This package provides a Shiny application designed to support the learning of basic concepts in statistics and probability. The tool provides an interactive interface that allows students to explore and visualize different statistical concepts intuitively, including descriptive statistics for continuous and qualitative variables, and probability distributions.

Implementation of uniformity tests on the circle and (hyper)sphere. The main function of the package is unif_test(), which conveniently collects more than 35 tests for assessing uniformity on S^p-1 = x in R^p : ||x|| = 1, p >= 2. The test statistics are implemented in the unif_stat() function, which allows computing several statistics for different samples within a single call, thus facilitating Monte Carlo experiments. Furthermore, the unif_stat_MC() function allows parallelizing them in a simple way. The asymptotic null distributions of the statistics are available through the function unif_stat_distr(). The core of sphunif is coded in C++ by relying on the Rcpp package. The package also provides several novel datasets and gives the replicability for the data applications/simulations in Garcà a-Portugués et al. (2021) <doi:10.1007/978-3-030-69944-4_12>, Garcà a-Portugués et al. (2023) <doi:10.3150/21-BEJ1454>, Fernández-de-Marcos and Garcà a-Portugués (2024) <doi:10.1016/j.spl.2024.110218>, and Garcà a-Portugués et al. (2025) <doi:10.1080/01621459.2025.2566414>.

This package provides a specialized selection algorithm designed to align simulated fire perimeters with specific fire size distribution scenarios. The foundation of this approach lies in generating a vast collection of plausible simulated fires across a wide range of conditions, assuming a random pattern of ignition. The algorithm then assembles individual fire perimeters based on their specific probabilities of occurrence, e.g., determined by (i) the likelihood of ignition and (ii) the probability of particular fire-weather scenarios, including wind speed and direction. Implements the method presented in Rodrigues (2025a) <doi:10.5194/egusphere-egu25-8974>. Demo data and code examples can be found in Rodrigues (2025b) <doi:10.5281/zenodo.15282605>.

This package performs the EM algorithm for regression models using Skew Scale Mixtures of Normal Distributions.

Explains the behavior of a time series by decomposing it into its trend, seasonality and residuals. It is built to perform very well in the presence of significant level shifts. It is designed to play well with any breakpoint algorithm and any smoothing algorithm. Currently defaults to lowess for smoothing and strucchange for breakpoint identification. The package is useful in areas such as trend analysis, time series decomposition, breakpoint identification and anomaly detection.

Studies otolith shape variation among fish populations. Otoliths are calcified structures found in the inner ear of teleost fish and their shape has been known to vary among several fish populations and stocks, making them very useful in taxonomy, species identification and to study geographic variations. The package extends previously described software used for otolith shape analysis by allowing the user to automatically extract closed contour outlines from a large number of images, perform smoothing to eliminate pixel noise described in Haines and Crampton (2000) <doi:10.1111/1475-4983.00148>, choose from conducting either a Fourier or wavelet see Gençay et al (2001) <doi:10.1016/S0378-4371(00)00463-5> transform to the outlines and visualize the mean shape. The output of the package are independent Fourier or wavelet coefficients which can be directly imported into a wide range of statistical packages in R. The package might prove useful in studies of any two dimensional objects.

Discovers synergistic gene pairs in single-cell RNA-seq and spatial transcriptomics data. Unlike conventional pairwise co-expression analyses that rely on a single correlation metric, scPairs integrates 14 complementary metrics across five orthogonal evidence layers to compute a composite synergy score with optional permutation-based significance testing. The five evidence layers span cell-level co-expression (Pearson, Spearman, biweight midcorrelation, mutual information, ratio consistency), neighbourhood-aware smoothing (KNN-smoothed correlation, neighbourhood co-expression, cluster pseudo-bulk, cross-cell-type, neighbourhood synergy), prior biological knowledge (GO/KEGG co-annotation Jaccard, pathway bridge score), trans-cellular interaction, and spatial co-variation (Lee's L, co-location quotient). This multi-scale design enables researchers to move beyond simple co-expression towards a comprehensive characterisation of cooperative gene regulation at transcriptomic and spatial resolution. For more information, see the package documentation at <https://github.com/zhaoqing-wang/scPairs>.

This package provides functionality for working with tensors, alternating forms, wedge products, Stokes's theorem, and related concepts from the exterior calculus. Uses disordR discipline (Hankin, 2022, <doi:10.48550/arXiv.2210.03856>). The canonical reference would be M. Spivak (1965, ISBN:0-8053-9021-9) "Calculus on Manifolds". To cite the package in publications please use Hankin (2022) <doi:10.48550/arXiv.2210.17008>.

