Interface to the ReebGraphPairing program to compute critical points of Reeb graphs following Tu, Hajij, & Rosen (2019) <doi:10.1007/978-3-030-33720-9_8> via the rJava package. Also store Reeb graphs in a minimal S3 class, convert between other network data structures, and post-process pairing data to obtain extended persistent homology following Carrière & Oudot (2018) <doi:10.1007/s10208-017-9370-z>.

This package provides methods for detecting structural breaks and estimating break locations for linear multiple regression models under general linear restrictions on the coefficient vector. Restrictions can be within regimes, across regimes, or both, and are supported in two forms: an affine parameterization (Form A: delta = S*theta + s) and explicit linear constraints (Form B: R*delta = r). Provides break date estimation with confidence intervals, a restricted sup-F test for the null of no structural change, simulation of critical values by Monte Carlo, and a bootstrap restart procedure to reduce the risk of convergence to spurious local optima. Also implements a generalized regression tree (linear model tree) procedure where each leaf contains a linear regression model rather than a local average. Reference: Perron, P., and Qu, Z. (2006). Estimating Restricted Structural Change Models. Journal of Econometrics, 134(2), 373-399. <doi:10.1016/j.jeconom.2005.06.030>.

This package provides a rotatogram is a method of displaying an association which is axis non-dominant. This is achieved in two ways: First, the method of estimating the slope and intercept uses the least-products method rather than more typical least squared error for the "dependent" variable. The least products method has no "dependent" variable and is scale independent. Second, the plot is rotated such that the resulting regression line is vertical, reducing the suggestion that the vertical axis is the dominant one. The slope can be read relative to either axis equally.

Implementation of corrected two-sample tests. A corrected version of the Pearson and Kendall correlation tests, the Mann-Whitney (Wilcoxon) rank sum test, the Wilcoxon signed rank test and a variance test are implemented. The package also proposes a test for the median and an independence test between two continuous variables of Kolmogorov-Smirnov's type. All these corrected tests are asymptotically calibrated in the sense that the probability of rejection under the null hypothesis is asymptotically equal to the level of the test. See <doi:10.48550/arXiv.2211.08784> for more details on the statistical tests.

Recursive display of names and paths of all the items nested within sublists of a list object.

This package provides read and write support for the Conic Benchmark Format (CBF, version 4) within the R Optimization Infrastructure ('ROI'). Supported cone types include the positive orthant, second-order (SOC), rotated second-order (bridged automatically to standard SOC), exponential (primal and dual), power (primal and dual), and semidefinite (symmetric-vectorised) cones, as well as their mixed-integer variants. The reader translates a .cbf file into an ROI OP object, handling coordinate-convention differences between CBF and ROI transparently; the writer serialises an ROI OP object back to CBF plain-text.

We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.

Yandex Translate (https://translate.yandex.com/) is a statistical machine translation system. The system translates separate words, complete texts, and webpages. This package can be used to detect language from text and to translate it to supported target language. For more info: https://tech.yandex.com/translate/doc/dg/concepts/About-docpage/ .

This holds r markdown and quarto templates for academic papers and slide decks. It also has templates to create research projects which contain academic papers as vignettes.

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.

Quickly imports, processes, analyzes, and visualizes mass-spectrometric data. Includes functions for easily extracting specific data and measurements from large (multi-gigabyte) raw Bruker data files, as well as a set of S3 object classes for manipulating and measuring mass spectrometric peaks and plotting peaks and spectra using the ggplot2 package.

This package provides functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.

Implemented fast and memory-efficient Notch-filter, Welch-periodogram, discrete wavelet spectrogram for minutes of high-resolution signals, fast 3D convolution, image registration, 3D mesh manipulation; providing fundamental toolbox for intracranial Electroencephalography (iEEG) pipelines. Documentation and examples about RAVE project are provided at <https://rave.wiki>, and the paper by John F. Magnotti, Zhengjia Wang, Michael S. Beauchamp (2020) <doi:10.1016/j.neuroimage.2020.117341>; see citation("ravetools") for details.

The Radiant Multivariate menu includes interfaces for perceptual mapping, factor analysis, cluster analysis, and conjoint analysis. The application extends the functionality in radiant.data'.

Ray Shooting Depth functions are provided for bivariate analysis. This mainly includes functions for computing the bivariate depth as well as RS median. Drawing functions for depth bags are also provided.

An interface to the BaM (Bayesian Modeling) engine, a Fortran'-based executable aimed at estimating a model with a Bayesian approach and using it for prediction, with a particular focus on uncertainty quantification. Classes are defined for the various building blocks of BaM inference (model, data, error models, Markov Chain Monte Carlo (MCMC) samplers, predictions). The typical usage is as follows: (1) specify the model to be estimated; (2) specify the inference setting (dataset, parameters, error models...); (3) perform Bayesian-MCMC inference; (4) read, analyse and use MCMC samples; (5) perform prediction experiments. Technical details are available (in French) in Renard (2017) <https://hal.science/hal-02606929v1>. Examples of applications include Mansanarez et al. (2019) <doi:10.1029/2018WR023389>, Le Coz et al. (2021) <doi:10.1002/hyp.14169>, Perret et al. (2021) <doi:10.1029/2020WR027745>, Darienzo et al. (2021) <doi:10.1029/2020WR028607> and Perret et al. (2023) <doi:10.1061/JHEND8.HYENG-13101>.

This package provides an interface to many endpoints of Mixpanel's Data Export, Engage and JQL API. The R functions allow for event and profile data export as well as for segmentation, retention, funnel and addiction analysis. Results are always parsed into convenient R objects. Furthermore it is possible to load and update profiles.

This package provides a simple and efficient way to read data from Paradox database files (.db) directly into R as modern tibble data frames. It uses the underlying pxlib C library, to handle the low-level file format details and provides a clean, user-friendly R interface.

Implementation of the Johnson Quantile-Parameterised Distribution in R. The Johnson Quantile-Parameterised Distribution (J-QPD) is a flexible distribution system that is parameterised by a symmetric percentile triplet of quantile values (typically the 10th-50th-90th) along with known support bounds for the distribution. The J-QPD system was developed by Hadlock and Bickel (2017) <doi:10.1287/deca.2016.0343>. This package implements the density, quantile, CDF and random number generator functions.

Checking the reliability of predictions via the CORP approach, which generates provably statistically C'onsistent, O'ptimally binned, and R'eproducible reliability diagrams using the P'ool-adjacent-violators algorithm. See Dimitriadis, Gneiting, Jordan (2021) <doi:10.1073/pnas.2016191118>.

Much as roxygen2 allows one to document functions in the same file as the function itself, roxut allows one to write the unit tests in the same file as the function. Once processed, the unit tests are moved to the appropriate directory. Currently supports testthat and tinytest frameworks. The roxygen2 package provides much of the infrastructure.

An implementation of calls designed to collect and organize Mastodon data via its Application Program Interfaces (API), which can be found at the following URL: <https://docs.joinmastodon.org/>.

Rcmdr plug-in GUI extension for Evidence Based Medicine medical indicators calculations (Sensitivity, specificity, absolute risk reduction, relative risk, ...).

Adds menu items for case 2 (profile case) best-worst scaling (BWS2) to the R Commander. BWS2 is a question-based survey method that constructs profiles (combinations of attribute levels) using an orthogonal array, asks respondents to select the best and worst levels in each profile, and measures preferences for attribute levels by analyzing the responses. For details, see Aizaki and Fogarty (2019) <doi:10.1016/j.jocm.2019.100171>.