The Sparse Marginal Epistasis Test is a computationally efficient genetics method which detects statistical epistasis in complex traits; see Stamp et al. (2025, <doi:10.1101/2025.01.11.632557>) for details.

This package provides a collection of various techniques correcting statistical models for sample selection bias is provided. In particular, the resampling-based methods "stochastic inverse-probability oversampling" and "parametric inverse-probability bagging" are placed at the disposal which generate synthetic observations for correcting classifiers for biased samples resulting from stratified random sampling. For further information, see the article Krautenbacher, Theis, and Fuchs (2017) <doi:10.1155/2017/7847531>. The methods may be used for further purposes where weighting and generation of new observations is needed.

This package provides a tool for working with SQLite databases. SQLite has some idiosyncrasies and limitations that impose some hurdles to the R developer who is using this database as a repository. For instance, SQLite doesn't have a date type and sqliteutils has some functions to deal with that.

Make interactive d3.js sequence sunburst diagrams in R with the convenience and infrastructure of an htmlwidget'.

It can be useful to temporarily hide some text or other HTML elements in Shiny applications. Building on Spoiler-Alert.js', it is possible to select the elements to hide at startup, to partially reveal them by hovering them, and to completely show them when clicking on them.

This package performs two-sample comparisons using the restricted mean survival time (RMST) as a summary measure of the survival time distribution. Three kinds of between-group contrast metrics (i.e., the difference in RMST, the ratio of RMST and the ratio of the restricted mean time lost (RMTL)) are computed. It performs an ANCOVA-type covariate adjustment as well as unadjusted analyses for those measures.

This package provides a set of functions that can be used to spatially thin species occurrence data. The resulting thinned data can be used in ecological modeling, such as ecological niche modeling.

This package provides a set of functions allowing to implement the SpiceFP approach which is iterative. It involves transformation of functional predictors into several candidate explanatory matrices (based on contingency tables), to which relative edge matrices with contiguity constraints are associated. Generalized Fused Lasso regression are performed in order to identify the best candidate matrix, the best class intervals and related coefficients at each iteration. The approach is stopped when the maximal number of iterations is reached or when retained coefficients are zeros. Supplementary functions allow to get coefficients of any candidate matrix or mean of coefficients of many candidates. The methods in this package are describing in Girault Gnanguenon Guesse, Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021) "An exploratory penalized regression to identify combined effects of functional variables -Application to agri-environmental issues" <https://hal.archives-ouvertes.fr/hal-03298977>.

The heterogeneity of spatial data presenting a finite number of categories can be measured via computation of spatial entropy. Functions are available for the computation of the main entropy and spatial entropy measures in the literature. They include the traditional version of Shannon's entropy (Shannon, 1948 <doi:10.1002/j.1538-7305.1948.tb01338.x>), Batty's spatial entropy (Batty, 1974 <doi:10.1111/j.1538-4632.1974.tb01014.x>), O'Neill's entropy (O'Neill et al., 1998 <doi:10.1007/BF00162741>), Li and Reynolds contagion index (Li and Reynolds, 1993 <doi:10.1007/BF00125347>), Karlstrom and Ceccato's entropy (Karlstrom and Ceccato, 2002 <https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-61351>), Leibovici's entropy (Leibovici, 2009 <doi:10.1007/978-3-642-03832-7_24>), Parresol and Edwards entropy (Parresol and Edwards, 2014 <doi:10.3390/e16041842>) and Altieri's entropy (Altieri et al., 2018, <doi:10.1007/s10651-017-0383-1>). Full references for all measures can be found under the topic SpatEntropy'. The package is able to work with lattice and point data. The updated version works with the updated spatstat package (>= 3.0-2).

This package provides Markov Chain Monte Carlo (MCMC) routine for the structural equation modelling described in Maity et. al. (2020) <doi:10.1093/bioinformatics/btaa286>. This MCMC sampler is useful when one attempts to perform an integrative survival analysis for multiple platforms of the Omics data where the response is time to event and the predictors are different omics expressions for different platforms.

