R interface to access the web services of the ICES Stock Database <https://sd.ices.dk>.

Note that imageData has been superseded by growthPheno'. The package growthPheno incorporates all the functionality of imageData and has functionality not available in imageData', but some imageData functions have been renamed. The imageData package is no longer maintained, but is retained for legacy purposes.

This is an substitute for the %V and %u formats which are not implemented on Windows. In addition, the package offers functions to convert from standard calender format yyyy-mm-dd to and from ISO 8601 week format yyyy-Www-d.

Estimates the density of a spatially distributed animal population sampled with an array of passive detectors, such as traps. Models incorporating distance-dependent detection are fitted by simulation and inverse prediction as proposed by Efford (2004) <doi:10.1111/j.0030-1299.2004.13043.x>.

Computes bootstrapped Monte Carlo estimate of p value of Kolmogorov-Smirnov (KS) test and likelihood ratio test for zero-inflated count data, based on the work of Aldirawi et al. (2019) <doi:10.1109/BHI.2019.8834661>. With the package, user can also find tools to simulate random deviates from zero inflated or hurdle models and obtain maximum likelihood estimate of unknown parameters in these models.

This package provides a collection of several utility functions related to binary incomplete block designs. Contains function to generate A- and D-efficient binary incomplete block designs with given numbers of treatments, number of blocks and block size. Contains function to generate an incomplete block design with specified concurrence matrix. There are functions to generate balanced treatment incomplete block designs and incomplete block designs for test versus control treatments comparisons with specified concurrence matrix. Allows performing analysis of variance of data and computing estimated marginal means of factors from experiments using a connected incomplete block design. Tests of hypothesis of treatment contrasts in incomplete block design set up is supported.

Specific functions are provided for rounding real weights to integers and performing an integer programming algorithm for calibration problems. These functions are useful for census-weights adjustments, survey calibration, or for performing linear regression with integer parameters <https://www.nass.usda.gov/Education_and_Outreach/Reports,_Presentations_and_Conferences/reports/New_Integer_Calibration_%20Procedure_2016.pdf>. This research was supported in part by the U.S. Department of Agriculture, National Agriculture Statistics Service. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, or US Government determination or policy.

Builds statistical control charts with exact limits for univariate and multivariate cases.

This package provides the density, distribution function, quantile function, random generation, and visualization tools for the Inverted Exponentiated Rayleigh Distribution.

Reproducible, programmatic retrieval of datasets from the Inter-university Consortium for Political and Social Research archive.

This package provides a framework for analysing inbreeding and heterozygosity-fitness correlations (HFCs) based on microsatellite and SNP markers.

The Percentage of Importance Indice (Percentage_I.I.) bases in magnitudes, frequencies, and distributions of occurrence of an event (DEMOLIN-LEITE, 2021) <http://cjascience.com/index.php/CJAS/article/view/1009/1350>. This index can detect the key loss sources (L.S) and solution sources (S.S.), classifying them according to their importance in terms of loss or income gain, on the productive system. The Percentage_I.I. = [(ks1 x c1 x ds1)/SUM (ks1 x c1 x ds1) + (ks2 x c2 x ds2) + (ksn x cn x dsn)] x 100. key source (ks) is obtained using simple regression analysis and magnitude (abundance). Constancy (c) is SUM of occurrence of L.S. or S.S. on the samples (absence = 0 or presence = 1), and distribution source (ds) is obtained using chi-square test. This index has derivations: i.e., i) Loss estimates and solutions effectiveness and ii) Attention and non-attention levels (DEMOLIN-LEITE,2024) <DOI: 10.1590/1519-6984.253215>.

This package provides datasets and supporting functions for the book Introduction to Biostatistics with R by Schifano and Yan (2026+), published by Taylor & Francis. The package is intended for teaching introductory biostatistics and for reproducing examples in the text.

This package provides functions are provided to facilitate prior elicitation for Bayesian generalised linear models using independent conditional means priors. The package supports the elicitation of multivariate normal priors for generalised linear models. The approach can be applied to indirect elicitation for a generalised linear model that is linear in the parameters. The package is designed such that the facilitator executes functions within the R console during the elicitation session to provide graphical and numerical feedback at each design point. Various methodologies for eliciting fractiles (equivalently, percentiles or quantiles) are supported, including versions of the approach of Hosack et al. (2017) <doi:10.1016/j.ress.2017.06.011>. For example, experts may be asked to provide central credible intervals that correspond to a certain probability. Or experts may be allowed to vary the probability allocated to the central credible interval for each design point. Additionally, a median may or may not be elicited.

