This package provides a client for the WebDriver API'. It allows driving a (probably headless) web browser, and can be used to test web applications, including Shiny apps. In theory it works with any WebDriver implementation, but it was only tested with PhantomJS'.

Utility functions to convert between the Spatial classes specified by the package sp', and the well-known binary (WKB) representation for geometry specified by the Open Geospatial Consortium'. Supports Spatial objects of class SpatialPoints', SpatialPointsDataFrame', SpatialLines', SpatialLinesDataFrame', SpatialPolygons', and SpatialPolygonsDataFrame'. Supports WKB geometry types Point', LineString', Polygon', MultiPoint', MultiLineString', and MultiPolygon'. Includes extensions to enable creation of maps with TIBCO Spotfire'.

Analyze given data frame with multiple endpoints and return Kaplan-Meier survival probabilities together with the specified confidence interval. See Nabipoor M, Westerhout CM, Rathwell S, and Bakal JA (2023) <doi:10.1186/s12874-023-01857-0>.

Computation of the Wasserstein Bipolarization Index as described in Lee and Sobel (Forthcoming) <doi:10.48550/arXiv.2408.03331>. Provides both asymptotic (Sommerfeld, 2017 <https://ediss.uni-goettingen.de/bitstream/handle/11858/00-1735-0000-0023-3FA1-C/DissertationSommerfeldRev.pdf?sequence=1>) and bootstrap methods (Efron and Narasimhan, 2020 <doi:10.1080/10618600.2020.1714633>) for calculating confidence intervals.

This package provides a comprehensive suite of functions for processing, analyzing, and visualizing textual data from tweets is offered. Users can clean tweets, analyze their sentiments, visualize data, and examine the correlation between sentiments and environmental data such as weather conditions. Main features include text processing, sentiment analysis, data visualization, correlation analysis, and synthetic data generation. Text processing involves cleaning and preparing tweets by removing textual noise and irrelevant words. Sentiment analysis extracts and accurately analyzes sentiments from tweet texts using advanced algorithms. Data visualization creates various charts like word clouds and sentiment polarity graphs for visual representation of data. Correlation analysis examines and calculates the correlation between tweet sentiments and environmental variables such as weather conditions. Additionally, random tweets can be generated for testing and evaluating the performance of analyses, empowering users to effectively analyze and interpret Twitter data for research and commercial purposes.

Time series outlier detection with non parametric test. This is a new outlier detection methodology (washer): efficient for time saving elaboration and implementation procedures, adaptable for general assumptions and for needing very short time series, reliable and effective as involving robust non parametric test. You can find two approaches: single time series (a vector) and grouped time series (a data frame). For other informations: Andrea Venturini (2011) Statistica - Universita di Bologna, Vol.71, pp.329-344. For an informal explanation look at R-bloggers on web.

This package provides API access to the Walmart Open API <https://developer.walmartlabs.com/>, that contains data about stores, Value of the day and products which includes names, sale prices, shipping rates and taxonomies.

Computes inequality measures of a given variable taking into account weights. Suitable for ratio, interval and ordered scale. Includes Gini, Theil, Leti index, Palma ratio, 20:20 ratio, Allison and Foster index, Jenkins index, Cowell and Flechaire index, Abul Naga and Yalcin index, Apouey index, Blair and Lacy index. Bootstrap provides distribution of inequality measures enabling significance tests.

This package provides a shiny application that helps learners connect regression tables to fitted generalised linear models. Users construct models via drag-and-drop controls, obtain fitted equations and plain-language explanations generated by a large language model, and can view plots of the fitted model in settings with a single continuous covariate.

R is used by a vast array of people for a vast array of purposes - including web analytics. This package contains functions for consuming and munging various common forms of request log, including the Common and Combined Web Log formats and various Amazon access logs.

This package provides a hierarchy of classes and methods for manipulating matrices formed implicitly from the sums of the inverses of other matrices, a situation commonly encountered in spatial statistics and related fields. Enables easy use of the Woodbury matrix identity and the matrix determinant lemma to allow computation (e.g., solving linear systems) without having to form the actual matrix. More information on the underlying linear algebra can be found in Harville, D. A. (1997) <doi:10.1007/b98818>.

Weighted versions of common descriptive statistics (variance, standard deviation, covariance, correlation, quantiles).

Access and analyze the World Bank's World Development Indicators (WDI) using the corresponding API <https://datahelpdesk.worldbank.org/knowledgebase/articles/889392-about-the-indicators-api-documentation>. WDI provides more than 24,000 country or region-level indicators for various contexts. wbwdi enables users to download, process and work with WDI series across multiple countries, aggregates, and time periods.

This package provides a computationally efficient way of fitting weighted linear fixed effects estimators for causal inference with various weighting schemes. Weighted linear fixed effects estimators can be used to estimate the average treatment effects under different identification strategies. This includes stratified randomized experiments, matching and stratification for observational studies, first differencing, and difference-in-differences. The package implements methods described in Imai and Kim (2017) "When should We Use Linear Fixed Effects Regression Models for Causal Inference with Longitudinal Data?", available at <https://imai.fas.harvard.edu/research/FEmatch.html>.

