Solves optimal pairing and matching problems using linear assignment algorithms. Provides implementations of the Hungarian method (Kuhn 1955) <doi:10.1002/nav.3800020109>, Jonker-Volgenant shortest path algorithm (Jonker and Volgenant 1987) <doi:10.1007/BF02278710>, Auction algorithm (Bertsekas 1988) <doi:10.1007/BF02186476>, cost-scaling (Goldberg and Kennedy 1995) <doi:10.1007/BF01585996>, scaling algorithms (Gabow and Tarjan 1989) <doi:10.1137/0218069>, push-relabel (Goldberg and Tarjan 1988) <doi:10.1145/48014.61051>, and Sinkhorn entropy-regularized transport (Cuturi 2013) <doi:10.48550/arxiv.1306.0895>. Designed for matching plots, sites, samples, or any pairwise optimization problem. Supports rectangular matrices, forbidden assignments, data frame inputs, batch solving, k-best solutions, and pixel-level image morphing for visualization. Includes automatic preprocessing with variable health checks, multiple scaling methods (standardized, range, robust), greedy matching algorithms, and comprehensive balance diagnostics for assessing match quality using standardized differences and distribution comparisons.

This package provides functions to compute and plot Coverage Probability Excursion (CoPE) sets for real valued functions on a 2-dimensional domain. CoPE sets are obtained from repeated noisy observations of the function on the entire domain. They are designed to bound the excursion set of the target function at a given level from above and below with a predefined probability. The target function can be a parameter in spatially-indexed linear regression. Support by NIH grant R01 CA157528 is gratefully acknowledged.

Estimation of crop water demand can be processed via this package. As example, the data from TerraClimate dataset (<https://www.climatologylab.org/terraclimate.html>) calibrated with automatic weather stations of National Meteorological Institute of Brazil is available in a coarse spatial resolution to do the crop water demand. However, the user have also the option to download the variables directly from TerraClimate repository with the download.terraclimate function and access the original TerraClimate products. If the user believes that is necessary calibrate the variables, there is another function to do it. Lastly, the estimation of the crop water demand present in this package can be run for all the Brazilian territory with TerraClimate dataset.

Manipulate and analyze 3-D structural geometry of Protein Data Bank (PDB) files.

This package implements the multiple changepoint algorithm PELT with a nonparametric cost function based on the empirical distribution of the data. This package extends the changepoint package (see Killick, R and Eckley, I (2014) <doi:10.18637/jss.v058.i03> ).

Model soil gas fluxes with the Flux-Gradient Method. It includes functions for data handling, a forward and an inverse model for flux modeling and methods for calibration and uncertainty estimation. For more details see Gartiser et al. (2025a) <doi:10.21105/joss.08094> and Gartiser et al. (2025b) <doi:10.1111/ejss.70126>.

This package provides an interactive shiny web application for constructing, analyzing, and visualizing composite indices from multidimensional datasets. Users can upload or select indicator data, group variables into logical categories, apply normalization and weighting methods (such as equal or custom schemes), and compute aggregate composite indices. The shiny interface includes tools for exploring results through tables, plots, and data exports, making it useful for researchers, policymakers, and analysts interested in index-based evaluations.

This package provides an alternative approach to multiple testing by calculating a simultaneous upper confidence bounds for the number of true null hypotheses among any subset of the hypotheses of interest, using the methods of Goeman and Solari (2011) <doi:10.1214/11-STS356>.

Many correlation coefficient related functions are offered, such as correlations, partial correlations and hypothesis testing using asymptotic tests and computer intensive methods (bootstrap and permutation). References include Mardia K.V., Kent J.T. and Bibby J.M. (1979). "Multivariate Analysis". ISBN: 978-0124712522. London: Academic Press and Owen A. B. (2001). "Empirical likelihood". Chapman and Hall/CRC Press. ISBN: 9781584880714.

Convert BCD (raw bytes) to decimal numbers and vice versa. BCD format is used to preserve decimals exactly, as opposed to the binary rounding errors inherent in "numeric" or "floating-point" formats.

