Receiver Operating Characteristic (ROC) analysis is performed assuming samples are from the proposed distributions. In addition, the volume under the ROC surface and true positive fractions values are evaluated by ROC surface analysis.

Analyses sentiment of a sentence in English and assigns score to it. It can classify sentences to the following categories of sentiments:- Positive, Negative, very Positive, very negative, Neutral. For a vector of sentences, it counts the number of sentences in each category of sentiment.In calculating the score, negation and various degrees of adjectives are taken into consideration. It deals only with English sentences.

Interface to use and access Wilensky's NetLogo (Wilensky 1999) from R using either headless (no GUI) or interactive GUI mode. Provides functions to load models, execute commands, and get values from reporters. Mostly analogous to the NetLogo Mathematica Link <https://github.com/NetLogo/Mathematica-Link>.

Helps users in quickly visualizing risk-of-bias assessments performed as part of a systematic review. It allows users to create weighted bar-plots of the distribution of risk-of-bias judgments within each bias domain, in addition to traffic-light plots of the specific domain-level judgments for each study. The resulting figures are of publication quality and are formatted according the risk-of-bias assessment tool use to perform the assessments. Currently, the supported tools are ROB2.0 (for randomized controlled trials; Sterne et al (2019) <doi:10.1136/bmj.l4898>), ROBINS-I (for non-randomised studies of interventions; Sterne et al (2016) <doi:10.1136/bmj.i4919>), and QUADAS-2 (for diagnostic accuracy studies; Whiting et al (2011) <doi:10.7326/0003-4819-155-8-201110180-00009>).

Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary: (1) The graph parameters file, which name is passed to the function rplotengine()'. The user can specify the titles, choose the type of the graph, graph output formats (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend, whether to show or not a grid at the background, etc. (2) The data to be plotted, which name is specified as a parameter ('data_filename') in the previous file. This data file has a tabulated format, with a single character (e.g. tab) between each column. Optionally, the file could include data columns for showing confidence intervals.

Robust likelihood cross validation bandwidth for uni- and multi-variate kernel densities. It is robust against fat-tailed distributions and/or outliers. Based on "Robust Likelihood Cross-Validation for Kernel Density Estimation," Wu (2019) <doi:10.1080/07350015.2018.1424633>.

This package provides a framework for estimating ensembles of meta-analytic, meta-regression, and multilevel models (assuming either presence or absence of the effect, heterogeneity, publication bias, and moderators). The RoBMA framework uses Bayesian model-averaging to combine the competing meta-analytic models into a model ensemble, weights the posterior parameter distributions based on posterior model probabilities and uses Bayes factors to test for the presence or absence of the individual components (e.g., effect vs. no effect; BartoÅ¡ et al., 2022, <doi:10.1002/jrsm.1594>; Maier, BartoÅ¡ & Wagenmakers, 2022, <doi:10.1037/met0000405>; BartoÅ¡ et al., 2025, <doi:10.1037/met0000737>). Users can define a wide range of prior distributions for the effect size, heterogeneity, publication bias (including selection models and PET-PEESE), and moderator components. The package provides convenient functions for summary, visualizations, and fit diagnostics.

This package provides a collection of tools for extracting structured data from <https://www.reddit.com/>.

This package provides a toolkit for the analysis of high-dimensional repeated measurements, providing functions for outlier detection, differential expression analysis, gene-set tests, and binary random data generation.

Load data from vk.com api about your communiti users and views, ads performance, post on user wall and etc. For more information see API Documentation <https://vk.com/dev/first_guide>.

Access the Refuge API, a web-application for locating trans and intersex-friendly restrooms, including unisex and accessible restrooms. Includes data on the location of restrooms, along with directions, comments, user ratings and amenities. Coverage is global, but data is most comprehensive in the United States. See <https://www.refugerestrooms.org/api/docs/> for full API documentation.

Open any data frame with visidata', a terminal-based spreadsheet application <https://www.visidata.org>.

Protocol Buffers are a way of encoding structured data in an efficient yet extensible format. Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats. Additional documentation is available in two included vignettes one of which corresponds to our JSS paper (2016, <doi:10.18637/jss.v071.i02>. A sufficiently recent version of Protocol Buffers library is required; currently version 3.3.0 from 2017 is the tested minimum.

