This package provides an R interface to rclone <https://rclone.org>, a command-line program for managing files on cloud storage. rclone supports over 40 cloud storage providers including S3'-compatible services ('Amazon S3', MinIO', Ceph'), Google Cloud Storage', Azure Blob Storage', and many others. This package downloads and manages the rclone binary automatically and wraps its commands as R functions, returning results as data frames where appropriate.

Implementations of several robust nonparametric two-sample tests for location or scale differences. The test statistics are based on robust location and scale estimators, e.g. the sample median or the Hodges-Lehmann estimators as described in Fried & Dehling (2011) <doi:10.1007/s10260-011-0164-1>. The p-values can be computed via the permutation principle, the randomization principle, or by using the asymptotic distributions of the test statistics under the null hypothesis, which ensures (approximate) distribution independence of the test decision. To test for a difference in scale, we apply the tests for location difference to transformed observations; see Fried (2012) <doi:10.1016/j.csda.2011.02.012>. Random noise on a small range can be added to the original observations in order to hold the significance level on data from discrete distributions. The location tests assume homoscedasticity and the scale tests require the location parameters to be zero.

This package provides tools for qPCR data analysis using Delta Ct and Delta Delta Ct methods, including t-test, Wilcoxon-test, ANOVA models, and publication-ready visualizations. The package supports multiple target, and multiple reference genes, and uses a calculation framework adopted from Ganger et al. (2017) <doi:10.1186/s12859-017-1949-5> and Taylor et al. (2019) <doi:10.1016/j.tibtech.2018.12.002>, covering both the Livak and Pfaffl methods.

This package provides R and JavaScript functions to allow WebGL'-based 3D plotting using the three.js JavaScript library. Interactivity through roll-over highlighting and toggle buttons is also supported.

Create an R Journal Rmarkdown template article, that will generate html and pdf versions of your paper. Check that the paper folder has all the required components needed for submission. Examples of R Journal publications can be found at <https://journal.r-project.org>.

This package contains utilities for the analysis of Michaelian kinetic data. Beside the classical linearization methods (Lineweaver-Burk, Eadie-Hofstee, Hanes-Woolf and Eisenthal-Cornish-Bowden), features include the ability to carry out weighted regression analysis that, in most cases, substantially improves the estimation of kinetic parameters (Aledo (2021) <doi:10.1002/bmb.21522>). To avoid data transformation and the potential biases introduced by them, the package also offers functions to directly fitting data to the Michaelis-Menten equation, either using ([S], v) or (time, [S]) data. Utilities to simulate substrate progress-curves (making use of the Lambert W function) are also provided. The package is accompanied of vignettes that aim to orientate the user in the choice of the most suitable method to estimate the kinetic parameter of an Michaelian enzyme.

Blaze is an open-source, high-performance C++ math library for dense and sparse arithmetic. With its state-of-the-art Smart Expression Template implementation Blaze combines the elegance and ease of use of a domain-specific language with HPC-grade performance, making it one of the most intuitive and fastest C++ math libraries available. The RcppBlaze package includes the header files from the Blaze library with disabling some functionalities related to link to the thread and system libraries which make RcppBlaze be a header-only library. Therefore, users do not need to install Blaze'.

Robust parameter estimation and prediction of Gaussian stochastic process emulators. It allows for robust parameter estimation and prediction using Gaussian stochastic process emulator. It also implements the parallel partial Gaussian stochastic process emulator for computer model with massive outputs See the reference: Mengyang Gu and Jim Berger, 2016, Annals of Applied Statistics; Mengyang Gu, Xiaojing Wang and Jim Berger, 2018, Annals of Statistics.

R implementation of SIDES-based subgroup search algorithms (Lipkovich et al. (2017) <doi:10.1002/sim.7064>).

Collection of models and analysis methods used in regional and urban economics and (quantitative) economic geography, e.g. measures of inequality, regional disparities and convergence, regional specialization as well as accessibility and spatial interaction models.

This package provides a simple user-friendly library based on the python module reservoirpy'. It provides a flexible interface to implement efficient Reservoir Computing (RC) architectures with a particular focus on Echo State Networks (ESN). Some of its features are: offline and online training, parallel implementation, sparse matrix computation, fast spectral initialization, advanced learning rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, and complex feedback loops. Moreover, graphical tools are included to easily explore hyperparameters. Finally, it includes several tutorials exploring time series forecasting, classification and hyperparameter tuning. For more information about reservoirpy', please see Trouvain et al. (2020) <doi:10.1007/978-3-030-61616-8_40>. This package was developed in the framework of the University of Bordeauxâ s IdEx "Investments for the Future" program / RRI PHDS.

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>).

