Developed for model-based clustering using the finite mixtures of skewed sub-Gaussian stable distributions developed by Teimouri (2022) <arXiv:2205.14067> and estimating parameters of the symmetric stable distribution within the Bayesian framework.

Allows users to simulate matrix population models with particular characteristics based on aspects of life history such as mortality trajectories and fertility trajectories. Also allows the exploration of sampling error due to small sample size.

Highly variable gene selection methods, including popular public available methods, and also the mixture of multiple highly variable gene selection methods, <https://github.com/RuzhangZhao/mixhvg>. Reference: <doi:10.1101/2024.08.25.608519>.

The main function MMEst() performs (Restricted) Maximum Likelihood in a variance component mixed models using a Min-Max (MM) algorithm (Laporte, F., Charcosset, A. & Mary-Huard, T. (2022) <doi:10.1371/journal.pcbi.1009659>).

This function allows to generate two biological conditions synthetic microarray dataset which has similar behavior to those currently observed with common platforms. User provides a subset of parameters. Available default parameters settings can be modified.

Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions modeling oriented towards flow-cytometry data preprocessing applications. Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.

This package provides functions to compute and plot power levels, minimum detectable effect sizes, and minimum required sample sizes for the test of the overall average effect size in meta-analysis of dependent effect sizes.

M-estimator for threshold and non-threshold spatial dynamic panel data model. Yang, Z (2018) <doi:10.1016/j.jeconom.2017.08.019>. Wu, J., Matsuda, Y (2021) <doi:10.1007/s43071-021-00008-1>.

Simulates data from model objects (e.g., from lm(), glm()), and plots this along with the original data to compare how well the simulated data matches the original data to determine model fit.

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>.

Truncation of univariate probability distributions. The probability distribution can come from other packages so long as the function names follow the standard d, p, q, r naming format. Also other univariate probability distributions are included.

This package performs Thresholded Ordered Sparse Canonical Correlation Analysis (CCA). For more details see Senar, N. (2024) <doi:10.1093/bioadv/vbae021> and Senar, N. et al. (2025) <doi:10.48550/arXiv.2503.15140>.

Simplifies access to Tunisian government open data from <https://data.gov.tn/fr/>. Queries datasets by theme, author, or keywords, retrieves metadata, and gets structured results ready for analysis; all through the official CKAN API.

This package provides a convenient interface for constructing plots to visualize the fit of regression models arising from a wide variety of models in R ('lm', glm', coxph', rlm', gam', locfit', lmer', randomForest', etc.).

Visualizing of distributions of covariance matrices. The package implements the methodology described in Tokuda, T., Goodrich, B., Van Mechelen, I., Gelman, A., & Tuerlinckx, F. (2012) <https://sites.stat.columbia.edu/gelman/research/unpublished/Visualization.pdf>.

Generates Realizations of First-Order Integer Valued Autoregressive Processes with Zero-Inflated Innovations (ZINAR(1)) and Estimates its Parameters as described in Garay et al. (2021) <doi:10.1007/978-3-030-82110-4_2>.

Risk-related information (like the prevalence of conditions, the sensitivity and specificity of diagnostic tests, or the effectiveness of interventions or treatments) can be expressed in terms of frequencies or probabilities. By providing a toolbox of corresponding metrics and representations, riskyr computes, translates, and visualizes risk-related information in a variety of ways. Adopting multiple complementary perspectives provides insights into the interplay between key parameters and renders teaching and training programs on risk literacy more transparent (see <doi:10.3389/fpsyg.2020.567817>, for details).

Developed to assist researchers with planning analysis, prior to obtaining data from Trusted Research Environments (TREs) also known as safe havens. With functionality to export and import marginal distributions as well as synthesise data, both with and without correlations from these marginal distributions. Using a multivariate cumulative distribution (COPULA). Additionally the International Stroke Trial (IST) is included as an example dataset under ODC-By licence Sandercock et al. (2011) <doi:10.7488/ds/104>, Sandercock et al. (2011) <doi:10.1186/1745-6215-12-101>.

This package implements the RecMap MP2 construction heuristic <doi:10.1109/INFVIS.2004.57>. This algorithm draws maps according to a given statistical value, e.g., election results, population, or epidemiological data. The basic idea of the RecMap algorithm is that each map region, e.g., different countries, is represented by a rectangle. The area of each rectangle represents the statistical value provided as input to maintain zero cartographic error. Computationally intensive tasks are implemented in C++. The included vignette documents recmap algorithm usage.

Empirical orthogonal teleconnections in R. remote is short for R(-based) EMpirical Orthogonal TEleconnections'. It implements a collection of functions to facilitate empirical orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections (EOTs) denote a regression based approach to decompose spatio-temporal fields into a set of independent orthogonal patterns. They are quite similar to Empirical Orthogonal Functions (EOFs) with EOTs producing less abstract results. In contrast to EOFs, which are orthogonal in both space and time, EOT analysis produces patterns that are orthogonal in either space or time.

This is a package for semi-supervised isoform detection and annotation from both bulk and single-cell long read RNA-seq data. Flames provides automated pipelines for analysing isoforms, as well as intermediate functions for manual execution.

This package provides an API for efficient .hic file data extraction with programmatic matrix access. It doesn't store the pointer data for all the matrices, only the one queried, and currently it only supports matrices.

PrInCE (Predicting Interactomes from Co-Elution) uses a naive Bayes classifier trained on dataset-derived features to recover protein-protein interactions from co-elution chromatogram profiles. This package contains the R implementation of PrInCE.

Build and control interactive 2D and 3D maps with R/Shiny'. Lean set of powerful commands wrapping native calls to AMap <https://lbs.amap.com/api/jsapi-v2/summary/>. Deliver rich mapping functionality with minimal overhead.