Casting metadata for REDCap database creation and handling of castellated data using repeated instruments and longitudinal projects in REDCap'. Keeps a focused data export approach, by allowing to only export required data from the database. Also for casting new REDCap databases based on datasets from other sources. Originally forked from the R part of REDCapRITS by Paul Egeler. See <https://github.com/pegeler/REDCapRITS>. REDCap (Research Electronic Data Capture) is a secure, web-based software platform designed to support data capture for research studies, providing 1) an intuitive interface for validated data capture; 2) audit trails for tracking data manipulation and export procedures; 3) automated export procedures for seamless data downloads to common statistical packages; and 4) procedures for data integration and interoperability with external sources (Harris et al (2009) <doi:10.1016/j.jbi.2008.08.010>; Harris et al (2019) <doi:10.1016/j.jbi.2019.103208>).

This package provides a straightforward, easy-to-use and robust parsing package which aims to digest history files from the popular messenger service WhatsApp in all locales and from all devices.

Implementation of an algorithm in two steps to estimate parameters of a model whose latent dynamics are inferred through latent processes, jointly regularized. This package uses Monolix software (<https://monolixsuite.slp-software.com/>), which provide robust statistical method for non-linear mixed effects modeling. Monolix must have been installed prior to use.

This package performs aggregation of ordered lists based on the ranks using several different algorithms: Cross-Entropy Monte Carlo algorithm, Genetic algorithm, and a brute force algorithm (for small problems).

The ropenblas package (<https://prdm0.github.io/ropenblas/>) is useful for users of any GNU/Linux distribution. It will be possible to download, compile and link the OpenBLAS library (<https://www.openblas.net/>) with the R language, always by the same procedure, regardless of the GNU/Linux distribution used. With the ropenblas package it is possible to download, compile and link the latest version of the OpenBLAS library even the repositories of the GNU/Linux distribution used do not include the latest versions of OpenBLAS'. If of interest, older versions of the OpenBLAS library may be considered. Linking R with an optimized version of BLAS (<https://netlib.org/blas/>) may improve the computational performance of R code. The OpenBLAS library is an optimized implementation of BLAS that can be easily linked to R with the ropenblas package.

Partitions the phenotypic variance of a plastic trait, studied through its reaction norm. The variance partition distinguishes between the variance arising from the average shape of the reaction norms (V_Plas) and the (additive) genetic variance . The latter is itself separated into an environment-blind component (V_G/V_A) and the component arising from plasticity (V_GxE/V_AxE). The package also provides a way to further partition V_Plas into aspects (slope/curvature) of the shape of the average reaction norm (pi-decomposition) and partition V_Add (gamma-decomposition) and V_AxE (iota-decomposition) into the impact of genetic variation in the reaction norm parameters. Reference: de Villemereuil & Chevin (2025) <doi:10.32942/X2NC8B>.

Recursive partitioning for least absolute deviation regression trees. Another algorithm from the 1984 book by Breiman, Friedman, Olshen and Stone in addition to the rpart package (Breiman, Friedman, Olshen, Stone (1984, ISBN:9780412048418).

Biologically relevant, yet mathematically sound constraints are used to compute the propensity and thence infer the dominant direction of reactions of a generic biochemical network. The reactions must be unique and their number must exceed that of the reactants,i.e., reactions >= reactants + 2. ReDirection', computes the null space of a user-defined stoichiometry matrix. The spanning non-zero and unique reaction vectors (RVs) are combinatorially summed to generate one or more subspaces recursively. Every reaction is represented as a sequence of identical components across all RVs of a particular subspace. The terms are evaluated with (biologically relevant bounds, linear maps, tests of convergence, descriptive statistics, vector norms) and the terms are classified into forward-, reverse- and equivalent-subsets. Since, these are mutually exclusive the probability of occurrence is binary (all, 1; none, 0). The combined propensity of a reaction is the p1-norm of the sub-propensities, i.e., sum of the products of the probability and maximum numeric value of a subset (least upper bound, greatest lower bound). This, if strictly positive is the probable rate constant, is used to infer dominant direction and annotate a reaction as "Forward (f)", "Reverse (b)" or "Equivalent (e)". The inherent computational complexity (NP-hard) per iteration suggests that a suitable value for the number of reactions is around 20. Three functions comprise ReDirection. These are check_matrix() and reaction_vector() which are internal, and calculate_reaction_vector() which is external.

An implementation of calls designed to collect and organize Mastodon data via its Application Program Interfaces (API), which can be found at the following URL: <https://docs.joinmastodon.org/>.

This package provides a Tidy implementation of grouping sets', rollup and cube - extensions of the group_by clause that allow for computing multiple group_by clauses in a single statement. For more detailed information on these functions, please refer to "Enhanced Aggregation, Cube, Grouping and Rollup" <https://cwiki.apache.org/confluence/display/Hive/Enhanced+Aggregation%2C+Cube%2C+Grouping+and+Rollup>.

