Analytical computation of rolling and expanding Shapley values for time-series data. The rollshap package decomposes the coefficient of determination (R-squared) of a linear regression into nonnegative contributions from each explanatory variable using the Shapley value from cooperative game theory (Shapley, 1953, <doi:10.1515/9781400881970-018>). For each window, the exact Shapley value is computed by fitting all subsets of the explanatory variables and averaging the marginal contribution to R-squared across all orderings, which returns an order-invariant attribution that sums to the full-model R-squared. Use cases include variable importance, factor attribution, and feature selection in time-series regression. The package supports rolling and expanding windows, weights, and handling of missing values via min_obs', complete_obs', and na_restore arguments. The implementation uses the online and offline algorithms from the roll package to compute rolling and expanding cross-products efficiently with parallelism across columns and windows provided by RcppParallel'.

Rapid realistic routing on multimodal transport networks (walk, bike, public transport and car) using R5', the Rapid Realistic Routing on Real-world and Reimagined networks engine <https://github.com/conveyal/r5>. The package allows users to generate detailed routing analysis or calculate travel time and monetary cost matrices using seamless parallel computing on top of the R5 Java machine. While R5 is developed by Conveyal, the package r5r is independently developed by a team at the Institute for Applied Economic Research (Ipea) with contributions from collaborators. Apart from the documentation in this package, users will find additional information on R5 documentation at <https://docs.conveyal.com/>. Although we try to keep new releases of r5r in synchrony with R5, the development of R5 follows Conveyal's independent update process. Hence, users should confirm the R5 version implied by the Conveyal user manual (see <https://docs.conveyal.com/changelog>) corresponds with the R5 version that r5r depends on. This version of r5r depends on R5 v7.1.

This package provides a bagging predictor based on generalized linear models (GLMs) is implemented. The method is published in Song, Langfelder and Horvath (2013) <doi:10.1186/1471-2105-14-5>.

Bundles the duckhts DuckDB extension for reading High Throughput Sequencing file formats with DuckDB'. The DuckDB C extension API <https://duckdb.org/docs/stable/clients/c/api> and its htslib dependency are compiled from vendored sources during package installation. James K Bonfield and co-authors (2021) <doi:10.1093/gigascience/giab007>. VariantKey / RegionKey support follows Nicola Asuni (2018) <doi:10.1101/473744>.

This package provides a method generate() is implemented in this package for the random generation of vector time series according to models obtained by RMAWGEN', vars or other packages. This package was created to generalize the algorithms of the RMAWGEN package for the analysis and generation of any environmental vector time series.

Polynomially bounded algorithms to aggregate complete rankings under Kemeny's axiomatic framework. RankAggSIgFUR (pronounced as rank-agg-cipher) contains two heuristics algorithms: FUR and SIgFUR. For details, please see Badal and Das (2018) <doi:10.1016/j.cor.2018.06.007>.

This package provides tools for building brick-proof, reproducible, self-contained data capsules. Resolves open-data sources through the Comprehensive Knowledge Archive Network ('CKAN', <https://ckan.org/>) package_show and package_search endpoints, records and verifies provenance with Secure Hash Algorithm 256 ('SHA-256') digests and Internet Archive Wayback Machine (<https://web.archive.org/>) snapshots, validates downloaded data against a pinned schema, and falls back to schema-driven synthetic data when the real source is unreachable. Run records are captured in a manifest plus a plain-language summary so any result can be traced back to its inputs. Distributional drift between a pinned capsule and a fresh fetch is tested with Kolmogorov-Smirnov, chi-square, population stability index, Jensen-Shannon divergence and Benford first-digit screens, because a re-released extract can be statistically identical yet differ byte-for-byte, and a column can keep its name and type while having been silently rescaled. Manifests can be authenticated rather than only checksum-verified, with keyed digests ('HMAC-SHA-256', RFC 2104) or post-quantum hash-based signatures ('Winternitz one-time signatures under a Merkle tree, RFC 8391), and pinned chunk-wise through a Merkle tree so a mismatch identifies which part of a capsule moved. Also ships a compiled C++ core (summary, robust and rank statistics, SHA-256', SHA-512 and CRC-32') that sibling packages in the rmorie ecosystem reach through LinkingTo for a single, shared numeric and provenance-hashing backend. For the published administrative tables these capsules usually hold, it computes period-over-period change matched on the period rather than the row, with the exact conditional-binomial interval for a ratio of counts and with a percentage-point reading kept distinct from a percent change, rendered to Hypertext Markup Language ('HTML'), Portable Document Format ('PDF'), delimited text, JavaScript Object Notation ('JSON') or Markdown. Interval categories such as "2 to 5" or "50+" are parsed to bounds and the dependence of any derived figure on the open top band is measured rather than assumed. Concentration is summarised by the Gini coefficient, the Lorenz curve and tail-index estimation by exact discrete maximum likelihood; trend in a series of a few periods by the Mann-Kendall test with Theil-Sen slopes, a permutation step-change scan and Poisson rate ratios; and region-coded counts by indirect standardisation, exact standardised incidence ratios, the empirical Bayes shrinkage of Clayton and Kaldor (1987) <doi:10.2307/2532003>, funnel-plot limits and Moran's I.

