Native R implementation of many-facet ordered-response measurement models with arbitrary facet counts, rating-scale and partial-credit parameterizations, a bounded generalized partial-credit extension, and both marginal and joint maximum likelihood estimation. The package provides a fit / diagnose / report pipeline covering anchoring, linking, bias and differential-functioning screening, and publication-oriented reporting summaries, with reproducibility manifests for replay. See Andrich (1978) <doi:10.1007/BF02293814>, Masters (1982) <doi:10.1007/BF02296272>, and Muraki (1992) <doi:10.1177/014662169201600206> for the underlying ordered-response models.

The tdROC package facilitates the estimation of time-dependent ROC (Receiver Operating Characteristic) curves and the Area Under the time-dependent ROC Curve (AUC) in the context of survival data, accommodating scenarios with right censored data and the option to account for competing risks. In addition to the ROC/AUC estimation, the package also estimates time-dependent Brier score and survival difference. Confidence intervals of various estimated quantities can be obtained from bootstrap. The package also offers plotting functions for visualizing time-dependent ROC curves.

This package provides a comprehensive suite of statistical tools for analyzing, simulating, and computing properties of the Topp-Leone Cauchy Rayleigh (TLCAR) distribution, a versatile distribution amalgamating features of the Topp-Leone, Cauchy, and Rayleigh distributions, ideal for modeling intricate, heterogeneous data across scientific domains. See Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2023) <doi:10.1007/s44199-023-00066-4> and Atchadé, M.N., Bogninou, M.J., and Djibril, A.M. (2024) <doi:10.1007/s44199-023-00069-1> for further insights.

This tool provides functions to load, segment and classify zooplankton images. The image processing algorithms and the machine learning classifiers in this package are (will be, since these have not been added yet) direct ports of an early python implementation that can be found at <https://github.com/arickGrootveld/ZooID>. The model weights and datasets (also not added yet) that are a part of this package can also be found at Arick Grootveld, Eva R. Kozak, Carmen Franco-Gordo (2023) <doi:10.5281/zenodo.7979996>.

This package provides flexible Bayesian estimation of IMIFA and related models, for nonparametrically clustering high-dimensional data. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.

This is package for QTL mapping in a mixed model framework with separate detection and localization stages. The first stage detects the number of QTL on each chromosome based on the genetic variation due to grouped markers on the chromosome; the second stage uses this information to determine the most likely QTL positions. The mixed model can accommodate general fixed and random effects, including spatial effects in field trials and pedigree effects. It is applicable to backcrosses, doubled haploids, recombinant inbred lines, F2 intercrosses, and association mapping populations.

An approximate Bayesian method for inferring Directed Acyclic Graphs (DAGs) for continuous, discrete, and mixed data. The algorithm can use the graph inferred by another more efficient graph inference method as input; the input graph may contain false edges or undirected edges but can help reduce the search space to a more manageable size. A Metropolis-Hastings-like sampling algorithm is then used to infer the posterior probabilities of edge direction and edge absence. References: Martin, Patchigolla and Fu (2026) <doi:10.48550/arXiv.1909.10678>.

Decomposition of (income) inequality by population sub groups. For a decomposition on a single variable the mean log deviation can be used (see Mookherjee Shorrocks (1982) <DOI:10.2307/2232673>). For a decomposition on multiple variables a regression based technique can be used (see Fields (2003) <DOI:10.1016/s0147-9121(03)22001-x>). Recentered influence function regression for marginal effects of the (income or wealth) distribution (see Firpo et al. (2009) <DOI:10.3982/ECTA6822>). Some extensions to inequality functions to handle weights and/or missings.

This package implements an efficient algorithm for solving sparse-penalized support vector machines with kernel density convolution. This package is designed for high-dimensional classification tasks, supporting lasso (L1) and elastic-net penalties for sparse feature selection and providing options for tuning kernel bandwidth and penalty weights. The dcsvm is applicable to fields such as bioinformatics, image analysis, and text classification, where high-dimensional data commonly arise. Learn more about the methodology and algorithm at Wang, Zhou, Gu, and Zou (2023) <doi:10.1109/TIT.2022.3222767>.

This package implements the Elliptical Covering Marginal Likelihood Estimator (ECMLE), a geometric method for approximating marginal likelihood from posterior draws and log-posterior evaluations. The method constructs a collection of non-overlapping ellipsoids in a high-posterior-density region, computes the covered volume, and combines this with posterior sample coverage to estimate model evidence. It is designed to stabilize harmonic-mean-based evidence approximation and can be applied in multimodal settings. The methodology is described in Naderi et al. (2025) <doi:10.48550/arXiv.2510.20617>.

This package provides methods for fitting various extreme value distributions with parameters of generalised additive model (GAM) form are provided. For details of distributions see Coles, S.G. (2001) <doi:10.1007/978-1-4471-3675-0>, GAMs see Wood, S.N. (2017) <doi:10.1201/9781315370279>, and the fitting approach see Wood, S.N., Pya, N. & Safken, B. (2016) <doi:10.1080/01621459.2016.1180986>. Details of how evgam works and various examples are given in Youngman, B.D. (2022) <doi:10.18637/jss.v103.i03>.

Helper functions provide an accurate imputation algorithm for reconstructing the missing segment in a multi-variate data streams. Inspired by single-shot learning, it reconstructs the missing segment by identifying the first similar segment in the stream. Nevertheless, there should be one column of data available, i.e. a constraint column. The values of columns can be characters (A, B, C, etc.). The result of the imputed dataset will be returned a .csv file. For more details see Reza Rawassizadeh (2019) <doi:10.1109/TKDE.2019.2914653>.