Routines for solving large systems of linear equations and eigenproblems in R. Direct and iterative solvers from the Eigen C++ library are made available. Solvers include Cholesky, LU, QR, and Krylov subspace methods (Conjugate Gradient, BiCGSTAB). Dense and sparse problems are supported.

Cluster-independent method based on topology structure of gene co-expression network for identifying feature gene sets, extracting cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Without prior cell clustering, SifiNet circumvents potential inaccuracies in clustering that may influence subsequent analyses. This method is introduced in Qi Gao, Zhicheng Ji, Liuyang Wang, Kouros Owzar, Qi-Jing Li, Cliburn Chan, Jichun Xie "SifiNet: a robust and accurate method to identify feature gene sets and annotate cells" (2024) <doi:10.1093/nar/gkae307>.

This package provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).

Modifies the progress() function from httr package to let it send output to progressBar() function from shinyWidgets package. It is just a tweak at the original functions from httr package to make it smooth for shiny developers.

Data processing and visualizations for rodent vocalizations exported from DeepSqueak'. These functions are compatible with the SqueakR Shiny Dashboard, which can be used to visualize experimental results and analyses.

An R Shiny application dedicated to the intra-site spatial analysis of piece-plotted archaeological remains, making the two and three-dimensional spatial exploration of archaeological data as user-friendly as possible. Documentation about SEAHORS is provided by the vignette included in this package and by the companion scientific paper: Royer, Discamps, Plutniak, Thomas (2023, PCI Archaeology, <doi:10.5281/zenodo.7674698>).

This package provides interface to sparsepp - fast, memory efficient hash map. It is derived from Google's excellent sparsehash implementation. We believe sparsepp provides an unparalleled combination of performance and memory usage, and will outperform your compiler's unordered_map on both counts. Only Google's dense_hash_map is consistently faster, at the cost of much greater memory usage (especially when the final size of the map is not known in advance).

Using principal component analysis as a base model, SCOUTer offers a new approach to simulate outliers in a simple and precise way. The user can generate new observations defining them by a pair of well-known statistics: the Squared Prediction Error (SPE) and the Hotelling's T^2 (T^2) statistics. Just by introducing the target values of the SPE and T^2, SCOUTer returns a new set of observations with the desired target properties. Authors: Alba González, Abel Folch-Fortuny, Francisco Arteaga and Alberto Ferrer (2020).

Allows you to make clean, good-looking scatter plots with the option to easily add marginal density or box plots on the axes. It is also available as a module for jamovi (see <https://www.jamovi.org> for more information). Scatr is based on the cowplot package by Claus O. Wilke and the ggplot2 package by Hadley Wickham.

This package provides machine-readable access to parliamentary data of the Swiss Federal Assembly via the OData interface (<https://ws.parlament.ch/odata.svc/>) and the OpenParlData REST API (<https://api.openparldata.ch>), which also offers harmonized data for selected cantonal and municipal parliaments.

Connect to a remote server over SSH to transfer files via SCP, setup a secure tunnel, or run a command or script on the host while streaming stdout and stderr directly to the client.

This package implements a three-dimensional stochastic model of cancer growth and mutation similar to the one described in Waclaw et al. (2015) <doi:10.1038/nature14971>. Allows for interactive 3D visualizations of the simulated tumor. Provides a comprehensive summary of the spatial distribution of mutants within the tumor. Contains functions which create synthetic sequencing datasets from the generated tumor.

The computation of a seasonal index is a fundamental step in time-series forecasting when the data exhibits seasonality. Specifically, a seasonal index quantifies â for each season (e.g. month, quarter, week) â the relative magnitude of the seasonal effect compared to the overall average level of the series. This package has been developed to compute seasonal index for time series data and it also seasonalise and desesaonalise the time series data.

Combining Predictive Analytics and Experimental Design to Optimize Results. To be utilized to select a test data calibrated training population in high dimensional prediction problems and assumes that the explanatory variables are observed for all of the individuals. Once a "good" training set is identified, the response variable can be obtained only for this set to build a model for predicting the response in the test set. The algorithms in the package can be tweaked to solve some other subset selection problems.