Supports the full analysis pipeline for researchers working with firm-level microdata. Provides data tools for panel preparation (import, outlier detection, classification harmonization), analytical methods (production function estimation, capital stock measurement, markups, intensity measures, distributions, regression, clustering), and disclosure tools for tagging outputs with dominance and observation counts before aggregation and publication. Production function estimation implements methods by Ackerberg, Caves and Frazer (2015) <doi:10.3982/ECTA13408>, Levinsohn and Petrin (2003) <doi:10.1111/1467-937X.00246>, Wooldridge (2009) <doi:10.1016/j.econlet.2009.04.026>, Petrin, Poi and Levinsohn (2004) <doi:10.1177/1536867X0400400202>, and Arellano and Bond (1991) <doi:10.2307/2297968> with the "too many instruments" correction by Roodman (2009) <doi:10.1111/j.1468-0084.2008.00542.x>. Markup estimation follows De Loecker and Warzynski (2012) <doi:10.1257/aer.102.6.2437>. Cost-share production function estimation follows Basu and Fernald (1997) <doi:10.1086/262073>. Capital stock estimation via the Perpetual Inventory Method follows OECD (2009) <doi:10.1787/9789264068476-en>.

Record linkage for public health surveillance datasets using either the Fellegi-Sunter probabilistic framework (via reclin2) or deterministic exact-key matching. Provides pre-linkage data quality auditing (preflight()), Medicare number checksum validation (check_medicare()), blocking variable construction (flock()), and the murmuration() linkage engine. murmuration() expresses linkage as a single cohort-to-event concept (linking a linelist of people to a dated stream of vaccination, hospitalisation, or case events within a before/during/after time window), with the historical four-code taxonomy (case-to-hospitalisation, vaccination-to-case, vaccination-to-hospitalisation, vaccination-to-event) retained as deprecated aliases. Also provides linkage weight distribution visualisation (murmuration_plot()) and threshold sensitivity analysis (perch()) with annotated AIHW, WA Data Linkage Unit, and PHRN reference benchmarks. perch() can be called standalone or triggered automatically mid-linkage via murmuration(perch_before_linking = TRUE). Includes synthetic datasets (cases_notifiable, vax_air) and three vignettes demonstrating the complete linkage workflow. Part of the aviary public health surveillance ecosystem.

Splines are efficiently represented through their Taylor expansion at the knots. The representation accounts for the support sets and is thus suitable for sparse functional data. Two cases of boundary conditions are considered: zero-boundary or periodic-boundary for all derivatives except the last. The periodical splines are represented graphically using polar coordinates. The B-splines and orthogonal bases of splines that reside on small total support are implemented. The orthogonal bases are referred to as splinets and are utilized for functional data analysis. Random spline generator is implemented as well as all fundamental algebraic and calculus operations on splines. The optimal, in the least square sense, functional fit by splinets to data consisting of sampled values of functions as well as splines build over another set of knots is obtained and used for functional data analysis. The S4-version of the object oriented R is used. <doi:10.48550/arXiv.2102.00733>, <doi:10.1016/j.cam.2022.114444>, <doi:10.48550/arXiv.2302.07552>.

This package provides tools to import, clean, validate, and analyze freshwater quality data in Brazil. Implements water quality indices including the Water Quality Index ('WQI'/'IQA') using the weighted geometric mean following CETESB methodology, the Trophic State Index ('TSI'/'IET') after Carlson (1977) <doi:10.4319/lo.1977.22.2.0361> and Lamparelli (2004) <https://teses.usp.br/teses/disponiveis/41/41134/tde-20032006-075813/publico/TeseLamparelli2004.pdf>, and the National Sanitation Foundation Water Quality Index ('NSF WQI', Brown (1970)). The package also checks compliance with Brazilian standard CONAMA Resolution 357/2005 <https://conama.mma.gov.br/?id=450&option=com_sisconama&task=arquivo.download> including the legal frequency rule (Art. 15, 80% conformity over six or more samples per year), and provides seasonal analysis with regional flow-season calendars, pollutant load computation, exceedance probability estimation, IET visualization, and multivariate PCA tools for routine monitoring workflows. The example dataset ('wq_demo') is a real subset from INEMA monitoring data from a river in Bahia, Brazil (2020-2024).