This package provides functions for simulating and estimating parameters of various growth models, including Logistic, Exponential, Theta-logistic, Von-Bertalanffy, and Gompertz models. The package supports both simulated and real data analysis, including parameter estimation, visualization, and calculation of global and local estimates. The methods are based on research described by Md Aktar Ul Karim and Amiya Ranjan Bhowmick (2022) in (<https://www.researchsquare.com/article/rs-2363586/v1>). An interactive web application is also available at [GPEMR Web App](<https://gpem-r.shinyapps.io/GPEM-R/>).

An S4 class and several functions which utilize internally stored datasets and gauging data enable 1d water level interpolation. The S4 class (WaterLevelDataFrame) structures the computation and visualisation of 1d water level information along the German federal waterways Elbe and Rhine. hyd1d delivers 1d water level data - extracted from the FLYS database - and validated gauging data - extracted from the hydrological database WISKI7 - package-internally. For computations near real time gauging data are queried externally from the PEGELONLINE REST API <https://pegelonline.wsv.de/webservice/dokuRestapi>.

This package performs Gaussian process regression with heteroskedastic noise following the model by Binois, M., Gramacy, R., Ludkovski, M. (2016) <doi:10.48550/arXiv.1611.05902>, with implementation details in Binois, M. & Gramacy, R. B. (2021) <doi:10.18637/jss.v098.i13>. The input dependent noise is modeled as another Gaussian process. Replicated observations are encouraged as they yield computational savings. Sequential design procedures based on the integrated mean square prediction error and lookahead heuristics are provided, and notably fast update functions when adding new observations.

The implementation of a statistical framework for performing overlap assessments on lists comprising sets of strings (such as lists of gene sets) described in Stoica (2023) <https://ora.ox.ac.uk/objects/uuid:b0847284-a02f-47ee-88e3-a3c4e0cdb8b1>. It can assess overlaps of pairs of sets of strings selected either from the same universe or from different universes, and overlaps of triplets of sets of strings selected from the same universe. Designed for single-cell RNA-sequencing data analysis applications, but suitable for other purposes as well.

Estimate diagnostic classification models (also called cognitive diagnostic models) with Stan'. Diagnostic classification models are confirmatory latent class models, as described by Rupp et al. (2010, ISBN: 978-1-60623-527-0). Automatically generate Stan code for the general loglinear cognitive diagnostic diagnostic model proposed by Henson et al. (2009) <doi:10.1007/s11336-008-9089-5> and other subtypes that introduce additional model constraints. Using the generated Stan code, estimate the model evaluate the model's performance using model fit indices, information criteria, and reliability metrics.

This package provides a set of evolutionary algorithms to solve many-objective optimization. Hybridization between the algorithms are also facilitated. Available algorithms are: SMS-EMOA <doi:10.1016/j.ejor.2006.08.008> NSGA-III <doi:10.1109/TEVC.2013.2281535> MO-CMA-ES <doi:10.1145/1830483.1830573> The following many-objective benchmark problems are also provided: DTLZ1'-'DTLZ4 from Deb, et al. (2001) <doi:10.1007/1-84628-137-7_6> and WFG4'-'WFG9 from Huband, et al. (2005) <doi:10.1109/TEVC.2005.861417>.

Generate and analyze Optimal Channel Networks (OCNs): oriented spanning trees reproducing all scaling features characteristic of real, natural river networks. As such, they can be used in a variety of numerical experiments in the fields of hydrology, ecology and epidemiology. See Carraro et al. (2020) <doi:10.1002/ece3.6479> for a presentation of the package; Rinaldo et al. (2014) <doi:10.1073/pnas.1322700111> for a theoretical overview on the OCN concept; Furrer and Sain (2010) <doi:10.18637/jss.v036.i10> for the construct used.

Complex machine learning models are often hard to interpret. However, in many situations it is crucial to understand and explain why a model made a specific prediction. Shapley values is the only method for such prediction explanation framework with a solid theoretical foundation. Previously known methods for estimating the Shapley values do, however, assume feature independence. This package implements methods which accounts for any feature dependence, and thereby produces more accurate estimates of the true Shapley values. An accompanying Python wrapper ('pyshapr') is available through PyPI.

This package provides a simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the "Using skimr" vignette and the README.

This package implements a variable neighborhood trust region search (VNTRS) algorithm for nonlinear global optimization, based on Bierlaire et al. (2009) "A Heuristic for Nonlinear Global Optimization" <doi:10.1287/ijoc.1090.0343>. The method combines neighborhood exploration with a trust-region framework to search the solution space efficiently. It can terminate a local search early when the iterates converge toward a previously visited local optimum or when further improvement within the current region is unlikely. The algorithm can also be used to identify multiple local optima.

Real-time quantitative polymerase chain reaction (qPCR) data sets by Boggy et al. (2008) <doi:10.1371/journal.pone.0012355>. This package provides a dilution series for one PCR target: a random sequence that minimizes secondary structure and off-target primer binding. The data set is a six-point, ten-fold dilution series. For each concentration there are two replicates. Each amplification curve is 40 cycles long. Original raw data file: <https://journals.plos.org/plosone/article/file?type=supplementary&id=10.1371/journal.pone.0012355.s004>.

Provide functions for overlaps clustering, fuzzy clustering and interval-valued data manipulation. The package implement the following algorithms: OKM (Overlapping Kmeans) from Cleuziou, G. (2007) <doi:10.1109/icpr.2008.4761079> ; NEOKM (Non-exhaustive overlapping Kmeans) from Whang, J. J., Dhillon, I. S., and Gleich, D. F. (2015) <doi:10.1137/1.9781611974010.105> ; Fuzzy Cmeans from Bezdek, J. C. (1981) <doi:10.1007/978-1-4757-0450-1> ; Fuzzy I-Cmeans from de A.T. De Carvalho, F. (2005) <doi:10.1016/j.patrec.2006.08.014>.