Recursive partitioning for least absolute deviation regression trees. Another algorithm from the 1984 book by Breiman, Friedman, Olshen and Stone in addition to the rpart package (Breiman, Friedman, Olshen, Stone (1984, ISBN:9780412048418).

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, lexical summary, terms co-occurrences and documents similarity measures, graphs of terms, 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.

Toolbox for chemometrics analysis of bidimensional gas chromatography data. This package import data for common scientific data format (NetCDF) and fold it to 2D chromatogram. Then, it can perform preprocessing and multivariate analysis. In the preprocessing algorithms, baseline correction, smoothing, and peak alignment are available. While in multivariate analysis, multiway principal component analysis is incorporated.

In repeated measures studies with extreme large or small values it is common that the subjects measurements on average are closer to the mean of the basic population. Interpreting possible changes in the mean in such situations can lead to biased results since the values were not randomly selected, they come from truncated sampling. This method allows to estimate the range of means where treatment effects are likely to occur when regression toward the mean is present. Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.<doi:10.1186/1471-2288-8-52>. Acknowledgments: We would like to acknowledge "Lena Roth" and "Nico Steckhan" for the package's initial updates (Q3 2024) and continued supervision and guidance. Both have contributed to discussing and integrating these methods into the package, ensuring they are up-to-date and contextually relevant.

Calculates the Iberian Actuarial Climate Index and its componentsâ including temperature, precipitation, wind power, and sea level dataâ to support climate change analysis and risk assessment. See "Zhou et al." (2023) <doi:10.26360/2023_3> for further details.

This package implements the regularized exponentially tilted empirical likelihood method. Details of the method are given in Kim, MacEachern, and Peruggia (2023) <doi:10.48550/arXiv.2312.17015>. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

Mixture Composer (Biernacki (2015) <https://inria.hal.science/hal-01253393v1>) is a project to perform clustering using mixture models with heterogeneous data and partially missing data. Mixture models are fitted using a SEM algorithm. It includes 8 models for real, categorical, counting, functional and ranking data.

R implementation of the FAIR Data Pipeline API'. The FAIR Data Pipeline is intended to enable tracking of provenance of FAIR (findable, accessible and interoperable) data used in epidemiological modelling.

An interface to the Integrated Taxonomic Information System ('ITIS') (<https://www.itis.gov>). Includes functions to work with the ITIS REST API methods (<https://www.itis.gov/ws_description.html>), as well as the Solr web service (<https://www.itis.gov/solr_documentation.html>).

This package provides an R6 class and several utility methods to facilitate the implementation of models based on ordinary differential equations. The heart of the package is a code generator that creates compiled Fortran (or R') code which can be passed to a numerical solver. There is direct support for solvers contained in packages deSolve and rootSolve'.

An interface to the powerful and fairly complete computer algebra system Maxima'. It can be used to start and control Maxima from within R by entering Maxima commands. Results from Maxima can be parsed and evaluated in R. It facilitates outputting results from Maxima in LaTeX and MathML'. 2D and 3D plots can be displayed directly. This package also registers a knitr'-engine enabling Maxima code chunks to be written in RMarkdown documents.

The GenDataSample() and GenDataPopulation() functions create, respectively, a sample or population of multivariate nonnormal data using methods described in Ruscio and Kaczetow (2008). Both of these functions call a FactorAnalysis() function to reproduce a correlation matrix. The EFACompData() function allows users to determine how many factors to retain in an exploratory factor analysis of an empirical data set using a method described in Ruscio and Roche (2012). The latter function uses populations of comparison data created by calling the GenDataPopulation() function. <DOI: 10.1080/00273170802285693>. <DOI: 10.1037/a0025697>.

Access Synthesize Bio models from their API <https://app.synthesize.bio/> using this wrapper that provides a convenient interface to the Synthesize Bio API, allowing users to generate realistic gene expression data based on specified biological conditions. This package enables researchers to easily access AI-generated transcriptomic data for various modalities including bulk RNA-seq, single-cell RNA-seq, microarray data, and more.

TSON, short for Typed JSON, is a binary-encoded serialization of JSON like document that support JavaScript typed data (https://github.com/tercen/TSON).