The theory of cooperative games with transferable utility offers useful insights into the way parties can share gains from cooperation and secure sustainable agreements, see e.g. one of the books by Chakravarty, Mitra and Sarkar (2015, ISBN:978-1107058798) or by Driessen (1988, ISBN:978-9027727299) for more details. A comprehensive set of tools for cooperative game theory with transferable utility is provided. Users can create special families of cooperative games, like e.g. bankruptcy games, cost sharing games and weighted voting games. There are functions to check various game properties and to compute five different set-valued solution concepts for cooperative games. A large number of point-valued solution concepts is available reflecting the diverse application areas of cooperative game theory. Some of these point-valued solution concepts can be used to analyze weighted voting games and measure the influence of individual voters within a voting body. There are routines for visualizing both set-valued and point-valued solutions in the case of three or four players.

Estimation and inference for coefficients of linear EIV models with symmetric measurement errors. The measurement errors can be homoscedastic or heteroscedastic, for the latter, replication for at least some observations needs to be available. The estimation method and asymptotic inference are based on a generalised method of moments framework, where the estimating equations are formed from (1) minimising the distance between the empirical phase function (normalised characteristic function) of the response and that of the linear combination of all the covariates at the estimates, and (2) minimising a corrected least-square discrepancy function. Specifically, for a linear EIV model with p error-prone and q error-free covariates, if replicates are available, the GMM approach is based on a 2(p+q) estimating equations if some replicates are available and based on p+2q estimating equations if no replicate is available. The details of the method are described in Nghiem and Potgieter (2020) <doi:10.1093/biomet/asaa025> and Nghiem and Potgieter (2025) <doi:10.5705/ss.202022.0331>.

This package provides a design-based approach to statistical inference, with a focus on spatial data. Spatially balanced samples are selected using the Generalized Random Tessellation Stratified (GRTS) algorithm. The GRTS algorithm can be applied to finite resources (point geometries) and infinite resources (linear / linestring and areal / polygon geometries) and flexibly accommodates a diverse set of sampling design features, including stratification, unequal inclusion probabilities, proportional (to size) inclusion probabilities, legacy (historical) sites, a minimum distance between sites, and two options for replacement sites (reverse hierarchical order and nearest neighbor). Data are analyzed using a wide range of analysis functions that perform categorical variable analysis, continuous variable analysis, attributable risk analysis, risk difference analysis, relative risk analysis, change analysis, and trend analysis. spsurvey can also be used to summarize objects, visualize objects, select samples that are not spatially balanced, select panel samples, measure the amount of spatial balance in a sample, adjust design weights, and more. For additional details, see Dumelle et al. (2023) <doi:10.18637/jss.v105.i03>.

This package provides a unified and reproducible framework for statistical inference of spatiotemporal trends in gridded environmental data. The framework addresses the interconnected challenges of serial correlation, spatial dependence and multiple testing that commonly arise when analysing gridded environmental time series. Its core methods support serial-correlation treatment through trend-preserving prewhitening, pixel-wise and spatially explicit trend inference, slope estimation and multiple-testing correction. These methods may be applied independently or integrated within configurable analytical workflows. Dedicated workflows are also provided to reproduce methodologies published in the scientific literature: Gutiérrez-Hernández and Garcà a (2025) <doi:10.1016/j.rsase.2024.101377> for the True Significant Trends workflow, Gutiérrez-Hernández and Garcà a (2024) <doi:10.3390/rs16203886> for the Robust Trend Analysis workflow, and Gutiérrez-Hernández and Garcà a (2025) <doi:10.3390/math13223630> for the adaptive false discovery rate procedure. Supporting utilities facilitate raster data import and inspection, anomaly calculation, spatial autocorrelation diagnostics, simulation studies, benchmarking, visualisation, mapping, and reporting.

