Package runonce helps automating the saving of long-running code to help running the same code multiple times. If you run some long-running code once, it saves the result in a file on disk. Then, if the result already exists, i.e. if the code has already been run and its output has already been saved, it just reads the result from the stored file instead of running the code again.

Estimates life tables, specifically (crude) death rates and (raw and graduated) death probabilities, using rolling windows in one (e.g., age), two (e.g., age and time) or three (e.g., age, time and income) dimensions. The package can also be utilised for summarising statistics and smoothing continuous variables through rolling windows in other domains, such as estimating averages of self-positioning ideology in political science. Acknowledgements: The authors wish to thank Ministerio de Ciencia, Innovación y Universidades (grant PID2021-128228NB-I00) and Generalitat Valenciana (grants HIECPU/2023/2, Conselleria de Hacienda, Economà a y Administración Pública, and CIGE/2023/7, Conselleria de Educación, Cultura, Universidades y Empleo) for supporting this research.

These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses.

Interface to SWI'-'Prolog', <https://www.swi-prolog.org/>. This package is normally not loaded directly, please refer to package rolog instead. The purpose of this package is to provide the Prolog runtime on systems that do not have a software installation of SWI'-'Prolog'.

Utility functions for interacting with the COMPADRE and COMADRE databases of matrix population models. Described in Jones et al. (2021) <doi:10.1101/2021.04.26.441330>.

EZR (Easy R) adds a variety of statistical functions, including survival analyses, ROC analyses, metaanalyses, sample size calculation, and so on, to the R commander. EZR enables point-and-click easy access to statistical functions, especially for medical statistics. EZR is platform-independent and runs on Windows, Mac OS X, and UNIX. Its complete manual is available only in Japanese (Chugai Igakusha, ISBN: 978-4-498-10918-6, Nankodo, ISBN: 978-4-524-21861-5, Ohmsha, ISBN: 978-4-274-22632-8), but an report that introduced the investigation of EZR was published in Bone Marrow Transplantation (Nature Publishing Group) as an Open article. This report can be used as a simple manual. It can be freely downloaded from the journal website as shown below. This report has been cited in more than 14,000 scientific articles.

Retrieve metadata about packages from repositories to explore package dependencies, links between help pages, aliases, package availability on a given date, and other repository-dependent outcome. In addition, it provides access to information about the processes at CRAN. This metadata can be used to help package maintainers and users navigate changes to dependencies and with reproducibility.

The detection of troubling approximate collinearity in a multiple linear regression model is a classical problem in Econometrics. This package is focused on determining whether or not the degree of approximate multicollinearity in a multiple linear regression model is of concern, meaning that it affects the statistical analysis (i.e. individual significance tests) of the model. This objective is achieved by using the variance inflation factor redefined and the scatterplot between the variance inflation factor and the coefficient of variation. For more details see Salmerón R., Garcà a C.B. and Garcà a J. (2018) <doi:10.1080/00949655.2018.1463376>, Salmerón, R., Rodrà guez, A. and Garcà a C. (2020) <doi:10.1007/s00180-019-00922-x>, Salmerón, R., Garcà a, C.B, Rodrà guez, A. and Garcà a, C. (2022) <doi:10.32614/RJ-2023-010>, Salmerón, R., Garcà a, C.B. and Garcà a, J. (2025) <doi:10.1007/s10614-024-10575-8> and Salmerón, R., Garcà a, C.B, Garcà a J. (2023, working paper) <doi:10.48550/arXiv.2005.02245>. You can also view the package vignette using browseVignettes("rvif")', the package website (<https://www.ugr.es/local/romansg/rvif/index.html>) using browseURL(system.file("docs/index.html", package = "rvif")) or version control on GitHub (<https://github.com/rnoremlas/rvif_package>).

The method generate() is extended for spatial multi-site stochastic generation of daily precipitation. It generates precipitation occurrence in several sites using logit regression (Generalized Linear Models) and the approach by D.S. Wilks (1998) <doi:10.1016/S0022-1694(98)00186-3> .

Offers functions for fetching JSON data from the US EPA Air Quality System (AQS) API with options to comply with the API rate limits. See <https://aqs.epa.gov/aqsweb/documents/data_api.html> for details of the AQS API.

