This package provides methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) <doi:10.1016/j.catena.2023.106927> and Trevisani and Guth (2025) <doi:10.3390/rs17233864>). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface (i.e., without de-trending) using increments of order 2 and of order 4. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices.

This package performs inference of several model-free group contrast measures, which include difference/ratio of cumulative incidence rates at given time points, quantiles, and restricted mean survival times (RMST). Two kinds of covariate adjustment procedures (i.e., regression and augmentation) for inference of the metrics based on RMST are also included.

Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

This package provides tools for modeling non-continuous linear responses of ecological communities to environmental data. The package is straightforward through three steps: (1) data ordering (function OrdData()), (2) split-moving-window analysis (function SMW()) and (3) piecewise redundancy analysis (function pwRDA()). Relevant references include Cornelius and Reynolds (1991) <doi:10.2307/1941559> and Legendre and Legendre (2012, ISBN: 9780444538697).

Efficient implementations for Sorted L-One Penalized Estimation (SLOPE): generalized linear models regularized with the sorted L1-norm (Bogdan et al. 2015). Supported models include ordinary least-squares regression, binomial regression, multinomial regression, and Poisson regression. Both dense and sparse predictor matrices are supported. In addition, the package features predictor screening rules that enable fast and efficient solutions to high-dimensional problems.

Calculates a degree of spatial association between regionalizations or categorical maps using the information-theoretical V-measure (Nowosad and Stepinski (2018) <doi:10.1080/13658816.2018.1511794>). It also offers an R implementation of the MapCurve method (Hargrove et al. (2006) <doi:10.1007/s10109-006-0025-x>).

Hierarchical models for the analysis of species-area relationships (SARs) by combining several data sets and covariates; with a global data set combining individual SAR studies; as described in Solymos and Lele (2012) <doi:10.1111/j.1466-8238.2011.00655.x>.

Integrating a stratified structure in the population in a sampling design can considerably reduce the variance of the Horvitz-Thompson estimator. We propose in this package different methods to handle the selection of a balanced sample in stratified population. For more details see Raphaël Jauslin, Esther Eustache and Yves Tillé (2021) <doi:10.1007/s42081-021-00134-y>. The package propose also a method based on optimal transport and balanced sampling, see Raphaël Jauslin and Yves Tillé <doi:10.1016/j.jspi.2022.12.003>.

This package implements a thresholded version of the Sliced Inverse Regression method (Li, K. C. (1991) <doi:10.2307/2290563>), which allows to do variable selection.

Compare directories flexibly (by date, content, or both) and synchronize files efficiently, with asymmetric and symmetric modes, helper tools, and visualization support for file management.

The goal of the SwimmeR package is to provide means of acquiring, and then analyzing, data from swimming (and diving) competitions. To that end SwimmeR allows results to be read in from .html sources, like Hy-Tek real time results pages, .pdf files, ISL results, Omega results, and (on a development basis) .hy3 files. Once read in, SwimmeR can convert swimming times (performances) between the computationally useful format of seconds reported to the 100ths place (e.g. 95.37), and the conventional reporting format (1:35.37) used in the swimming community. SwimmeR can also score meets in a variety of formats with user defined point values, convert times between courses ('LCM', SCM', SCY') and draw single elimination brackets, as well as providing a suite of tools for working cleaning swimming data. This is a developmental package, not yet mature.

This package provides a simple, one-command package which runs an interactive dashboard capable of common visualizations for single cell RNA-seq. SeuratExplorer requires a processed Seurat object, which is saved as rds or qs2 file.

Ordinary and modified statistics for symmetrical linear regression models with small samples. The supported ordinary statistics include Wald, score, likelihood ratio and gradient. The modified statistics include score, likelihood ratio and gradient. Diagnostic tools associated with the fitted model are implemented. For more details see Medeiros and Ferrari (2017) <DOI:10.1111/stan.12107>.

This package provides functions for the stratigraphic analysis of phylogenetic trees.

Test and estimates of location, tests of independence, tests of sphericity and several estimates of shape all based on spatial signs, symmetrized signs, ranks and signed ranks. For details, see Oja and Randles (2004) <doi:10.1214/088342304000000558> and Oja (2010) <doi:10.1007/978-1-4419-0468-3>.