Searches configurational data for causes that are each an insufficient but non-redundant part of an unnecessary but sufficient (INUS) condition for their effect, so that cause-effect relations are marked by conjunctivity and disjunctivity. The method, Combinational Regularity Analysis (CORA), borrows its Boolean minimisation algorithms from switching circuit analysis. Truth tables are minimised either with the classical Quine-McCluskey algorithm over positive and don't care terms or with McCluskey's modified algorithm over positive and negative terms, and the resulting prime implicant charts are solved with Petrick's method. Multi-value conditions and structures with simple as well as complex effects are supported, together with a configurational data-mining search and two-level logic diagrams. The package is an R port of the Python packages CORA and LOGIGRAM described in Sebechlebská, Mkrtchyan and Thiem (2023) <doi:10.21105/joss.05019>; it computes in plain R and requires no Python installation. It is an independent implementation and is not endorsed by the authors of the original packages.

This package implements measures of tree similarity, including information-based generalized Robinson-Foulds distances (Phylogenetic Information Distance, Clustering Information Distance, Matching Split Information Distance; Smith 2020) <doi:10.1093/bioinformatics/btaa614>; Jaccard-Robinson-Foulds distances (Bocker et al. 2013) <doi:10.1007/978-3-642-40453-5_13>, including the Nye et al. (2006) metric <doi:10.1093/bioinformatics/bti720>; the Matching Split Distance (Bogdanowicz & Giaro 2012) <doi:10.1109/TCBB.2011.48>; the Hierarchical Mutual Information (Perotti et al. 2015) <doi:10.1103/PhysRevE.92.062825>; Maximum Agreement Subtree distances; the Kendall-Colijn (2016) distance <doi:10.1093/molbev/msw124>, and the Nearest Neighbour Interchange (NNI) distance, approximated per Li et al. (1996) <doi:10.1007/3-540-61332-3_168>. Includes tools for visualizing mappings of tree space (Smith 2022) <doi:10.1093/sysbio/syab100>, for identifying islands of trees (Silva and Wilkinson 2021) <doi:10.1093/sysbio/syab015>, for calculating the median of sets of trees, and for computing the information content of trees and splits.

Seahtrue organizes oxygen consumption and extracellular acidification analysis data from experiments performed on an XF analyzer into structured nested tibbles.This allows for detailed processing of raw data and advanced data visualization and statistics. Seahtrue introduces an open and reproducible way to analyze these XF experiments. It uses file paths to .xlsx files. These .xlsx files are supplied by the userand are generated by the user in the Wave software from Agilent from the assay result files (.asyr). The .xlsx file contains different sheets of important data for the experiment; 1. Assay Information - Details about how the experiment was set up. 2. Rate Data - Information about the OCR and ECAR rates. 3. Raw Data - The original raw data collected during the experiment. 4. Calibration Data - Data related to calibrating the instrument. Seahtrue focuses on getting the specific data needed for analysis. Once this data is extracted, it is prepared for calculations through preprocessing. To make sure everything is accurate, both the initial data and the preprocessed data go through thorough checks.

This package provides a series of R functions that come in handy while working with metabarcoding data. The reasoning of doing this is to have the same functions we use all the time stored in a curated, reproducible way. In a way it is all about putting together the grammar of the tidyverse from Wickham et al.(2019) <doi:10.21105/joss.01686> with the functions we have used in community ecology compiled in packages like vegan from Dixon (2003) <doi:10.1111/j.1654-1103.2003.tb02228.x> and phyloseq McMurdie & Holmes (2013) <doi:10.1371/journal.pone.0061217>. The package includes functions to read sequences from FAST(A/Q) into a tibble ('fasta_reader and fastq_reader'), to process cutadapt Martin (2011) <doi:10.14806/ej.17.1.200> info-file output. When it comes to sequence counts across samples, the package works with the long format in mind (a three column tibble with Sample, Sequence and counts ), with functions to move from there to the wider format.

