This package provides a powerful and flexible tool for visualizing proportional data across spatially resolved contexts. By combining the concepts of scatter plots and stacked bar charts, scatterbar allows users to create scattered bar chart plots, which effectively display the proportions of different categories at each (x, y) location. This visualization is particularly useful for applications where understanding the distribution of categories across spatial coordinates is essential. This package features automatic determination of optimal scaling factors based on data, customizable scaling and padding options for both x and y axes, flexibility to specify custom colors for each category, options to customize the legend title, and integration with ggplot2 for robust and high-quality visualizations. For more details, see Velazquez et al. (2024) <doi:10.1101/2024.08.14.606810>.

This package provides the SMOTE with Boosting (SMOTEWB) algorithm. See F. SaÄ lam, M. A. Cengiz (2022) <doi:10.1016/j.eswa.2022.117023>. It is a SMOTE-based resampling technique which creates synthetic data on the links between nearest neighbors. SMOTEWB uses boosting weights to determine where to generate new samples and automatically decides the number of neighbors for each sample. It is robust to noise and outperforms most of the alternatives according to Matthew Correlation Coefficient metric. Alternative resampling methods are also available in the package.

Function for the GUI API to interact with external IDE/code editors.

Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation. (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>, Breiman, L. (1996), <doi:10.1007/BF00058655>, Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>, Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>, Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>, Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).

Animal movement models including Moving-Resting Process with Embedded Brownian Motion (Yan et al., 2014, <doi:10.1007/s10144-013-0428-8>; Pozdnyakov et al., 2017, <doi:10.1007/s11009-017-9547-6>), Brownian Motion with Measurement Error (Pozdnyakov et al., 2014, <doi:10.1890/13-0532.1>), Moving-Resting-Handling Process with Embedded Brownian Motion (Pozdnyakov et al., 2020, <doi:10.1007/s11009-020-09774-1>), Moving-Resting Process with Measurement Error (Hu et al., 2021, <doi:10.1111/2041-210X.13694>), Moving-Moving Process with two Embedded Brownian Motions.

Monte Carlo simulations of a game-theoretic model for the legal exemption system of the European cartel law are implemented in order to estimate the (mean) deterrent effect of this system. The input and output parameters of the simulated cartel opportunities can be visualized by three-dimensional projections. A description of the model is given in Moritz et al. (2018) <doi:10.1515/bejeap-2017-0235>.

This package provides a rendering tool for parameterized SQL that also translates into different SQL dialects. These dialects include Microsoft SQL Server', Oracle', PostgreSql', Amazon RedShift', Apache Impala', IBM Netezza', Google BigQuery', Microsoft PDW', Snowflake', Azure Synapse Analytics Dedicated', Apache Spark', SQLite', and InterSystems IRIS'.

Collection of custom input controls and user interface components for Shiny applications. Give your applications a unique and colorful style !

Spatial statistical modeling and prediction for data on stream networks, including models based on in-stream distance (Ver Hoef, J.M. and Peterson, E.E., (2010) <DOI:10.1198/jasa.2009.ap08248>.) Models are created using moving average constructions. Spatial linear models, including explanatory variables, can be fit with (restricted) maximum likelihood. Mapping and other graphical functions are included.

Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs.

The cartogram heatmaps generated by the included methods are an alternative to choropleth maps for the United States and are based on work by the Washington Post graphics department in their report on "The states most threatened by trade" (<http://www.washingtonpost.com/wp-srv/special/business/states-most-threatened-by-trade/>). "State bins" preserve as much of the geographic placement of the states as possible but have the look and feel of a traditional heatmap. Functions are provided that allow for use of a binned, discrete scale, a continuous scale or manually specified colors depending on what is needed for the underlying data.

Fit univariate right, left or interval censored regression model under the scale mixture of normal distributions.

This package provides tools for the optimization of stratified sampling design. It determines a stratification of a sampling frame that minimizes sample cost while satisfying precision constraints in a multivariate and multidomain context. The approach relies on a genetic algorithm; each candidate partition of the frame is an individual whose fitness is evaluated via the Bethel-Chromy allocation to meet target precisions. Functions support analysis of optimization results, labeling of the frame with new strata, and drawing a sample according to the optimal allocation. Algorithmic components adapt code from the genalg package. See M. Ballin and G. Barcaroli (2020) "R package SamplingStrata: new developments and extension to Spatial Sampling" <doi:10.48550/arXiv.2004.09366>.