This package performs an analysis of time-to-event clinical trial data using various "win time" methods, including ewt', ewtr', rmt', ewtp', rewtp', ewtpr', rewtpr', max', wtr', rwtr', pwt', and rpwt'. These methods are used to calculate and compare treatment effects on ordered composite endpoints. The package handles event times, event indicators, and treatment arm indicators and supports calculations on observed and resampled data. Detailed explanations of each method and usage examples are provided in "Use of win time for ordered composite endpoints in clinical trials," by Troendle et al. (2024)<doi:10.1002/sim.10045>. For more information, see the package documentation or the vignette titled "Introduction to wintime.".

Within-subject mediation analysis using structural equation modeling. Examine how changes in an outcome variable between two conditions are mediated through one or more variables. Supports within-subject mediation analysis using the lavaan package by Rosseel (2012) <doi:10.18637/jss.v048.i02>, and extends Monte Carlo confidence interval estimation to missing data scenarios using the semmcci package by Pesigan and Cheung (2023) <doi:10.3758/s13428-023-02114-4>.

Graphical data analysis of accelerated life tests. Methods derived from Wayne Nelson (1990, ISBN: 9780471522775), William Q. Meeker and Lois A. Escobar (1998, ISBN: 1-471-14328-6).

The Model Disability Survey (MDS) <https://www.who.int/activities/collection-of-data-on-disability> is a World Health Organization (WHO) general population survey instrument to assess the distribution of disability within a country or region, grounded in the International Classification of Functioning, Disability and Health <https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health>. This package provides fit-for-purpose functions for calculating and presenting the results from this survey, as used by the WHO. The package primarily provides functions for implementing Rasch Analysis (see Andrich (2011) <doi:10.1586/erp.11.59>) to calculate a metric scale for disability.

Allows users to create weighted confusion matrices and accuracy metrics that help with the model selection process for classification problems, where distance from the correct category is important. The package includes several weighting schemes which can be parameterized, as well as custom configuration options. Furthermore, users can decide whether they wish to positively or negatively affect the accuracy score as a result of applying weights to the confusion matrix. Functions are included to calculate accuracy metrics for imbalanced data. Finally, wconf integrates well with the caret package, but it can also work standalone when provided data in matrix form. References: Kuhn, M. (2008) "Building Perspective Models in R Using the caret Package" <doi:10.18637/jss.v028.i05> Monahov, A. (2021) "Model Evaluation with Weighted Threshold Optimization (and the mewto R package)" <doi:10.2139/ssrn.3805911> Monahov, A. (2024) "Improved Accuracy Metrics for Classification with Imbalanced Data and Where Distance from the Truth Matters, with the wconf R Package" <doi:10.2139/ssrn.4802336> Starovoitov, V., Golub, Y. (2020). New Function for Estimating Imbalanced Data Classification Results. Pattern Recognition and Image Analysis, 295â 302 Van de Velden, M., Iodice D'Enza, A., Markos, A., Cavicchia, C. (2023) "A general framework for implementing distances for categorical variables" <doi:10.48550/arXiv.2301.02190>.

The main functionalities of wrappedtools are: adding backticks to variable names; rounding to desired precision with special case for p-values; selecting columns based on pattern and storing their position, name, and backticked name; computing and formatting of descriptive statistics (e.g. mean±SD), comparing groups and creating publication-ready tables with descriptive statistics and p-values; creating specialized plots for correlation matrices. Functions were mainly written for my own daily work or teaching, but may be of use to others as well.

Estimates high-dimensional multivariate normal copula regression models with the weighted composite likelihood estimating equations in Nikoloulopoulos (2023) <doi:10.1016/j.csda.2022.107654>. It provides autoregressive moving average correlation structures and binary, ordinal, Poisson, and negative binomial regressions.

Assortativity coefficients, centrality measures, and clustering coefficients for weighted and directed networks. Rewiring unweighted networks with given assortativity coefficients. Generating general preferential attachment networks.

This method generates a tour path by interpolating between d-D frames in p-D using Givens rotations. The algorithm arises from the problem of zeroing elements of a matrix. This interpolation method is useful for showing specific d-D frames in the tour, as opposed to d-D planes, as done by the geodesic interpolation. It is useful for projection pursuit indexes which are not s invariant. See more details in Buj, Cook, Asimov and Hurley (2005) <doi:10.1016/S0169-7161(04)24014-7> and Batsaikhan, Cook and Laa (2023) <doi:10.48550/arXiv.2311.08181>.

Four filters have been chosen namely haar', c6', la8', and bl14 (Kindly refer to wavelets in CRAN repository for more supported filters). Levels of decomposition are 2, 3, 4, etc. up to maximum decomposition level which is ceiling value of logarithm of length of the series base 2. For each combination two models are run separately. Results are stored in input'. First five metrics are expected to be minimum and last three metrics are expected to be maximum for a model to be considered good. Firstly, every metric value (among first five) is searched in every columns and minimum values are denoted as MIN and other values are denoted as NA'. Secondly, every metric (among last three) is searched in every columns and maximum values are denoted as MAX and other values are denoted as NA'. output contains the similar number of rows (which is 8) and columns (which is number filter-level combinations) as of input'. Values in output are corresponding NA', MIN or MAX'. Finally, the column containing minimum number of NA values is denoted as the best ('FL'). In special case, if two columns having equal NA', it has been checked among these two columns which one is having least NA in first five rows and has been inferred as the best. FL_metrics_values are the corresponding metrics values. WARIGAANbest is the data frame (dimension: 1*8) containing different metrics of the best filter-level combination. More details can be found in Garai and others (2023) <doi:10.13140/RG.2.2.11977.42087>.