This package provides a toolkit for common statistical analyses including descriptive statistics, Student's t-tests (one-sample, independent, and paired), one-way and two-way Analysis of Variance (ANOVA), Multivariate Analysis of Variance (MANOVA), chi-square tests, Fisher's Exact Test, McNemar's Test, correlation analysis, simple and multiple linear regression, logistic regression, Friedman Test, and non-parametric tests (Mann-Whitney U, Wilcoxon Signed Rank, and Kruskal-Wallis). Each function automatically interprets results in plain English, reporting effect sizes, confidence intervals, and p-value interpretations, and prints relevant assumption checks by default. A context argument allows users to describe their study design, echoed back alongside the interpretation as a reminder to read results in that context. Post-hoc tests are automatically applied following significant results. A master function automatically detects the appropriate test based on the structure of the input data. Methods are based on Cohen, J. (1988) <doi:10.4324/9780203771587>, Tukey, J. W. (1949) <doi:10.2307/3001913>, and Shapiro and Wilk (1965) <doi:10.2307/2333709>.

The EMDomics algorithm is used to perform a supervised multi-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between groups. Usually the data will be gene expression values from array-based or sequence-based experiments, but data from other types of experiments can also be analyzed (e.g. copy number variation). Traditional methods like Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA) use significance tests based on summary statistics (mean and standard deviation) of the distributions. This approach lacks power to identify expression differences between groups that show high levels of intra-group heterogeneity. The Earth Mover's Distance (EMD) algorithm instead computes the "work" needed to transform one distribution into another, thus providing a metric of the overall difference in shape between two distributions. Permutation of sample labels is used to generate q-values for the observed EMD scores. This package also incorporates the Komolgorov-Smirnov (K-S) test and the Cramer von Mises test (CVM), which are both common distribution comparison tests.

This package provides useful tools for cognitive diagnosis modeling (CDM). The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, <doi:10.1111/bmsp.12228>) and the discrete factor loading method (Wang, Song, & Ding, 2018, <doi:10.1007/978-3-319-77249-3_29>). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, <doi:10.3389/fpsyg.2021.614470>). Other relevant methods and features for CDM applications, such as the restricted DINA model (Nájera et al., 2023; <doi:10.3102/10769986231158829>), the general nonparametric classification method (Chiu et al., 2018; <doi:10.1007/s11336-017-9595-4>), and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022; <doi:10.3758/s13428-022-01967-5>) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices.

Offers tools for visualizing and analyzing size and power properties of tests for equal predictive accuracy, including Diebold-Mariano and related procedures. Provides multiple Diebold-Mariano test implementations based on fixed-smoothing approaches, including fixed-b methods such as Kiefer and Vogelsang (2005) <doi:10.1017/S0266466605050565>, and applications to tests for equal predictive accuracy as in Coroneo and Iacone (2020) <doi:10.1002/jae.2756>, alongside conventional large-sample approximations. HAR inference involves nonparametric estimation of the long-run variance, and a key tuning parameter (the truncation parameter) trades off size and power. Lazarus, Lewis, and Stock (2021) <doi:10.3982/ECTA15404> theoretically characterize the size-power frontier for the Gaussian multivariate location model. ForeComp computes and visualizes the finite-sample size-power frontier of the Diebold-Mariano test based on fixed-b asymptotics together with the Bartlett kernel. To compute finite-sample size and power, it fits a best approximating ARMA process to the input data and reports how the truncation parameter performs and how robust testing outcomes are to its choice.

