Offers item-response theory (IRT) ideal-point measurement modeling for diverse distributions, missing data, and over-time variation. Full and approximate Bayesian sampling with Stan (<https://mc-stan.org/>).

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.

This package provides bibliographic information and term-frequency text analysis tools for publications of the U.S. Geological Survey (USGS) Idaho National Laboratory (INL) Project Office. Includes datasets of publications, authors, and term frequencies, along with functions to search terms, build word clouds, and extract text and cover images from publication documents.

Tidyverse'-friendly interface to the Brazilian Institute of Geography and Statistics ('IBGE') aggregate data API <https://servicodados.ibge.gov.br/api/docs/agregados?versao=3>. Query aggregates, variables, localities, periods, and metadata from surveys and censuses conducted by IBGE'.

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.

Calculate false ring proportions from data frames of intra annual density fluctuations.

This package provides a user-friendly interface, using Shiny, to analyse glucose-stimulated insulin secretion (GSIS) assays in pancreatic beta cells or islets. The package allows the user to import several sets of experiments from different spreadsheets and to perform subsequent steps: summarise in a tidy format, visualise data quality and compare experimental conditions without omitting to account for technical confounders such as the date of the experiment or the technician. Together, insane is a comprehensive method that optimises pre-processing and analyses of GSIS experiments in a friendly-user interface. The Shiny App was initially designed for EndoC-betaH1 cell line following method described in Ndiaye et al., 2017 (<doi:10.1016/j.molmet.2017.03.011>).

Manipulate integer-bounded intervals including finding overlaps, piling and merging.

This network estimation procedure eLasso, which is based on the Ising model, combines l1-regularized logistic regression with model selection based on the Extended Bayesian Information Criterion (EBIC). EBIC is a fit measure that identifies relevant relationships between variables. The resulting network consists of variables as nodes and relevant relationships as edges. Can deal with binary data.

The ISA is a biclustering algorithm that finds modules in an input matrix. A module or bicluster is a block of the reordered input matrix.

Easily implement the checking of WHOIS information for a particular domain. IP2WHOIS supports the query for 1113 Top-level Domains(TLDs) and 634 Country Code Top-level Domains(ccTLDs). To get started with a free API key, you may sign up at here <https://www.ip2whois.com/register>.

Neural network has potential in forestry modelling. This package is designed to create and assess Artificial Intelligence based Neural Networks with varying architectures for prediction of volume of forest trees using two input features: height and diameter at breast height, as they are the key factors in predicting volume, therefore development and validation of efficient volume prediction neural network model is necessary. This package has been developed using the algorithm of Tabassum et al. (2022) <doi:10.18805/ag.D-5555>.

Calculates point estimates and standard errors using replicate weights and plausible values for International Large-Scale Assessments (ILSA), including: means, proportions, quantiles, correlations, singlelevel regressions, and multilevel regressions.

This package implements Interpretable Boosted Linear Models (IBLMs). These combine a conventional generalized linear model (GLM) with a machine learning component, such as XGBoost. The package also provides tools within for explaining and analyzing these models. For more details see Gawlowski and Wang (2025) <https://ifoa-adswp.github.io/IBLM/reference/figures/iblm_paper.pdf>.

This package provides functions to estimate the intrinsic dimension of a dataset via likelihood-based approaches. Specifically, the package implements the TWO-NN and Gride estimators and the Hidalgo Bayesian mixture model. In addition, the first reference contains an extended vignette on the usage of the TWO-NN and Hidalgo models. References: Denti (2023, <doi:10.18637/jss.v106.i09>); Allegra et al. (2020, <doi:10.1038/s41598-020-72222-0>); Denti et al. (2022, <doi:10.1038/s41598-022-20991-1>); Facco et al. (2017, <doi:10.1038/s41598-017-11873-y>); Santos-Fernandez et al. (2021, <doi:10.1038/s41598-022-20991-1>).

Estimates the intraclass correlation coefficient for trajectory data using a matrix of distances between trajectories. The distances implemented are the extended Hausdorff distances (Min et al. 2007) <doi:10.1080/13658810601073315> and the discrete Fréchet distance (Magdy et al. 2015) <doi:10.1109/IntelCIS.2015.7397286>.