Routine that allows the user to run several goodness-of-fit tests. It also combines the tests and returns a properly adjusted family-wise p value. Details can be found in <arXiv:2007.04727>.

The saemix package implements the Stochastic Approximation EM algorithm for parameter estimation in (non)linear mixed effects models. It (i) computes the maximum likelihood estimator of the population parameters, without any approximation of the model (linearisation, quadrature approximation,...), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm, (ii) provides standard errors for the maximum likelihood estimator (iii) estimates the conditional modes, the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm (see Comets et al. (2017) <doi:10.18637/jss.v080.i03>). Many applications of SAEM in agronomy, animal breeding and PKPD analysis have been published by members of the Monolix group. The full PDF documentation for the package including references about the algorithm and examples can be downloaded on the github of the IAME research institute for saemix': <https://github.com/iame-researchCenter/saemix/blob/7638e1b09ccb01cdff173068e01c266e906f76eb/docsaem.pdf>.

Toolbox containing a variety of spectral clustering tools functions. Among the tools available are the hierarchical spectral clustering algorithm, the Shi and Malik clustering algorithm, the Perona and Freeman algorithm, the non-normalized clustering, the Von Luxburg algorithm, the Partition Around Medoids clustering algorithm, a multi-level clustering algorithm, recursive clustering and the fast method for all clustering algorithm. As well as other tools needed to run these algorithms or useful for unsupervised spectral clustering. This toolbox aims to gather the main tools for unsupervised spectral classification. See <http://mawenzi.univ-littoral.fr/> for more information and documentation.

This package provides a combined slider and numeric input for usage in a Shiny app. The slider and the numeric input are linked together: each one is updated when the other one changes. Many styling properties are customizable (e.g. colors and size).

Sample Generation by Replacement simulations (SGR; Lombardi & Pastore, 2014; Pastore & Lombardi, 2014). The package can be used to perform fake data analysis according to the sample generation by replacement approach. It includes functions for making simple inferences about discrete/ordinal fake data. The package allows to study the implications of fake data for empirical results.

This package provides tools for calculating disclosure risk measures for microdata, including record-level and file-level measures. The record-level disclosure risk is estimated primarily using exhaustive tabulation. The file-level disclosure risk is estimated by fitting loglinear models on the observed sample counts in cells formed by key variables and their interactions. Funded by the National Center for Education Statistics. See Skinner and Shlomo (2008) <doi:10.1198/016214507000001328> for a description of the file-level risk measures and the loglinear model approach.

This package provides functions to parse and analyze logs generated by ShinyProxy containers. It extracts metadata from log file names, reads log contents, and computes summary statistics (such as the total number of lines and lines containing error messages), facilitating efficient monitoring and debugging of ShinyProxy deployments.

This package provides ggplot2 extensions to construct glyph-maps for visualizing seasonality in spatiotemporal data. See the Journal of Statistical Software reference: Zhang, H. S., Cook, D., Laa, U., Langrené, N., & Menéndez, P. (2024) <doi:10.18637/jss.v110.i07>. The manuscript for this package is currently under preparation and can be found on GitHub at <https://github.com/maliny12/paper-sugarglider>.

This package contains various functions to be used for simulation education, including simple Monte Carlo simulation functions, queueing simulation functions, variate generation functions capable of producing independent streams and antithetic variates, functions for illustrating random variate generation for various discrete and continuous distributions, and functions to compute time-persistent statistics. Also contains functions for visualizing: event-driven details of a single-server queue model; a Lehmer random number generator; variate generation via acceptance-rejection; and of generating a non-homogeneous Poisson process via thinning. Also contains two queueing data sets (one fabricated, one real-world) to facilitate input modeling. More details on the use of these functions can be found in Lawson and Leemis (2015) <doi:10.1109/WSC.2017.8248124>, in Kudlay, Lawson, and Leemis (2020) <doi:10.1109/WSC48552.2020.9384010>, and in Lawson and Leemis (2021) <doi:10.1109/WSC52266.2021.9715299>.