Calculate RNNI distance between and manipulate with ranked trees. RNNI stands for Ranked Nearest Neighbour Interchange and is an extension of the classical NNI space (space of trees created by the NNI moves) to ranked trees, where internal nodes are ordered according to their heights (usually assumed to be times). The RNNI distance takes the tree topology into account, as standard NNI does, but also penalizes changes in the order of internal nodes, i.e. changes in the order of times of evolutionary events. For more information about the RNNI space see: Gavryushkin et al. (2018) <doi:10.1007/s00285-017-1167-9>, Collienne & Gavryushkin (2021) <doi:10.1007/s00285-021-01567-5>, Collienne et al. (2021) <doi:10.1007/s00285-021-01685-0>, and Collienne (2021) <http://hdl.handle.net/10523/12606>.

Perform mediation analysis via the fast-and-robust bootstrap test ROBMED (Alfons, Ates & Groenen, 2022a; <doi:10.1177/1094428121999096>), as well as various other methods. Details on the implementation and code examples can be found in Alfons, Ates, and Groenen (2022b) <doi:10.18637/jss.v103.i13>. Further discussion on robust mediation analysis can be found in Alfons & Schley (2025) <doi:10.1002/wics.70051>.

External jars required for package RWeka'.

Providing wrapper functions to implement Bayesian analysis in JAGS. Some major features include monitoring convergence of a MCMC model using Rubin and Gelman Rhat statistics, automatically running a MCMC model till it converges, and implementing parallel processing of a MCMC model for multiple chains.

This package provides a random-effects stochastic model that allows quick detection of clonal dominance events from clonal tracking data collected in gene therapy studies. Starting from the Ito-type equation describing the dynamics of cells duplication, death and differentiation at clonal level, we first considered its local linear approximation as the base model. The parameters of the base model, which are inferred using a maximum likelihood approach, are assumed to be shared across the clones. Although this assumption makes inference easier, in some cases it can be too restrictive and does not take into account possible scenarios of clonal dominance. Therefore we extended the base model by introducing random effects for the clones. In this extended formulation the dynamic parameters are estimated using a tailor-made expectation maximization algorithm. Further details on the methods can be found in L. Del Core et al., (2022) <doi:10.1101/2022.05.31.494100>.

Verified interval arithmetic for R, in the inf-sup (endpoint) representation of the set-based flavor of the interval standard. Every operation returns an enclosure that provably contains the exact result: outward rounding is obtained from the predecessor and successor formulas of Rump, Zimmermann, Boldo and Melquiond (2009) <doi:10.1007/s10543-009-0218-z>, which are valid under round-to-nearest and therefore need no change to the floating-point rounding mode. That mode is not reachable from R, and changing it would not be a local act: it is per-thread state of the processor, so it would govern every floating-point operation executed afterwards on that thread, in this package or anywhere else. Elementary functions are provided at two levels: a fast level over the included correctly rounded binary64 implementation, comprising fifteen kernels from CORE-MATH <doi:10.1109/ARITH54963.2022.00014> and the hardware square root, widened by the pre-registered slack of two outward steps; and a rigorous level over Rmpfr with a directed-rounding bridge, reached by an escalation ladder of precisions when a verdict would otherwise fall inside the slack. Fast-level enclosures retain measured provenance because correct rounding of the included software is verified numerically rather than established here as a theorem for every kernel. On top of the kernel the package builds natural and centered interval extensions of expressions, a monotonicity test, the Hansen-Sengupta interval Newton operator with extended division and epsilon-inflated candidate verification, and a subdivision (paving) engine whose only failure mode is a named abstention with its budget printed. Conformance with IEEE Std 1788.1-2017 <doi:10.1109/IEEESTD.2018.8277144> is not claimed, and the reason is the standard's own: its subclause 1.5 makes conformance a list of requirements that an implementation shall satisfy, with no partial grade to claim. What this package follows, measured one requirement at a time and stated in the package documentation, is the interval type and the decoration system of clause 5, 22 of the 39 arithmetic operations of Table 4.1, and the seven numeric functions of Table 4.3. What it does not provide is the cancellative operations, the interval comparison relations, the text input and output of subclause 6.8, the interchange representation of subclause 7.3, and the tightest accuracy that subclause 6.5.2 requires of the basic operations, which here are one unit in the last place wider at each end.