Computes confidence intervals for nonlinear functions of model parameters (e.g., product of k coefficients) in single-level and multilevel structural equation models. Methods include the distribution of the product, Monte Carlo simulation, and bootstrap methods. It also performs the Model-Based Constrained Optimization (MBCO) procedure for hypothesis testing of indirect effects. References: Tofighi, D., and MacKinnon, D. P. (2011). RMediation: An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692-700. <doi:10.3758/s13428-011-0076-x>; Tofighi, D., and Kelley, K. (2020). Improved inference in mediation analysis: Introducing the model-based constrained optimization procedure. Psychological Methods, 25(4), 496-515. <doi:10.1037/met0000259>; Tofighi, D. (2020). Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects. Frontiers in Psychology, 10, 2989. <doi:10.3389/fpsyg.2019.02989>.

This package provides a set of tools to explore the behaviour statistics used for forensic DNA interpretation when close relatives are involved. The package also offers some useful tools for exploring other forensic DNA situations.

Parser for SQL statements. Currently, it supports parsing of only SELECT statements.

Supports analysis of spatial data processed with the GeoPAT 2 software <https://github.com/Nowosad/geopat2>. Available features include creation of a grid based on the GeoPAT 2 grid header file and reading a GeoPAT 2 text outputs.

Mass rollup for a Bill of Materials is an example of a class of computations in which elements are arranged in a tree structure and some property of each element is a computed function of the corresponding values of its child elements. Leaf elements, i.e., those with no children, have values assigned. In many cases, the combining function is simple arithmetic sum; in other cases (e.g., mass properties), the combiner may involve other information such as the geometric relationship between parent and child, or statistical relations such as root-sum-of-squares (RSS). This package implements a general function for such problems. It is adapted to specific recursive computations by functional programming techniques; the caller passes a function as the update parameter to rollup() (or, at a lower level, passes functions as the get, set, combine, and override parameters to update_prop()) at runtime to specify the desired operations. The implementation relies on graph-theoretic algorithms from the igraph package of Csárdi, et al. (2006 <doi:10.5281/zenodo.7682609>).

Examples for Seamless R and C++ integration The Rcpp package contains a C++ library that facilitates the integration of R and C++ in various ways. This package provides some usage examples. Note that the documentation in this package currently does not cover all the features in the package. The site <https://gallery.rcpp.org> regroups a large number of examples for Rcpp'.

Interface to the ZeroMQ lightweight messaging kernel (see <https://zeromq.org/> for more information).

As an advanced approach to computerized adaptive testing (CAT), shadow testing (van der Linden(2005) <doi:10.1007/0-387-29054-0>) dynamically assembles entire shadow tests as a part of selecting items throughout the testing process. Selecting items from shadow tests guarantees the compliance of all content constraints defined by the blueprint. RSCAT is an R package for the shadow-test approach to CAT. The objective of RSCAT is twofold: 1) Enhancing the effectiveness of shadow-test CAT simulation; 2) Contributing to the academic and scientific community for CAT research. RSCAT is currently designed for dichotomous items based on the three-parameter logistic (3PL) model.

It helps you to read (.dim) images with CRS directly into R programming. One can import both Sentinel 1 and 2 images or any processed data with this software.

Reallocating the respective lessons by hours (respecting the constraints induced by the existence of coupled lessons) so that the total number of gaps is as small as possible.

This package provides a simple user-friendly library based on the python module reservoirpy'. It provides a flexible interface to implement efficient Reservoir Computing (RC) architectures with a particular focus on Echo State Networks (ESN). Some of its features are: offline and online training, parallel implementation, sparse matrix computation, fast spectral initialization, advanced learning rules (e.g. Intrinsic Plasticity) etc. It also makes possible to easily create complex architectures with multiple reservoirs (e.g. deep reservoirs), readouts, and complex feedback loops. Moreover, graphical tools are included to easily explore hyperparameters. Finally, it includes several tutorials exploring time series forecasting, classification and hyperparameter tuning. For more information about reservoirpy', please see Trouvain et al. (2020) <doi:10.1007/978-3-030-61616-8_40>. This package was developed in the framework of the University of Bordeauxâ s IdEx "Investments for the Future" program / RRI PHDS.