This package provides a procedure for seeding R's built in random number generators using a variable-length sequence of values. Accumulates input entropy into a 256-bit hash digest or "ironseed" and is able to generate a variable-length sequence of output seeds from an ironseed.

This package implements some item response models for multiple ratings, including the hierarchical rater model, conditional maximum likelihood estimation of linear logistic partial credit model and a wrapper function to the commercial FACETS program. See Robitzsch and Steinfeld (2018) for a description of the functionality of the package. See Wang, Su and Qiu (2014; <doi:10.1111/jedm.12045>) for an overview of modeling alternatives.

This package provides access to the Idea Data Center (IDC) application for conducting nonresponse bias analysis (NRBA). The IDC NRBA app is an interactive, browser-based Shiny application that can be used to analyze survey data with respect to response rates, representativeness, and nonresponse bias. This app provides a user-friendly interface to statistical methods implemented by the nrba package. Krenzke, Van de Kerckhove, and Mohadjer (2005) <http://www.asasrms.org/Proceedings/y2005/files/JSM2005-000572.pdf> and Lohr and Riddles (2016) <https://www150.statcan.gc.ca/n1/en/pub/12-001-x/2016002/article/14677-eng.pdf?st=q7PyNsGR> provide an overview of the statistical methods implemented in the application.

Survival analysis of interval-censored data with proportional hazards, and an explicit smooth estimate of the baseline log-hazard with P-splines.

Iterator for generating permutations and combinations. They can be either drawn with or without replacement, or with distinct/ non-distinct items (multiset). The generated sequences are in lexicographical order (dictionary order). The algorithms to generate permutations and combinations are memory efficient. These iterative algorithms enable users to process all sequences without putting all results in the memory at the same time. The algorithms are written in C/C++ for faster performance. Note: iterpc is no longer being maintained. Users are recommended to switch to arrangements'.

Interfaces for choosing important predictors in supervised regression, classification, and censored regression models. Permuted importance scores (Biecek and Burzykowski (2021) <doi:10.1201/9780429027192>) can be computed for tidymodels model fits.

Convert files to and from IDX format to vectors, matrices and arrays. IDX is a very simple file format designed for storing vectors and multidimensional matrices in binary format. The format is described on the website from Yann LeCun <http://yann.lecun.com/exdb/mnist/>.

This package provides a collection of Item Response Theory (IRT) and Computerized Adaptive Testing (CAT) functions that are used in psychometrics.

This package provides tools for importing, merging, and analysing data from international assessment studies (TIMSS, PIRLS, PISA, ICILS, and PIAAC).

Regression models for interval censored data. Currently supports Cox-PH, proportional odds, and accelerated failure time models. Allows for semi and fully parametric models (parametric only for accelerated failure time models) and Bayesian parametric models. Includes functions for easy visual diagnostics of model fits and imputation of censored data.

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

Calculates fundamental IO matrices (Leontief, Wassily W. (1951) <doi:10.1038/scientificamerican1051-15>); within period analysis via various rankings and coefficients (Sonis and Hewings (2006) <doi:10.1080/09535319200000013>, Blair and Miller (2009) <ISBN:978-0-521-73902-3>, Antras et al (2012) <doi:10.3386/w17819>, Hummels, Ishii, and Yi (2001) <doi:10.1016/S0022-1996(00)00093-3>); across period analysis with impact analysis (Dietzenbacher, van der Linden, and Steenge (2006) <doi:10.1080/09535319300000017>, Sonis, Hewings, and Guo (2006) <doi:10.1080/09535319600000002>); and a variety of table operators.

This program facilitates exporting igraph graphs to the SoNIA file format.

This package provides a pipeline to annotate a number of peaks from the IDSL.IPA peaklists using an exhaustive chemical enumeration-based approach. This package can perform elemental composition calculations using the following 15 elements : C, B, Br, Cl, K, S, Si, N, H, As, F, I, Na, O, and P.

Simulate and implement early phase two-stage adaptive dose-finding design for binary and quasi-continuous toxicity endpoints. See Chiuzan et al. (2018) for further reading <DOI:10.1080/19466315.2018.1462727>.

It offers a sophisticated and versatile tool for creating and evaluating artificial intelligence based neural network models tailored for regression analysis on datasets with continuous target variables. Leveraging the power of neural networks, it allows users to experiment with various hidden neuron configurations across two layers, optimizing model performance through "5 fold"" or "10 fold"" cross validation. The package normalizes input data to ensure efficient training and assesses model accuracy using key metrics such as R squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Percentage Error (PER). By storing and visualizing the best performing models, it provides a comprehensive solution for precise and efficient regression modeling making it an invaluable tool for data scientists and researchers aiming to harness AI for predictive analytics.

This package contains two main functions: one for solving general isotone regression problems using the pool-adjacent-violators algorithm (PAVA); another one provides a framework for active set methods for isotone optimization problems with arbitrary order restrictions. Various types of loss functions are prespecified.

Estimate confidence intervals for mean, proportion, mean difference for unpaired and paired samples and proportion difference. Plot the confidence intervals. Generate documents explaining the statistical result step by step.

Offers modeling the association between gene-expression and bioassay data, taking care of the effect due to a fingerprint feature and helps with several plots to better understand the analysis.

This package provides a component-based reactive UI framework for Shiny that binds reactive values directly to DOM attributes, enabling fine-grained updates without re-rendering or update*Input callbacks.

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.

This package provides functions to download, clean, and structure open data from the Brazilian Federal Revenue (Receita Federal do Brasil - RFB) regarding Personal Income Tax (IRPF) statements. Includes a data dictionary and categorized metadata for several sections such as assets, rights, debts, and income brackets. More information about the data source can be found at <https://dados.gov.br/dados/conjuntos-dados/grandes-nmeros-do-imposto-de-renda-da-pessoa-fsica>.

Query for enriched data such as country, region, city, latitude & longitude, ZIP code, time zone, Autonomous System, Internet Service Provider, domain, net speed, International direct dialing (IDD) code, area code, weather station data, mobile data, elevation, usage type, address type, advertisement category, fraud score, and proxy data with an IP address. You can also query a list of hosted domain names for the IP address too. This package uses the IP2Location.io API to query this data. To get started with a free API key, sign up here <https://www.ip2location.io/sign-up?ref=1>.

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.