This package provides elastic net penalized maximum likelihood estimator for structural equation models (SEM). The package implements `lasso` and `elastic net` (l1/l2) penalized SEM and estimates the model parameters with an efficient block coordinate ascent algorithm that maximizes the penalized likelihood of the SEM. Hyperparameters are inferred from cross-validation (CV). A Stability Selection (STS) function is also available to provide accurate causal effect selection. The software achieves high accuracy performance through a `Network Generative Pre-trained Transformer` (Network GPT) Framework with two steps: 1) pre-trains the model to generate a complete (fully connected) graph; and 2) uses the complete graph as the initial state to fit the `elastic net` penalized SEM.

Facilitates basic and equation-based analyses of some important soil properties related to soil chemical environment and nutrient availability to plants. Freundlich H (1907). <doi:10.1515/zpch-1907-5723>. Datta SP, Bhadoria PBS (1999). <doi:10.1002%2F%28SICI%291522-2624%28199903%29162%3A2%3C183%3A%3AAID-JPLN183%3E3.0.CO%3B2-A>."Boron adsorption and desorption in some acid soils of West Bengal, India". Langmuir I (1918). <doi:10.1021/ja02242a004> "The adsorption of gases on plane surfaces of glass, mica, and platinum". Khasawneh FE (1971). <doi:10.2136/sssaj1971.03615995003500030029x> "Solution ion activity and plant growth".

The straightforward filtering index (SFINX) identifies true positive protein interactions in a fast, user-friendly, and highly accurate way. It is not only useful for the filtering of affinity purification - mass spectrometry (AP-MS) data, but also for similar types of data resulting from other co-complex interactomics technologies, such as TAP-MS, Virotrap and BioID. SFINX can also be used via the website interface at <http://sfinx.ugent.be>.

This package provides movies to help students to understand statistical concepts. The rpanel package <https://cran.r-project.org/package=rpanel> is used to create interactive plots that move to illustrate key statistical ideas and methods. There are movies to: visualise probability distributions (including user-supplied ones); illustrate sampling distributions of the sample mean (central limit theorem), the median, the sample maximum (extremal types theorem) and (the Fisher transformation of the) product moment correlation coefficient; examine the influence of an individual observation in simple linear regression; illustrate key concepts in statistical hypothesis testing. Also provided are dpqr functions for the distribution of the Fisher transformation of the correlation coefficient under sampling from a bivariate normal distribution.

Median-of-means is a generic yet powerful framework for scalable and robust estimation. A framework for Bayesian analysis is called M-posterior, which estimates a median of subset posterior measures. For general exposition to the topic, see the paper by Minsker (2015) <doi:10.3150/14-BEJ645>.

This package provides convenient snapshot testing functions for packages, including expect_snapshot_data() for data.frames and expect_snapshot_object() for any R object.

Toolbox for different kinds of spatio-temporal analyses to be performed on observed point patterns, following the growing stream of literature on point process theory. This R package implements functions to perform different kinds of analyses on point processes, proposed in the papers (Siino, Adelfio, and Mateu 2018<doi:10.1007/s00477-018-1579-0>; Siino et al. 2018<doi:10.1002/env.2463>; Adelfio et al. 2020<doi:10.1007/s00477-019-01748-1>; Dâ Angelo, Adelfio, and Mateu 2021<doi:10.1016/j.spasta.2021.100534>; Dâ Angelo, Adelfio, and Mateu 2022<doi:10.1007/s00362-022-01338-4>; Dâ Angelo, Adelfio, and Mateu 2023<doi:10.1016/j.csda.2022.107679>). The main topics include modeling, statistical inference, and simulation issues on spatio-temporal point processes on Euclidean space and linear networks. Version 1.0.0 has been updated for accompanying the journal publication D Angelo and Adelfio 2025 <doi:10.18637/jss.v113.i10>.

Estimates correlation coefficients with associated confidence limits for bivariate, partially censored survival times. Uses the iterative multiple imputation approach proposed by Schemper, Kaider, Wakounig and Heinze (2013) <doi:10.1002/sim.5874>. Provides a scatterplot function to visualize the bivariate distribution, either on the original time scale or as copula.

This package provides a pipeline for the comparative analysis of collective movement data (e.g. fish schools, bird flocks, baboon troops) by processing 2-dimensional positional data (x,y,t) from GPS trackers or computer vision tracking systems, discretizing events of collective motion, calculating a set of established metrics that characterize each event, and placing the events in a multi-dimensional swarm space constructed from these metrics. The swarm space concept, the metrics and data sets included are described in: Papadopoulou Marina, Furtbauer Ines, O'Bryan Lisa R., Garnier Simon, Georgopoulou Dimitra G., Bracken Anna M., Christensen Charlotte and King Andrew J. (2023) <doi:10.1098/rstb.2022.0068>.

This package provides a simple function that anonymises a list of variables in a consistent way: anonymised factors are not recycled and the same original levels receive the same anonymised factor even if located in different datasets.

Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008).