This package provides tools for large, sparse optimal matching of treated units and control units in observational studies. Provisions are made for refined covariate balance constraints, which include fine and near-fine balance as special cases. Matches are optimal in the sense that they are computed as solutions to network optimization problems rather than greedy algorithms. See Pimentel, et al.(2015) <doi:10.1080/01621459.2014.997879> and Pimentel (2016), Obs. Studies 2(1):4-23. The rrelaxiv package, which provides an alternative solver for the underlying network flow problems, carries an academic license and is not available on CRAN, but may be downloaded from Github at <https://github.com/josherrickson/rrelaxiv/>.

Flexible statistical modelling using a modular framework for regression, in which groups of transformations are composed together and act on probability distributions.

This package provides an easy way to report the results of ROC analysis, including: 1. an ROC curve. 2. the value of Cutoff, AUC (Area Under Curve), ACC (accuracy), SEN (sensitivity), SPE (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), PPV (positive predictive value), NPV (negative predictive value), PPA (percentage of positive accordance), NPA (percentage of negative accordance), TPA (percentage of total accordance), KAPPA (kappa value).

Randomization tests for the statistical comparison of i = two or more individual-based, sample-based or coverage-based rarefaction curves. The ecological null hypothesis is that the i samples were all drawn randomly from a single assemblage, with (necessarily) a single underlying species abundance distribution. The biogeographic null hypothesis is that the i samples were all drawn from different assemblages that, nonetheless, share similar species richness and species abundance distributions. Functions are described in L. Cayuela, N.J. Gotelli & R.K. Colwell (2015) <doi:10.1890/14-1261.1>.

Calculates tide heights based on tide station harmonics. It includes the harmonics data for 637 US stations. The harmonics data was converted from <https://github.com/poissonconsulting/rtide/blob/main/data-raw/harmonics-dwf-20151227-free.tar.bz2>, NOAA web site data processed by David Flater for XTide'. The code to calculate tide heights from the harmonics is based on XTide'.

Registers the COIN-OR Clp linear programming solver, through the coinclp package, with the R Optimization Infrastructure ('ROI'). Linear programs with continuous variables are then solved by ROI_solve(op, solver = "coinclp"), keeping sparse constraint matrices sparse all the way to the solver, and returning dual values, reduced costs and row activities alongside the primal solution.

Fully DBI'-compliant interface to Greenplum <https://greenplum.org/>, an open-source parallel database. This is an extension of the RPostgres package <https://github.com/r-dbi/RPostgres>.

Rcmdr Plugin for the FactoMineR package.

BEAST is a Bayesian estimator of abrupt change, seasonality, and trend for decomposing univariate time series and 1D sequential data. Interpretation of time series depends on model choice; different models can yield contrasting or contradicting estimates of patterns, trends, and mechanisms. BEAST alleviates this by abandoning the single-best-model paradigm and instead using Bayesian model averaging over many competing decompositions. It detects and characterizes abrupt changes (changepoints, breakpoints, structural breaks, joinpoints), cyclic or seasonal variation, and nonlinear trends. BEAST not only detects when changes occur but also quantifies how likely the changes are true. It estimates not just piecewise linear trends but also arbitrary nonlinear trends. BEAST is generically applicable to any real-valued time series, such as those from remote sensing, economics, climate science, ecology, hydrology, and other environmental and biological systems. Example applications include identifying regime shifts in ecological data, mapping forest disturbance and land degradation from satellite image time series, detecting market trends in economic indicators, pinpointing anomalies and extreme events in climate records, and analyzing system dynamics in biological time series. Details are given in Zhao et al. (2019) <doi:10.1016/j.rse.2019.04.034>.

Robust Clustering of Time Series (RCTS) has the functionality to cluster time series using both the classical and the robust interactive fixed effects framework. The classical framework is developed in Ando & Bai (2017) <doi:10.1080/01621459.2016.1195743>. The implementation within this package excludes the SCAD-penalty on the estimations of beta. This robust framework is developed in Boudt & Heyndels (2022) <doi:10.1016/j.ecosta.2022.01.002> and is made robust against different kinds of outliers. The algorithm iteratively updates beta (the coefficients of the observable variables), group membership, and the latent factors (which can be common and/or group-specific) along with their loadings. The number of groups and factors can be estimated if they are unknown.

The kappa statistic implemented by Fleiss is a very popular index for assessing the reliability of agreement among multiple observers. It is used both in the psychological and in the psychiatric field. Other fields of application are typically medicine, biology and engineering. Unfortunately,the kappa statistic may behave inconsistently in case of strong agreement between raters, since this index assumes lower values than it would have been expected. We propose a modification kappa implemented by Fleiss in case of nominal and ordinal variables. Monte Carlo simulations are used both to testing statistical hypotheses and to calculating percentile bootstrap confidence intervals based on proposed statistic in case of nominal and ordinal data.