Hierarchical continuous (and discrete) time state space modelling, for linear and nonlinear systems measured by continuous variables, with limited support for binary data. The subject specific dynamic system is modelled as a stochastic differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models. Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default. Nonlinearities, (state dependent parameters) and random effects on all parameters are possible, using either max likelihood / max a posteriori optimization (with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling. See <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> for details. See <https://osf.io/preprints/psyarxiv/4q9ex_v2> for a detailed tutorial. Priors may be used. For the conceptual overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>. Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> . <https://cdriver.netlify.app/> contains some tutorial blog posts.

Automated method for doublet detection in flow or mass cytometry data, based on simulating doublets and finding events whose protein expression patterns are similar to the simulated doublets.

Imports conversation transcripts into R, concatenates them into a single dataframe appending event identifiers, cleans and formats the text, then yokes user-specified psycholinguistic database values to each word. ConversationAlign then computes alignment indices between two interlocutors across each transcript for >40 possible semantic, lexical, and affective dimensions. In addition to alignment, ConversationAlign also produces a table of analytics (e.g., token count, type-token-ratio) in a summary table describing your particular text corpus.

This package contains the function calendR() for creating fully customizable monthly and yearly calendars (colors, fonts, formats, ...) and even heatmap calendars. In addition, it allows saving the calendars in ready to print A4 format PDF files.

This package provides tools that allow developers to write functions for cross-validation with minimal programming effort and assist users with model selection.

Compile inline C code and easily call with automatically generated wrapper functions. By allowing user-defined headers and compilation flags (preprocessor, compiler and linking flags) the user can configure optimization options and linking to third party libraries. Multiple functions may be defined in a single block of code - which may be defined in a string or a path to a source file.

Nonparametric change point estimation for survival data based on p-values of exact binomial tests.

We propose a consistent monitoring procedure to detect a structural change from a cointegrating relationship to a spurious relationship. The procedure is based on residuals from modified least squares estimation, using either Fully Modified, Dynamic or Integrated Modified OLS. It is inspired by Chu et al. (1996) <DOI:10.2307/2171955> in that it is based on parameter estimation on a pre-break "calibration" period only, rather than being based on sequential estimation over the full sample. See the discussion paper <DOI:10.2139/ssrn.2624657> for further information. This package provides the monitoring procedures for both the cointegration and the stationarity case (while the latter is just a special case of the former one) as well as printing and plotting methods for a clear presentation of the results.

To improve estimation accuracy and stability in statistical modeling, catalytic prior distributions are employed, integrating observed data with synthetic data generated from a simpler model's predictive distribution. This approach enhances model robustness, stability, and flexibility in complex data scenarios. The catalytic prior distributions are introduced by Huang et al. (2020, <doi:10.1073/pnas.1920913117>), Li and Huang (2023, <doi:10.48550/arXiv.2312.01411>).

Imports PxStat data in JSON-stat format and (optionally) reshapes it into wide format. The Central Statistics Office (CSO) is the national statistical institute of Ireland and PxStat is the CSOs online database of Official Statistics. This database contains current and historical data series compiled from CSO statistical releases and is accessed at <https://data.cso.ie>. The CSO PxStat Application Programming Interface (API), which is accessed in this package, provides access to PxStat data in JSON-stat format at <https://data.cso.ie>. This dissemination tool allows developers machine to machine access to CSO PxStat data.

Multiple comparison techniques are typically applied following an F test from an ANOVA to decide which means are significantly different from one another. As an alternative to traditional methods, cluster analysis can be performed to group the means of different treatments into non-overlapping clusters. Treatments in different groups are considered statistically different. Several approaches have been proposed, with varying clustering methods and cut-off criteria. This package implements cluster-based multiple comparisons tests and also provides a visual representation in the form of a dendrogram. Di Rienzo, J. A., Guzman, A. W., & Casanoves, F. (2002) <jstor.org/stable/1400690>. Bautista, M. G., Smith, D. W., & Steiner, R. L. (1997) <doi:10.2307/1400402>.

Estimation, testing and regression modeling of subdistribution functions in competing risks using quantile regressions, as described in Peng and Fine (2009) <DOI:10.1198/jasa.2009.tm08228>.

This package implements clustering techniques such as Proximus and Rock, utility functions for efficient computation of cross distances and data manipulation.

Create descriptive tables for continuous and categorical variables. Apply summary statistics and counting function, with or without a grouping variable, and create beautiful reports using rmarkdown or officer'. You can also compute effect sizes and statistical tests if needed.