Enables the diagnostics and enhancement of regression model calibration.It offers both global and local visualization tools for calibration diagnostics and provides one recalibration method: Torres R, Nott DJ, Sisson SA, Rodrigues T, Reis JG, Rodrigues GS (2024) <doi:10.48550/arXiv.2403.05756>. The method leverages on Probabilistic Integral Transform (PIT) values to both evaluate and perform the calibration of statistical models. For a more detailed description of the package, please refer to the bachelor's thesis available bellow.

This package provides an easy way to report the results of regression analysis, including: 1. Proportional hazards regression from function coxph of package survival'; 2. Conditional logistic regression from function clogit of package survival'; 3. Ordered logistic regression from function polr of package MASS'; 4. Binary logistic regression from function glm of package stats'; 5. Linear regression from function lm of package stats'; 6. Risk regression model for survival analysis with competing risks from function FGR of package riskRegression'; 7. Multilevel model from function lme of package nlme'.

Empirical best linear unbiased prediction (EBLUP) and robust prediction of the area-level means under the basic unit-level model. The model can be fitted by maximum likelihood or a (robust) M-estimator. Mean square prediction error is computed by a parametric bootstrap.

Differences in differences is a methodology to measure the impact of a treatment by comparing a control group, which remains untreated at two different time periods, with a treatment group, which receives the treatment at the later date. In many cases, this approach is used in situations where the intervention doesn't fully apply the treatment to the entire treatment group but rather increases the treatment rate. In response to such fuzzy scenarios, de Chaisemartin and D'Haultfoeuille (2018) <doi:10.1093/restud/rdx049> introduce estimands capable of identifying local average and quantile treatment effects under various assumptions. This R package computes the estimators they are proposing.

Validating sub-national statistical typologies, re-coding across standard typologies of sub-national statistics, and making valid aggregate level imputation, re-aggregation, re-weighting and projection down to lower hierarchical levels to create meaningful data panels and time series.

Utilities for sparse signal recovery suitable for compressed sensing. L1, L2 and TV penalties, DFT basis matrix, simple sparse signal generator, mutual cumulative coherence between two matrices and examples, Lp complex norm, scaling back regression coefficients.

Collection of tools to develop options strategies, value option contracts using the Black-Scholes-Merten option pricing model and calculate the option Greeks. Hull, John C. "Options, Futures, and Other Derivatives" (1997, ISBN:0-13-601589-1). Fischer Black, Myron Scholes (1973) "The Pricing of Options and Corporate Liabilities" <doi:10.1086/260062>.

Provide function for work with AcademyOcean API <https://academyocean.com/api>.

Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>, Engle and Gallo (2006) <doi:10.1016/j.jeconom.2005.01.018>, and Amendola et al. (2024) <doi:10.1016/j.seps.2023.101764>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. rumidas also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.

This package implements regression calibration methods for correcting measurement error in regression models using external or internal reliability studies. Methods are described in Carroll, Ruppert, Stefanski, and Crainiceanu (2006) "Measurement Error in Nonlinear Models: A Modern Perspective" <doi:10.1201/9781420010138>.

This package provides a collection of methods for estimating the basic reproduction number (R0) of infectious diseases. Features a web application to interface with the estimators. Uses the models from: Fisman et al. (2013) <DOI:10.1371/journal.pone.0083622>, Bettencourt and Ribeiro (2008) <DOI:10.1371/journal.pone.0002185>, and White and Pagano (2008) <DOI:10.1002/sim.3136>. Includes datasets for Canadian national and provincial COVID-19 case counts provided by Berry et al. (2021) <DOI:10.1038/s41597-021-00955-2>.

This package provides functions in this package will import filtered variant call format (VCF) files of SNPs data and generate data sets to detect copy number variants, visualize them and do downstream analyses with copy number variants(e.g. Environmental association analyses).

In order to facilitate R instruction for actuaries, we have organized several sets of publicly available data of interest to non-life actuaries. In addition, we suggest a set of packages, which most practicing actuaries will use routinely. Finally, there is an R markdown skeleton for basic reserve analysis.

This package provides a Bayesian-weighted estimator and two unweighted estimators are developed to estimate the number of newly found rare species in additional ecological samples. Among these methods, the Bayesian-weighted estimator and an unweighted (Chao-derived) estimator are of high accuracy and recommended for practical applications. Technical details of the proposed estimators have been well described in the following paper: Shen TJ, Chen YH (2018) A Bayesian weighted approach to predicting the number of newly discovered rare species. Conservation Biology, In press.