Linguistic Descriptions of Complex Phenomena (LDCP) is an architecture and methodology that allows us to model complex phenomena, interpreting input data, and generating automatic text reports customized to the user needs (see <doi:10.1016/j.ins.2016.11.002> and <doi:10.1007/s00500-016-2430-5>). The proposed package contains a set of methods that facilitates the development of LDCP systems. It main goal is increasing the visibility and practical use of this research line.

Read, write and manipulate Praat TextGrid, PitchTier, Pitch, IntensityTier, Formant, Sound, and Collection files <https://www.fon.hum.uva.nl/praat/>.

SEA performs simultaneous feature-set testing for (gen)omics data. It tests the unified null hypothesis and controls the family-wise error rate for all possible pathways. The unified null hypothesis is defined as: "The proportion of true features in the set is less than or equal to a threshold." Family-wise error rate control is provided through use of closed testing with Simes test. There are some practical functions to play around with the pathways of interest.

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.

Mass rollup for a Bill of Materials is an example of a class of computations in which elements are arranged in a tree structure and some property of each element is a computed function of the corresponding values of its child elements. Leaf elements, i.e., those with no children, have values assigned. In many cases, the combining function is simple arithmetic sum; in other cases (e.g., mass properties), the combiner may involve other information such as the geometric relationship between parent and child, or statistical relations such as root-sum-of-squares (RSS). This package implements a general function for such problems. It is adapted to specific recursive computations by functional programming techniques; the caller passes a function as the update parameter to rollup() (or, at a lower level, passes functions as the get, set, combine, and override parameters to update_prop()) at runtime to specify the desired operations. The implementation relies on graph-theoretic algorithms from the igraph package of Csárdi, et al. (2006 <doi:10.5281/zenodo.7682609>).

This package provides quality checks for MEDITS (International Bottom Trawl Survey in the Mediterranean) trawl survey exchange data tables (TA (Haul data), TB (Catch data), TC (Biological data), TE (Biological individual data), TL (Litter data)). The main function RoME() calls all check functions in a defined sequence to perform a complete quality control of TX (Generic exchange data) data, including header validation, controlled-vocabulary checks, cross-table consistency tests, and biological plausibility checks. No automatic correction is applied: the package detects errors, warns the user, and specifies the type of error to ease data correction. Checks can be run simultaneously on multi-year datasets. An embedded shiny application is also provided via run_RoME_app(). References describing the methods: MEDITS Working Group (2017).

Read raw and processed data from acoustic ejection mass spectrometry (AEMS) files produced by the Sciex EchoMS instrument. Includes functions to create interactive reader objects, extract raw intensity measurements, mass spectra, and fully-processed mass-transition intensity areas. Methods for data processing and analysis are described in Rimmer et al. (2025) <doi:10.1021/acs.analchem.5c03730>. Supports both multiple reaction monitoring (MRM) and full-scan (neutral loss and precursor ion) data formats.

This package provides a full-featured RethinkDB <https://rethinkdb.com/> client for R; allows users to store JSON-serialised data in a robust, distributed no-SQL system, use rich queries and react to data changes in real-time.

This package provides functions to analyse Agricultural Production Systems sIMulator ('APSIM') Next Generation simulation outputs to support cultivar-level comparison under specified environments and management assumptions. The package focuses on transparent, reproducible summarisation of model outputs such as yield, phenology timing, and stress exposure, without performing APSIM simulations, interpreting user intent, or making normative recommendations.

An R Commander plug-in providing an integrated solution to perform a series of text mining tasks such as importing and cleaning a corpus, and analyses like terms and documents counts, vocabulary tables, terms co-occurrences and documents similarity measures, time series analysis, correspondence analysis and hierarchical clustering. Corpora can be imported from spreadsheet-like files, directories of raw text files, as well as from Dow Jones Factiva', LexisNexis', Europresse and Alceste files.

The Diceware method can be used to generate strong passphrases. In short, you roll a 6-faced dice 5 times in a row, the number obtained is matched against a dictionary of easily remembered words. By combining together 7 words thus generated, you obtain a password that is relatively easy to remember, but would take several millions years (on average) for a powerful computer to guess.

Computation of the International Roughness Index (IRI) given a longitudinal road profile. The IRI can be calculated for a single road segment or for a sequence of segments with a fixed length (e. g. 100m). For the latter, an overlap of the segments can be selected. The IRI and likewise the algorithms for its determination are defined in Sayers, Michael W; Gillespie, Thomas D; Queiroz, Cesar A.V. 1986. The International Road Roughness Experiment (IRRE) : establishing correlation and a calibration standard for measurements. World Bank technical paper; no. WTP 45. Washington, DC : The World Bank. (ISBN 0-8213-0589-1) available from <http://documents.worldbank.org/curated/en/326081468740204115>.

Slow Feature Analysis (SFA), ported to R based on matlab implementations of SFA: SFA toolkit 1.0 by Pietro Berkes and SFA toolkit 2.8 by Wolfgang Konen.