Robust multivariate methods for high dimensional data including outlier detection (Filzmoser and Todorov (2013) <doi:10.1016/j.ins.2012.10.017>), robust sparse PCA (Croux et al. (2013) <doi:10.1080/00401706.2012.727746>, Todorov and Filzmoser (2013) <doi:10.1007/978-3-642-33042-1_31>), robust PLS (Todorov and Filzmoser (2014) <doi:10.17713/ajs.v43i4.44>), and robust sparse classification (Ortner et al. (2020) <doi:10.1007/s10618-019-00666-8>).

Aims at loading Criteo online advertising campaign data into R. Criteo <http://www.criteo.com/> is an online advertising service that enables advertisers to display commercial ads to web users. The package provides an authentication process for R with the Criteo API <http://kb.criteo.com/ advertising/content/5/27/en/api.html>. Moreover, the package features an interface to query campaign data from the Criteo API. The data can be downloaded and will be transformed into a R data frame.

Allow for easy-to-use testing or evaluating of linear equality and inequality restrictions about parameters and effects in (generalized) linear statistical models.

This package provides a function to plot a regression nomogram of regression objects. Covariate distributions are superimposed on nomogram scales and the plot can be animated to allow on-the-fly changes to distribution representation and to enable outcome calculation.

The provided benchmark suite enables the automated evaluation and comparison of any existing and novel indirect method for reference interval ('RI') estimation in a systematic way. Indirect methods take routine measurements of diagnostic tests, containing pathological and non-pathological samples as input and use sophisticated statistical methods to derive a model describing the distribution of the non-pathological samples, which can then be used to derive reference intervals. The benchmark suite contains 5,760 simulated test sets with varying difficulty. To include any indirect method, a custom wrapper function needs to be provided. The package offers functions for generating the test sets, executing the indirect method and evaluating the results. See ?RIbench or vignette("RIbench_package") for a more comprehensive description of the features. A detailed description and application is described in Ammer T., Schuetzenmeister A., Prokosch H.-U., Zierk J., Rank C.M., Rauh M. "RIbench: A Proposed Benchmark for the Standardized Evaluation of Indirect Methods for Reference Interval Estimation". Clinical Chemistry (2022) <doi:10.1093/clinchem/hvac142>.

This package provides an interface to the Facebook API.

Rare variant association tests: burden tests (Bocher et al. 2019 <doi:10.1002/gepi.22210>) and the Sequence Kernel Association Test (Bocher et al. 2021 <doi:10.1038/s41431-020-00792-8>) in the whole genome; and genetic simulations.

We propose a general ensemble classification framework, RaSE algorithm, for the sparse classification problem. In RaSE algorithm, for each weak learner, some random subspaces are generated and the optimal one is chosen to train the model on the basis of some criterion. To be adapted to the problem, a novel criterion, ratio information criterion (RIC) is put up with based on Kullback-Leibler divergence. Besides minimizing RIC, multiple criteria can be applied, for instance, minimizing extended Bayesian information criterion (eBIC), minimizing training error, minimizing the validation error, minimizing the cross-validation error, minimizing leave-one-out error. There are various choices of base classifier, for instance, linear discriminant analysis, quadratic discriminant analysis, k-nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also be applied to do feature ranking, providing us the importance of each feature based on the selected percentage in multiple subspaces. RaSE framework can be extended to the general prediction framework, including both classification and regression. We can use the selected percentages of variables for variable screening. The latest version added the variable screening function for both regression and classification problems.

Allows loading and displaying an Observable notebook (online JavaScript notebooks powered by <https://observablehq.com>) as an HTML Widget in an R session, shiny application or rmarkdown document.

An implementation of the RainFARM (Rainfall Filtered Autoregressive Model) stochastic precipitation downscaling method (Rebora et al. (2006) <doi:10.1175/JHM517.1>). Adapted for climate downscaling according to D'Onofrio et al. (2018) <doi:10.1175/JHM-D-13-096.1> and for complex topography as in Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>. The RainFARM method is based on the extrapolation to small scales of the Fourier spectrum of a large-scale precipitation field, using a fixed logarithmic slope and random phases at small scales, followed by a nonlinear transformation of the resulting linearly correlated stochastic field. RainFARM allows to generate ensembles of spatially downscaled precipitation fields which conserve precipitation at large scales and whose statistical properties are consistent with the small-scale statistics of observed precipitation, based only on knowledge of the large-scale precipitation field.