This package provides a Bayesian credible interval is interpreted with respect to posterior probability, and this interpretation is far more intuitive than that of a frequentist confidence interval. However, standard highest-density intervals can be wide due to between-subjects variability and tends to hide within-subject effects, rendering its relationship with the Bayes factor less clear in within-subject (repeated-measures) designs. This urgent issue can be addressed by using within-subject intervals in within-subject designs, which integrate four methods including the Wei-Nathoo-Masson (2023) <doi:10.3758/s13423-023-02295-1>, the Loftus-Masson (1994) <doi:10.3758/BF03210951>, the Nathoo-Kilshaw-Masson (2018) <doi:10.1016/j.jmp.2018.07.005>, and the Heck (2019) <doi:10.31234/osf.io/whp8t> interval estimates.

We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.

This package performs random projection using Johnson-Lindenstrauss (JL) Lemma (see William B.Johnson and Joram Lindenstrauss (1984) <doi:10.1090/conm/026/737400>). Random Projection is a dimension reduction technique, where the data in the high dimensional space is projected into the low dimensional space using JL transform. The original high dimensional data matrix is multiplied with the low dimensional projection matrix which results in reduced matrix. The projection matrix can be generated using the projection function that is independent to the original data. Then finally apply the classification task on the projected data.

This package provides a toolkit for Commodities analytics', risk management and trading professionals. Includes functions for API calls to <https://www.zema.global/platforms/zema-marketplace>, <https://developer.genscape.com/>, and <https://www.bankofcanada.ca/valet/docs>.

Administrative regions and other spatial objects of the Czech Republic.

This is a port of Jonathan Shewchuk's Triangle library to R. From his description: "Triangle generates exact Delaunay triangulations, constrained Delaunay triangulations, conforming Delaunay triangulations, Voronoi diagrams, and high-quality triangular meshes. The latter can be generated with no small or large angles, and are thus suitable for finite element analysis.".

*The package is deprecated. It uses the standard drivers on R >= 4.6.0 since they incorporate all the functionalities below.* Weave and tangle drivers for Sweave extending the standard drivers. RweaveExtraLatex and RtangleExtra provide options to completely ignore code chunks on weaving, tangling, or both. Chunks ignored on weaving are not parsed, yet are written out verbatim on tangling. Chunks ignored on tangling may be evaluated as usual on weaving, but are completely left out of the tangled scripts. The driver RtangleExtra also provides options to control the separation between code chunks in the tangled script, and to specify the extension of the file name (or remove it entirely) when splitting is selected.

This package provides a client for the public API of data.gouv.fr, the French government's open data platform. It helps you find a dataset that matches your interests, judge whether it is usable, download it, and re-fetch the exact same table later in a reproducible way. You can search the catalog and filter by producer or theme (dg_find_datasets(), dg_find_organization(), dg_find_topics()), pull a dataset's tabular resources into tidy tibbles (dg_pull_dataset()), inspect the documented variables of its data schema (dg_schema()), and compute summary metrics such as size, number of columns and missing-value rate (dg_summary(), dg_summarise()). Each returned table carries a stable identifier (dg_table_id(), dg_refetch()) so it can be re-fetched later. Requests are built on top of httr2'.

Builds Camera Trap Data Packages ('Camtrap DP') from arbitrary spreadsheets in a schema-driven way: table structure, types, constraints and relations are read from the Frictionless table schemas of the requested Camtrap DP version, so any version and custom columns are handled automatically. Provides validation against the schemas and an optional bridge to the frictionless Python framework. The Camtrap DP standard is described in Bubnicki et al. (2023) <doi:10.1002/rse2.374>.

Interface around JDemetra+ (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially recommended to the members of the European Statistical System (ESS) and the European System of Central Banks. It offers full access to all options and outputs of JDemetra+', including the two leading seasonal adjustment methods TRAMO/SEATS+ and X-12ARIMA/X-13ARIMA-SEATS.

This package provides a wrapper for running the bundled Open-WBO Maximum Satisfiability (MaxSAT) solver (<https://github.com/sat-group/open-wbo>). Users can pass command-line arguments to the solver and capture its output as a character string or file.