Makes easier the creation of R package or research compendium (i.e. a predefined files/folders structure) so that users can focus on the code/analysis instead of wasting time organizing files. A full ready-to-work structure is set up with some additional features: version control, remote repository creation, CI/CD configuration (check package integrity under several OS, test code with testthat', and build and deploy website using pkgdown'). This package heavily relies on the R packages devtools and usethis and follows recommendations made by Wickham H. (2015) <ISBN:9781491910597> and Marwick B. et al. (2018) <doi:10.7287/peerj.preprints.3192v2>.

Retime speech signals with a native Waveform Similarity Overlap-Add (WSOLA) implementation translated from the TSM toolbox by Driedger & Müller (2014) <https://www.audiolabs-erlangen.de/content/resources/MIR/TSMtoolbox/2014_DriedgerMueller_TSM-Toolbox_DAFX.pdf>. Design retimings and pitch (f0) transformations with tidy data and apply them via Praat interface. Produce spectrograms, spectra, and amplitude envelopes. Includes implementation of vocalic speech envelope analysis (fft_spectrum) technique and example data (mm1) from Tilsen, S., & Johnson, K. (2008) <doi:10.1121/1.2947626>.

Allows the user to learn Bayesian networks from datasets containing thousands of variables. It focuses on score-based learning, mainly the BIC and the BDeu score functions. It provides state-of-the-art algorithms for the following tasks: (1) parent set identification - Mauro Scanagatta (2015) <http://papers.nips.cc/paper/5803-learning-bayesian-networks-with-thousands-of-variables>; (2) general structure optimization - Mauro Scanagatta (2018) <doi:10.1007/s10994-018-5701-9>, Mauro Scanagatta (2018) <http://proceedings.mlr.press/v73/scanagatta17a.html>; (3) bounded treewidth structure optimization - Mauro Scanagatta (2016) <http://papers.nips.cc/paper/6232-learning-treewidth-bounded-bayesian-networks-with-thousands-of-variables>; (4) structure learning on incomplete data sets - Mauro Scanagatta (2018) <doi:10.1016/j.ijar.2018.02.004>. Distributed under the LGPL-3 by IDSIA.

Convert REDCap exports into tidy tables for easy handling of REDCap repeat instruments and event arms.

Enhances the R Optimization Infrastructure ('ROI') package with the SCS solver for solving convex cone problems.

This package provides functions for the Bayesian analysis of extreme value models. The rust package <https://cran.r-project.org/package=rust> is used to simulate a random sample from the required posterior distribution. The functionality of revdbayes is similar to the evdbayes package <https://cran.r-project.org/package=evdbayes>, which uses Markov Chain Monte Carlo ('MCMC') methods for posterior simulation. In addition, there are functions for making inferences about the extremal index, using the models for threshold inter-exceedance times of Suveges and Davison (2010) <doi:10.1214/09-AOAS292> and Holesovsky and Fusek (2020) <doi:10.1007/s10687-020-00374-3>. Also provided are d,p,q,r functions for the Generalised Extreme Value ('GEV') and Generalised Pareto ('GP') distributions that deal appropriately with cases where the shape parameter is very close to zero.

Indices for assessing riverscape fragmentation, including the Dendritic Connectivity Index, the Population Connectivity Index, the River Fragmentation Index, the Probability of Connectivity, and the Integral Index of connectivity. For a review, see Jumani et al. (2020) <doi:10.1088/1748-9326/abcb37> and Baldan et al. (2022) <doi:10.1016/j.envsoft.2022.105470> Functions to calculate temporal indices improvement when fragmentation due to barriers is reduced are also included.

This package provides a tool designed to analyze recurrent events when dealing with right-censored data and the potential presence of a terminal event (that prevents further occurrences, like death). It extends the random survival forest algorithm, adapting splitting rules and node estimators to handle complexities of recurrent events. The methodology is fully described in Murris, J., Bouaziz, O., Jakubczak, M., Katsahian, S., & Lavenu, A. (2024) (<https://hal.science/hal-04612431v1/document>).

Downloads Southern Oscillation Index, Oceanic Nino Index, North Pacific Gyre Oscillation data, North Atlantic Oscillation and Arctic Oscillation. Data sources are described in the help files for each function.

Routines for developing models that describe reaction and advective-diffusive transport in one, two or three dimensions. Includes transport routines in porous media, in estuaries, and in bodies with variable shape.

The Resource Description Framework, or RDF is a widely used data representation model that forms the cornerstone of the Semantic Web. RDF represents data as a graph rather than the familiar data table or rectangle of relational databases. The rdflib package provides a friendly and concise user interface for performing common tasks on RDF data, such as reading, writing and converting between the various serializations of RDF data, including rdfxml', turtle', nquads', ntriples', and json-ld'; creating new RDF graphs, and performing graph queries using SPARQL'. This package wraps the low level redland R package which provides direct bindings to the redland C library. Additionally, the package supports the newer and more developer friendly JSON-LD format through the jsonld package. The package interface takes inspiration from the Python rdflib library.

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 provides a simple R -> Stata interface allowing the user to execute Stata commands (both inline and from a .do file) from R.

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.