Detects and suggests fixes for semantic inconsistencies in data frames by calling large language models (LLMs) through a unified, provider-agnostic interface. Supported providers include OpenAI ('GPT-4o', GPT-4o-mini') <https://platform.openai.com>, Anthropic ('Claude') <https://www.anthropic.com>, Google ('Gemini') <https://ai.google.dev>, Groq (free-tier LLaMA and Mixtral') <https://groq.com>, and local Ollama models <https://ollama.com>. The package identifies issues that rule-based tools cannot detect: abbreviation variants, typographic errors, case inconsistencies, and malformed values. Results are returned as tidy data frames with column, row index, detected value, issue type, suggested fix, and confidence score. An offline fallback using statistical and fuzzy-matching methods is provided for use without any application programming interface (API) key. Interactive fix application with human review is supported via apply_fixes()'. Methods follow de Jonge and van der Loo (2013) <https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf> and Chaudhuri et al. (2003) <doi:10.1145/872757.872796>.

This package provides a suite of functions that fit models that use PPM type priors for partitions. Models include hierarchical Gaussian and probit ordinal models with a (covariate dependent) PPM. If a covariate dependent product partition model is selected, then all the options detailed in Page, G.L.; Quintana, F.A. (2018) <doi:10.1007/s11222-017-9777-z> are available. If covariate values are missing, then the approach detailed in Page, G.L.; Quintana, F.A.; Mueller, P (2020) <doi:10.1080/10618600.2021.1999824> is employed. Also included in the package is a function that fits a Gaussian likelihood spatial product partition model that is detailed in Page, G.L.; Quintana, F.A. (2016) <doi:10.1214/15-BA971>, and multivariate PPM change point models that are detailed in Quinlan, J.J.; Page, G.L.; Castro, L.M. (2023) <doi:10.1214/22-BA1344>. In addition, a function that fits a univariate or bivariate functional data model that employs a PPM or a PPMx to cluster curves based on B-spline coefficients is provided.

This package provides an alternative algorithm for ARIMA and seasonal ARIMA model identification based on Cartesian products of user-supplied parameter sets. Rather than relying on ACF/PACF plots or stepwise search (as in auto.arima()), the package exhaustively evaluates every candidate (p,d,q)(P,D,Q)[m] combination in the requested index sets, ranks all converged models by AIC, AICc, BIC, and HQIC simultaneously, computes Akaike weights for model uncertainty quantification, supports exogenous regressors, produces ensemble forecasts, evaluates candidate models by rolling-origin (expanding window) cross-validation, and provides publication-quality diagnostic and comparison plots. A feature-based exploratory data analysis suite computes scale-free time series characteristics (trend and seasonal strength, spectral entropy, autocorrelation, lumpiness, stability) in the spirit of Hyndman, Wang and Laptev (2015), and a feature-guided automatic search narrows the Cartesian product model space before the exhaustive search runs. The algorithm is flexible, transparent, and widely applicable for quick, reproducible ARIMA model selection in both academic research and industry forecasting pipelines. Applications are demonstrated with Nigerian macroeconomic time series data.

This package provides a colour-first toolkit for the statistical analysis of induced mutagenesis experiments in crop plants. It fits dose-response models to physical and chemical mutagen data and estimates the median lethal and growth-reduction doses (LD50, GR50) with confidence intervals obtained from Fieller's theorem; quantifies first-generation biological damage (lethality, injury and pollen sterility); and estimates mutagenic effectiveness and mutagenic efficiency. Effectiveness and efficiency are conventionally reported as point estimates only; this package treats them as functions of binomial proportions and supplies interval estimates by the delta method on the logarithmic scale and by the nonparametric bootstrap. It further provides chlorophyll mutation spectrum analysis with tests of homogeneity and diversity, generalised linear models for second-generation mutant counts with formal assessment of overdispersion, and formal comparison of mutagens including relative biological effectiveness. Every analysis returns a tidy result object and a publication-ready ggplot2 figure. Methods follow Konzak et al. (1965, ISBN:9789201150653), Fieller (1954) <doi:10.1111/j.2517-6161.1954.tb00159.x> and Katz et al. (1978) <doi:10.2307/2530610>.