Allows work with Management API for load counters, segments, filters, user permissions and goals list from Yandex Metrica, Reporting API allows you to get information about the statistics of site visits and other data without using the web interface, Logs API allows to receive non-aggregated data and Compatible with Google Analytics Core Reporting API v3 allows receive information about site traffic and other data using field names from Google Analytics Core API. For more information see official documents <https://yandex.ru/dev/metrika/doc/api2/concept/about-docpage>.

This package provides functions for assessing variable relations and associations prior to modeling with a Random Forest algorithm (although these are relevant for any predictive model). Metrics such as partial correlations and variance inflation factors are tabulated as well as plotted for the user. A function is available for tuning the main Random Forest hyper-parameter based on model performance and variable importance metrics. This grid-search technique provides tables and plots showing the effect of the main hyper-parameter on each of the assessment metrics. It also returns each of the evaluated models to the user. The package also provides superior variable importance plots for individual models. All of the plots are developed so that the user has the ability to edit and improve further upon the plots. Derivations and methodology are described in Bladen (2022) <https://digitalcommons.usu.edu/etd/8587/>.

Download up-to-date data from the Reserve Bank of Australia in a tidy data frame. Package includes functions to download current and historical statistical tables (<https://www.rba.gov.au/statistics/tables/>) and forecasts (<https://www.rba.gov.au/publications/smp/forecasts-archive.html>). Data includes a broad range of Australian macroeconomic and financial time series.

This package implements the fast iterative shrinkage-thresholding algorithm (FISTA) algorithm to fit a Gamma distribution with an elastic net penalty as described in Chen, Arakvin and Martin (2018) <doi:10.48550/arXiv.1804.07780>. An implementation for the case of the exponential distribution is also available, with details available in Chen and Martin (2018) <doi:10.2139/ssrn.3085672>.

Computes confidence intervals for binomial or Poisson rates and their differences or ratios. Including the rate (or risk) difference ('RD') or rate ratio (or relative risk, RR') for binomial proportions or Poisson rates, and odds ratio ('OR', binomial only). Also confidence intervals for RD, RR or OR for paired binomial data, and estimation of a proportion from clustered binomial data. Includes skewness-corrected asymptotic score ('SCAS') methods, which have been developed in Laud (2017) <doi:10.1002/pst.1813> from Miettinen and Nurminen (1985) <doi:10.1002/sim.4780040211> and Gart and Nam (1988) <doi:10.2307/2531848>, and in Laud (2026, under review) for paired proportions. In each case, the same score produces hypothesis tests that are improved versions of the non-inferiority test for binomial RD and RR by Farrington and Manning (1990) <doi:10.1002/sim.4780091208>, or a generalisation of the McNemar test for paired data. The package also includes MOVER methods (Method Of Variance Estimates Recovery) for all contrasts, derived from the Newcombe method but with options to use equal-tailed intervals in place of the Wilson score method, and generalised for Bayesian applications incorporating prior information. So-called exact methods for strictly conservative coverage are approximated using continuity adjustments, and the amount of adjustment can be selected to avoid over-conservative coverage. Also includes methods for stratified calculations (e.g. meta-analysis), either with fixed effect assumption (matching the CMH test) or incorporating stratum heterogeneity.

This package provides plotting helpers, table-formatting utilities, and report templates for econometrics, model development, and applied data analysis. Includes University of Cincinnati branded themes for ggplot2', modelsummary', flextable', rmarkdown', bookdown', and quarto'.

An interface to the Mangal database - a collection of ecological networks. This package includes functions to work with the Mangal RESTful API methods (<https://mangal-interactions.github.io/mangal-api/>).

This package provides tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data (Goolsby et al. 2017). Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.

This package contains logic for sample-level variable set scoring using randomized reduced rank reconstruction error. Frost, H. Robert (2023) "Reconstruction Set Test (RESET): a computationally efficient method for single sample gene set testing based on randomized reduced rank reconstruction error" <doi:10.1101/2023.04.03.535366>.

This package provides R-squared values and standardized regression coefficients for linear models applied to multiply imputed datasets as obtained by mice'. Confidence intervals, zero-order correlations, and alternative adjusted R-squared estimates are also available. The methods are described in Van Ginkel and Karch (2024) <doi:10.1111/bmsp.12344> and in Van Ginkel (2020) <doi:10.1007/s11336-020-09696-4>.

Turns regression models inside out. Functions decompose variances and coefficients for various regression model types. Functions also visualize regression model objects using techniques developed in Schoon, Melamed, and Breiger (2024) <doi:10.1017/9781108887205>.

Eurostat is the statistical office of the European Union and provides high quality statistics for Europe. Large set of the data is disseminated through the Eurostat database (<https://ec.europa.eu/eurostat/web/main/data/database>). The tools are using the REST API with the Statistical Data and Metadata eXchange (SDMX) Web Services (<https://ec.europa.eu/eurostat/web/user-guides/data-browser/api-data-access/api-detailed-guidelines/sdmx2-1>) to search and download data from the Eurostat database using the SDMX standard.