An implementation to reconstruct individual patient data from Kaplan-Meier (K-M) survival curves, visualize and assess the accuracy of the reconstruction, then perform secondary analysis on the reconstructed data. We involve a simple function to extract the coordinates form the published K-M curves. The function is developed based on Poisot T. â s digitize package (2011) <doi:10.32614/RJ-2011-004> . For more complex and tangled together graphs, digitizing software, such as DigitizeIt (for MAC or windows) or ScanIt'(for windows) can be used to get the coordinates. Additional information should also be involved to increase the accuracy, like numbers of patients at risk (often reported at 5-10 time points under the x-axis of the K-M graph), total number of patients, and total number of events. The package implements the modified iterative K-M estimation algorithm (modified-iKM) improved upon the approach proposed by Guyot (2012) <doi:10.1186/1471-2288-12-9> with some modifications.

Calculates intraclass correlation coefficient (ICC) for assessing reproducibility of interval-censored data with two repeated measurements (Kovacic and Varnai (2014) <doi:10.1097/EDE.0000000000000139>). ICC is estimated by maximum likelihood from model with one fixed and one random effect (both intercepts). Help in model checking (normality of subjects means and residuals) is provided.

Fit a full or subsampling bagging survival tree on a mixture of population (susceptible and nonsusceptible) using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance are computed for variable selection. The thresholds values for variable selection are computed using a nonparametric permutation test. See Cyprien Mbogning and Philippe Broet (2016)<doi:10.1186/s12859-016-1090-x> for an overview about the methods implemented in this package.

Download data from ISTAT (Italian Institute of Statistics) database, both old and new provider (respectively, <http://dati.istat.it/> and <https://esploradati.istat.it/databrowser/>). Additional functions for manipulating data are provided. Moreover, a shiny application called shinyIstat can be used to search, download and filter datasets more easily.

This package provides a lightweight R interface to the Interactive Brokers (IBKR) Client Portal REST API. Functions cover session management, account and portfolio queries, market data retrieval, and order placement and cancellation. Requires a locally running IBKR Client Portal Gateway.

Non-parametric resampling-based inference tests for ExPosition.

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>.

Comprehensive computational routines for random data generation, Maximum Likelihood Estimation (MLE), Maximum Product of Spacings Estimation (MPSE), and MCMC Bayesian estimation under the Improved Adaptive Type-II Progressive Censoring Scheme (IAT-II PCS). Users can supply custom probability density functions (PDF), cumulative distribution functions (CDF), survival functions, parameter ranges, and progressive censoring plans for any continuous univariate lifetime distribution, or rely on built-in parametric models (e.g., Generalized Exponential). Point estimation methods include MLE via optimization algorithms (Broyden-Fletcher-Goldfarb-Shanno (BFGS), Newton-Raphson (NR), Nelder-Mead (NM), Conjugate Gradients (CG), L-BFGS-B, Simulated Annealing (SANN), and Berndt-Hall-Hall-Hausman (BHHH)) and MPSE. Bayesian inference utilizes Metropolis-Hastings within Gibbs sampling under Squared Error Loss (SEL) and LINEX Loss (LL) functions to compute point estimates and Highest Posterior Density (HPD) credible intervals. Asymptotic confidence intervals for parameters, reliability, and hazard rate functions are constructed using asymptotic normality and delta method. Methods are based on Dev and Chacko (2026, Journal of the Iranian Statistical Society, 25, 1-29), Yan, Zhang, and Dong (2021, Journal of Computational and Applied Mathematics, 381, 113022, <doi:10.1016/j.cam.2020.113022>), Ng, Kundu, and Chan (2004, Naval Research Logistics, 51, 1145-1168, <doi:10.1002/nav.20045>), Cheng and Amin (1983, Journal of the Royal Statistical Society Series B, 45, 394-403, <doi:10.1111/j.2517-6161.1983.tb01268.x>), Kundu and Gupta (1999, Australian & New Zealand Journal of Statistics, 41, 173-188, <doi:10.1111/1467-842X.00072>), and Berndt, Hall, Hall, and Hausman (1974, Annals of Economic and Social Measurement, 3, 653-665).