Data from the anxiety and confinement study from Alvarado-Aravena et al. (2022) <doi:10.3390/bs12100398>.

An iterative process that optimizes a function by alternately performing restricted optimization over parameter subsets. Instead of joint optimization, it breaks the optimization problem down into simpler sub-problems. This approach can make optimization feasible when joint optimization is too difficult.

The functions defined in this program serve for implementing adaptive two-stage tests. Currently, four tests are included: Bauer and Koehne (1994), Lehmacher and Wassmer (1999), Vandemeulebroecke (2006), and the horizontal conditional error function. User-defined tests can also be implemented. Reference: Vandemeulebroecke, An investigation of two-stage tests, Statistica Sinica 2006.

This package provides a collection of lightweight functions that can be used to determine the computing environment in which your code is running. This includes operating systems, continuous integration (CI) environments, containers, and more.

This package provides statistical tools to analyze heterogeneous effects of rare variants within genes that are associated with multiple traits. The package implements methods for assessing pleiotropic effects and identifying allelic heterogeneity, which can be useful in large-scale genetic studies. Methods include likelihood-based statistical tests to assess these effects. For more details, see Lu et al. (2024) <doi:10.1101/2024.10.01.614806>.

Data from Gardner and Janson art history textbooks about both the artists featured in these books as well as their works. See Helen Gardner ("Art through the ages; an introduction to its history and significance," 1926, <https://find.library.duke.edu/catalog/DUKE000104481>. Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardnerâ s Art through the ages," 1980, ISBN: 0155037587). Fred S. Kleiner ("Gardnerâ s art through the ages: a global history," 2020, ISBN: 9781337630702). Horst de la Croix and Richard G. Tansey ("Gardner's art through the ages," 1986, ISBN: 0155037633). Helen Gardner ("Art through the ages; an introduction to its history and significance," 1936, <https://find.library.duke.edu/catalog/DUKE001199463>). Helen Gardner ("Art through the ages," 1948, <https://find.library.duke.edu/catalog/DUKE001199466>). Helen Gardner, revised under the editorship of Sumner M. Crosby ("Art through the ages," 1959, <https://find.library.duke.edu/catalog/DUKE001199469>). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardnerâ s Art through the ages," 1975, ISBN: 0155037560). Fred S. Kleiner ("Gardnerâ s Art through the ages: a global history," 2013, ISBN: 9780495915423. Fred S. Kleiner, Christin J. Mamiya, Richard G. Tansey ("Gardnerâ s art through the ages," 2001, ISBN: 0155083155). Fred S. Kleiner ("Gardnerâ s Art through the ages: a global history," 2016, ISBN: 9781285837840). Fred S. Kleiner, Christin J. Mamiya ("Gardnerâ s art through the ages," 2005, ISBN: 0534640958). Helen Gardner, revised by Horst de la Croix and Richard G. Tansey ("Gardnerâ s Art through the ages," 1970, ISBN: 0155037528). Helen Gardner, Richard G. Tansey, Fred S. Kleiner ("Gardnerâ s Art through the ages," 1996, ISBN: 0155011413). Helen Gardner, Horst de la Croix, Richard G. Tansey, Diane Kirkpatrick ("Gardnerâ s Art through the ages," 1991, ISBN: 0155037692). Helen Gardner, Fred S. Kleiner ("Gardnerâ s Art through the ages: a global history," 2009, ISBN: 9780495093077). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Jansonâ s history of art: the western tradition," 2007, ISBN: 0131934554). Davies, Penelope J.E., Walter B. Denny, Frima Fox Hofrichter, Joseph F. Jacobs, Ann S. Roberts, David L. Simon ("Jansonâ s history of art: the western tradition," 2011, ISBN: 9780205685172). H. W. Janson, Anthony F. Janson ("History of Art," 2001, ISBN: 0810934469). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1986, ISBN: 013389388). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1977, ISBN: 0810910527). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1969, <https://find.library.duke.edu/catalog/DUKE000005734>). H. W. Janson, Dora Jane Janson ("History of art: a survey of the major visual arts from the dawn of history to present day," 1963, <https://find.library.duke.edu/catalog/DUKE001521852>). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1991, ISBN: 0810934019). H. W. Janson, revised and expanded by Anthony F. Janson ("History of art," 1995, ISBN: 0810934213).

Find an upper bound for the total amount of overstatement of assets in a set of accounts, or estimate the amount of sales tax owed on a collection of transactions (Meeden and Sargent, 2007, <doi:10.1080/03610920701386802>).

Interface to the Azure Machine Learning Software Development Kit ('SDK'). Data scientists can use the SDK to train, deploy, automate, and manage machine learning models on the Azure Machine Learning service. To learn more about Azure Machine Learning visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.

An implementation of the Aligned Rank Transform technique for factorial analysis (see references below for details) including models with missing terms (unsaturated factorial models). The function first computes a separate aligned ranked response variable for each effect of the user-specified model, and then runs a classic ANOVA on each of the aligned ranked responses. For further details, see Higgins, J. J. and Tashtoush, S. (1994). An aligned rank transform test for interaction. Nonlinear World 1 (2), pp. 201-211. Wobbrock, J.O., Findlater, L., Gergle, D. and Higgins,J.J. (2011). The Aligned Rank Transform for nonparametric factorial analyses using only ANOVA procedures. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 11). New York: ACM Press, pp. 143-146. <doi:10.1145/1978942.1978963>.

This package provides the conditional Nelson-Aalen and Aalen-Johansen estimators. The methods are based on Bladt & Furrer (2023), in preparation.