Implementations of the weighted Kozachenko-Leonenko entropy estimator and independence tests based on this estimator, (Kozachenko and Leonenko (1987) <http://mi.mathnet.ru/eng/ppi797>). Also includes a goodness-of-fit test for a linear model which is an independence test between covariates and errors.

This package contains functions for evaluating & comparing the performance of Binary classification models. Functions can be called either statically or interactively (as Shiny Apps).

This package provides a literature-derived database of immune cell markers formatted as Gene Matrix Transposed (GMT) files. Users can search immune cell signatures, retrieve marker lists, export GMT files, create custom marker sets, and score gene-by-cell expression matrices with dependency-free rank-based or mean-expression methods. Cell subpopulations are distinguished by their source PMIDs. For the core curation of the lung cell atlas, see Travaglini et al. (2020) <doi:10.1038/s41586-020-2922-4>. For the pan-cancer B cell signatures, see Fitzsimons et al. (2024) <doi:10.1016/j.ccell.2024.09.011>.

Single Layer Feed-forward Neural networks (SLFNs) have many applications in various fields of statistical modelling, especially for time-series forecasting. However, there are some major disadvantages of training such networks via the widely accepted gradient-based backpropagation algorithm, such as convergence to local minima, dependencies on learning rate and large training time. These concerns were addressed by Huang et al. (2006) <doi:10.1016/j.neucom.2005.12.126>, wherein they introduced the Extreme Learning Machine (ELM), an extremely fast learning algorithm for SLFNs which randomly chooses the weights connecting input and hidden nodes and analytically determines the output weights of SLFNs. It shows good generalized performance, but is still subject to a high degree of randomness. To mitigate this issue, this package uses a dimensionality reduction technique given in Hyvarinen (1999) <doi:10.1109/72.761722>, namely, the Independent Component Analysis (ICA) to determine the input-hidden connections and thus, remove any sort of randomness from the algorithm. This leads to a robust, fast and stable ELM model. Using functions within this package, the proposed model can also be compared with an existing alternative based on the Principal Component Analysis (PCA) algorithm given by Pearson (1901) <doi:10.1080/14786440109462720>, i.e., the PCA based ELM model given by Castano et al. (2013) <doi:10.1007/s11063-012-9253-x>, from which the implemented ICA based algorithm is greatly inspired.

Read data from LimeSurvey (<https://www.limesurvey.org/>) in a comfortable way. Heavily inspired by limer (<https://github.com/cloudyr/limer/>), which lacked a few comfort features for me.

This package provides a simplified version of the IDSL.UFA package to calculate isotopic profiles and adduct formulas from molecular formulas with no dependency on other R packages for online tools and educational mass spectrometry courses. The IDSL.SUFA package also provides an ancillary module to process user-defined adduct formulas.

This package provides an interface to the ISTAT SDMX RESTful API <https://esploradati.istat.it/SDMXWS>. Allows users to discover available datasets, explore their structure and dimensions, and retrieve statistical data from the Italian National Institute of Statistics. Based on the Python istatapi package by Jacopo Attolini.

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.

This package provides tools for measuring income and wealth inequality. Computes the Gini coefficient with bootstrap or asymptotic confidence intervals following Davidson (2009) <doi:10.1016/j.jeconom.2008.11.004>, the extended S-Gini family, Theil T and L indices (generalised entropy family), the Atkinson index, the Kolm absolute inequality index, Palma ratio, Hoover index, percentile ratios, and Lorenz curves. Supports between-within group decomposition following Bourguignon (1979) <doi:10.2307/1914138>, income share tabulation, concentration indices for health inequality with Erreygers (2009) correction, Kakwani tax progressivity and Reynolds-Smolensky redistribution indices, Foster-Greer-Thorbecke poverty measures including the Sen index, growth incidence curves following Ravallion and Chen (2003) <doi:10.1016/S0165-1765(02)00205-7>, and Wolfson polarisation. All functions accept optional survey weights and work with data from any source.

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>.