This is a modification of HDoutliers package. The HDoutliers algorithm is a powerful unsupervised algorithm for detecting anomalies in high-dimensional data, with a strong theoretical foundation. However, it suffers from some limitations that significantly hinder its performance level, under certain circumstances. This package implements the algorithm proposed in Talagala, Hyndman and Smith-Miles (2019) <arXiv:1908.04000> for detecting anomalies in high-dimensional data that addresses these limitations of HDoutliers algorithm. We define an anomaly as an observation that deviates markedly from the majority with a large distance gap. An approach based on extreme value theory is used for the anomalous threshold calculation.

Reliability of (normal) stress-strength models and for building two-sided or one-sided confidence intervals according to different approximate procedures.

In a scatterplot where the response variable is Gaussian, Poisson or binomial, we consider the case in which the mean function is smooth with a change-point, which is a mode, an inflection point or a jump point. The main routine estimates the mean curve and the change-point as well using shape-restricted B-splines. An optional subroutine delivering a bootstrap confidence interval for the change-point is incorporated in the main routine.

Monte Carlo confidence intervals for free and defined parameters in models fitted in the structural equation modeling package lavaan can be generated using the semmcci package. semmcci has three main functions, namely, MC(), MCMI(), and MCStd(). The output of lavaan is passed as the first argument to the MC() function or the MCMI() function to generate Monte Carlo confidence intervals. Monte Carlo confidence intervals for the standardized estimates can also be generated by passing the output of the MC() function or the MCMI() function to the MCStd() function. A description of the package and code examples are presented in Pesigan and Cheung (2024) <doi:10.3758/s13428-023-02114-4>.

This package provides historical datasets related to John Snow's 1854 cholera outbreak study in London. Includes data on cholera cases, water pump locations, and the street layout, enabling analysis and visualisation of the outbreak.

This package provides option settings management that goes beyond R's default options function. With this package, users can define their own option settings manager holding option names, default values and (if so desired) ranges or sets of allowed option values that will be automatically checked. Settings can then be retrieved, altered and reset to defaults with ease. For R programmers and package developers it offers cloning and merging functionality which allows for conveniently defining global and local options, possibly in a multilevel options hierarchy. See the package vignette for some examples concerning functions, S4 classes, and reference classes. There are convenience functions to reset par() and options() to their factory defaults'.

Includes functions for interacting with common meta data fields, writing insert statements, calling functions, and more for T-SQL and Postgresql'.

Flexible implementation of the Standardized Ranking Performance Index (sRPI) for model selection based on multiple evaluation criteria. The package combines multiple statistical measures into a single index to provide an objective and robust ranking of models across calibration, validation, and combined scenarios. It supports evaluation of statistical, machine learning, and other predictive models using user-defined performance criteria. For more details see Aschonitis et al. (2019) <doi:10.1016/j.envsoft.2019.01.005> and Singh et al. (2023) <doi:10.1016/j.ecoinf.2022.101933>.

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.

This package provides a consistently well behaved method of interpolation based on piecewise rational functions using Stineman's algorithm.

Calculating home ranges and movements of animals in complex stream environments is often challenging, and standard home range estimators do not apply. This package provides a series of tools for assessing movements in a stream network, such as calculating the total length of stream used, distances between points, and movement patterns over time. See Vignette for additional details. This package was originally released on GitHub under the name SNM'. SNMA was developed for analyses in McKnight et al. (2025) <doi:10.3354/esr01442> which contains additional examples and information.

Read SubRip <https://sourceforge.net/projects/subrip/> subtitle files as data frames for easy text analysis or manipulation. Easily shift numeric timings and export subtitles back into valid SubRip timestamp format to sync subtitles and audio.

This package provides a collection of functions for estimating spatial regimes, aggregations of neighboring spatial units that are homogeneous in functional terms. The term spatial regime, therefore, should not be understood as a synonym for cluster. More precisely, the term cluster does not presuppose any functional relationship between the variables considered, while the term regime is linked to a regressive relationship underlying the spatial process.

This package performs hybrid multi-stage factor analytic procedure for controlling acquiescence in restricted solutions (Ferrando & Lorenzo-Seva, 2000 <https://www.uv.es/revispsi/articulos3.00/ferran7.pdf>).

Finds the vertex partition of an undirected graph that maximises a persistence-based objective, using the Milano local-search algorithm. Three related quality measures are supported: the persistence probability of a community, its null-adjusted persistence (NAP), and the scaled null-adjusted persistence (Scaled-NAP), which interpolates between NAP and modularity. The methods are described in Avellone et al. (2023) <doi:10.1007/s10288-023-00559-z> and Avellone et al. (2025) <doi:10.1016/j.ins.2025.123032>.

Provide various functions and tools to help fit models for estimating treatment effects in stepped wedge cluster randomized trials. Implements methods described in Kenny, Voldal, Xia, and Heagerty (2022) "Analysis of stepped wedge cluster randomized trials in the presence of a time-varying treatment effect", <doi:10.1002/sim.9511>.