695>.

Flexibly implements Integral Projection Models using a mathematical(ish) syntax. This package will not help with the vital rate modeling process, but will help convert those regression models into an IPM. ipmr handles density dependence and environmental stochasticity, with a couple of options for implementing the latter. In addition, provides functions to avoid unintentional eviction of individuals from models. Additionally, provides model diagnostic tools, plotting functionality, stochastic/deterministic simulations, and analysis tools. Integral projection models are described in depth by Easterling et al. (2000) <doi:10.1890/0012-9658(2000)081[0694:SSSAAN]2.0.CO;2>, Merow et al. (2013) <doi:10.1111/2041-210X.12146>, Rees et al. (2014) <doi:10.1111/1365-2656.12178>, and Metcalf et al. (2015) <doi:10.1111/2041-210X.12405>. Williams et al. (2012) <doi:10.1890/11-2147.1> discuss the problem of unintentional eviction.

Simulate general insurance policies, losses and loss emergence. The functions contemplate deterministic and stochastic policy retention and growth scenarios. Retention and growth rates are percentages relative to the expiring portfolio. Claims are simulated for each policy. This is accomplished either be assuming a frequency distribution per development lag or by generating random wait times until claim emergence and settlement. Loss simulation uses standard loss distributions for claim amounts.

This package implements continuous-time hidden Markov models (HMMs) to infer identity-by-descent (IBD) segments shared by two individuals from their single-nucleotide polymorphism (SNP) genotypes. Provides posterior probabilities at each marker (forward-backward algorithm), prediction of IBD segments (Viterbi algorithm), and functions for visualising results. Supports both autosomal data and X-chromosomal data.