This package provides functions for fitting discrete distribution models to count data. Included are the Poisson, the negative binomial, the Poisson-inverse gaussian and, most importantly, a new implementation of the Poisson-beta distribution (density, distribution and quantile functions, and random number generator) together with a needed new implementation of Kummer's function (also: confluent hypergeometric function of the first kind). Three different implementations of the Gillespie algorithm allow data simulation based on the basic, switching or bursting mRNA generating processes. Moreover, likelihood functions for four variants of each of the three aforementioned distributions are also available. The variants include one population and two population mixtures, both with and without zero-inflation. The package depends on the MPFR libraries (<https://www.mpfr.org/>) which need to be installed separately (see description at <https://github.com/fuchslab/scModels>). This package is supplement to the paper "A mechanistic model for the negative binomial distribution of single-cell mRNA counts" by Lisa Amrhein, Kumar Harsha and Christiane Fuchs (2019) <doi:10.1101/657619> available on bioRxiv.

This package provides a comprehensive toolset for any useR conducting topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. The tools this package currently provides can be conveniently split into three main sections: (1) calculating persistent homology; (2) conducting statistical inference on persistent homology calculations; (3) visualizing persistent homology and statistical inference. The published form of TDAstats can be found in Wadhwa et al. (2018) <doi:10.21105/joss.00860>. For a general background on computing persistent homology for topological data analysis, see Otter et al. (2017) <doi:10.1140/epjds/s13688-017-0109-5>. To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read Robinson & Turner (2017) <doi:10.1007/s41468-017-0008-7>. To learn more about how TDAstats calculates persistent homology, you can visit the GitHub repository for Ripser, the software that works behind the scenes at <https://github.com/Ripser/ripser>. This package has been published as Wadhwa et al. (2018) <doi:10.21105/joss.00860>.

The futurize() function turns sequential map-reduce functions such as base::lapply(), purrr::map(), foreach::foreach() %do% ... into concurrent alternatives, providing you with a simple, straightforward path to scalable parallel computing via the future ecosystem <doi:10.32614/RJ-2021-048>. By combining this transpiler function with R's native pipe operator, you have a convenient way for speeding up iterative computations with minimal refactoring, e.g. lapply(xs, fcn) |> futurize()', purrr::map(xs, fcn) |> futurize()', and foreach::foreach(x = xs) %do% fcn(x) |> futurize()'. Other map-reduce packages that can be "futurized" are BiocParallel', plyr', crossmap', pbapply packages. There is also support for a growing set of domain-specific packages on CRAN (e.g. boot', caret', DiceKriging', ez', fgsea', fwb', gamlss', glmmTMB', glmnet', kernelshap', lme4', metafor', mgcv', modelsummary', parameters', partykit', pls', pvclust', riskRegression', rugarch', sandwich', seriation', shapr', Sim.DiffProc', SimDesign', stars', strucchange', SuperLearner', tm', TSP', and vegan') and on Bioconductor (e.g. DESeq2', GenomicAlignments', GSVA', Rsamtools', scater', scuttle', SingleCellExperiment', and sva').

This package provides a number of statistical tests have been proposed to compare two survival curves, including the difference in (or ratio of) t-year survival, difference in (or ratio of) p-th percentile survival, difference in (or ratio of) restricted mean survival time, and the weighted log-rank test. Despite the multitude of options, the convention in survival studies is to assume proportional hazards and to use the unweighted log-rank test for design and analysis. This package provides sample size and power calculation for all of the above statistical tests with allowance for flexible accrual, censoring, and survival (eg. Weibull, piecewise-exponential, mixture cure). It is the companion R package to the paper by Yung and Liu (2020) <doi:10.1111/biom.13196>. Specific to the weighted log-rank test, users may specify which approximations they wish to use to estimate the large-sample mean and variance. The default option has been shown to provide substantial improvement over the conventional sample size and power equations based on Schoenfeld (1981) <doi:10.1093/biomet/68.1.316>.