Defines an algebra over maximum likelihood estimators (MLEs) by providing operators that are closed over MLEs, along with various statistical functions for inference. For background on maximum likelihood estimation, see Casella and Berger (2002, ISBN:978-0534243128). For the delta method and variance estimation, see Lehmann and Casella (1998, ISBN:978-0387985022).

This package provides a software that implements a method for partitioning genetic trends to quantify the sources of genetic gain in breeding programmes. The partitioning method is described in Garcia-Cortes et al. (2008) <doi:10.1017/S175173110800205X>. The package includes the main function AlphaPart for partitioning breeding values and auxiliary functions for manipulating data and summarizing, visualizing, and saving results.

Create, upload and run Acumos R models. Acumos (<https://www.acumos.org>) is a platform and open source framework intended to make it easy to build, share, and deploy AI apps. Acumos is part of the LF AI Foundation', an umbrella organization within The Linux Foundation'. With this package, user can create a component, and push it to an Acumos platform.

Simulates, fits, and predicts long-memory and anti-persistent time series, possibly mixed with ARMA, regression, transfer-function components. Exact methods (MLE, forecasting, simulation) are used. Bug reports should be done via GitHub (at <https://github.com/JQVeenstra/arfima>), where the development version of this package lives; it can be installed using devtools.

Self-organizing maps (also known as SOM, see Kohonen (2001) <doi:10.1007/978-3-642-56927-2>) are a method for dimensionality reduction and clustering of continuous data. This package introduces interactive (html) graphics for easier analysis of SOM results. It also features an interactive interface, for push-button training and visualization of SOM on numeric, categorical or mixed data, as well as tools to evaluate the quality of SOM.

This package implements anomaly detection as binary classification for cross-sectional data. Uses maximum likelihood estimates and normal probability functions to classify observations as anomalous. The method is presented in the following lecture from the Machine Learning course by Andrew Ng: <https://www.coursera.org/learn/machine-learning/lecture/C8IJp/algorithm/>, and is also described in: Aleksandar Lazarevic, Levent Ertoz, Vipin Kumar, Aysel Ozgur, Jaideep Srivastava (2003) <doi:10.1137/1.9781611972733.3>.

Fits a linear-binomial model using a modified Newton-type algorithm for solving the maximum likelihood estimation problem under linear box constraints. Similar methods are described in Wagenpfeil, Schöpe and Bekhit (2025, ISBN:9783111341972) "Estimation of adjusted relative risks in log-binomial regression using the BSW algorithm". In: Mau, Mukhin, Wang and Xu (Eds.), Biokybernetika. De Gruyter, Berlin, pp. 665â 676.

This package implements the Classification-based on Association Rules (CBA) algorithm for association rule classification. The package, also described in Hahsler et al. (2019) <doi:10.32614/RJ-2019-048>, contains several convenience methods that allow to automatically set CBA parameters (minimum confidence, minimum support) and it also natively handles numeric attributes by integrating a pre-discretization step. The rule generation phase is handled by the arules package. To further decrease the size of the CBA models produced by the arc package, postprocessing by the qCBA package is suggested.

Create beautiful and interactive visualizations in a single function call. The data.table package is utilized to perform the data wrangling necessary to prepare your data for the plot types you wish to build, along with allowing fast processing for big data. There are two broad classes of plots available: standard plots and machine learning evaluation plots. There are lots of parameters available in each plot type function for customizing the plots (such as faceting) and data wrangling (such as variable transformations and aggregation).

Scraping content from archived web pages stored in the Internet Archive (<https://archive.org>) using a systematic workflow. Get an overview of the mementos available from the respective homepage, retrieve the Urls and links of the page and finally scrape the content. The final output is stored in tibbles, which can be then easily used for further analysis.

Multidimensional scaling models and methods for the visualization and analysis of asymmetric proximity data. An asymmetric data matrix has the same number of rows and columns, and these rows and columns refer to the same set of objects. At least some elements in the upper-triangle are different from the corresponding elements in the lower triangle. An example of an asymmetric matrix is a student migration table, where the rows correspond to the countries of origin of the students and the columns to the destination countries. This package provides algorithms for three multidimensional scaling models, the slide-vector model, a scaling model with unique dimensions and the asymscal model.Furthermore, some other procedures, such as a heat map for skew-symmetric data, and the decomposition of asymmetry are also provided for the exploratory analysis of asymmetric tables.

Model adsorption behavior using classical isotherms, including Langmuir, Freundlich, Brunauerâ Emmettâ Teller (BET), and Temkin models. The package supports parameter estimation through both linearized and non-linear fitting techniques and generates high-quality plots for model diagnostics. It is intended for environmental scientists, chemists, and researchers working on adsorption phenomena in soils, water treatment, and material sciences. Functions are compatible with base R and ggplot2 for visualization.

This package provides tools for the identification of unique of multilocus genotypes when both genotyping error and missing data may be present; targeted for use with large datasets and databases containing multiple samples of each individual (a common situation in conservation genetics, particularly in non-invasive wildlife sampling applications). Functions explicitly incorporate missing data and can tolerate allele mismatches created by genotyping error. If you use this package, please cite the original publication in Molecular Ecology Resources (Galpern et al., 2012), the details for which can be generated using citation('allelematch'). For a complete vignette, please access via the Data S1 Supplementary documentation and tutorials (PDF) located at <doi:10.1111/j.1755-0998.2012.03137.x>.

Enables to compute the statistical indices of affluence (richness) with bootstrap errors, and inequality and polarization indices. Moreover, gives the possibility of calculation of affluence line. Some simple errors are fixed and it works with new version of Spatial Statistics packaged.