Estimating parameters of site clusters on 2D & 3D square lattice with various lattice sizes, relative fractions of open sites (occupation probability), iso- & anisotropy, von Neumann & Moore (1,d)-neighborhoods, described by Moskalev P.V. et al. (2011) <arXiv:1105.2334v1>.

This package provides functions that can be used to calculate time-dependent state and parameter sensitivities for both continuous- and discrete-time deterministic models. See Ng et al. (2023) <doi:10.1086/726143> for more information about time-dependent sensitivity analysis.

This package provides a collection of functions for visualizing,exploring and annotating genetic association results.Association results from multiple traits can be viewed simultaneously along with gene annotation, over the entire genome (Manhattan plot) or in the more detailed regional view.

The tinytest package offers a light-weight zero-dependency unit-testing framework to which this package adds support via the diffobj package for diff'-style textual comparison of R objects, as well as via tinysnapshot package for visual differences in plots.

The purpose of this package is to provide methods to interpret multiple linear regression and canonical correlation results including beta weights,structure coefficients, validity coefficients, product measures, relative weights, all-possible-subsets regression, dominance analysis, commonality analysis, and adjusted effect sizes.

This package provides a two-part zero-inflated Beta regression model with random effects (ZIBR) for testing the association between microbial abundance and clinical covariates for longitudinal microbiome data. Eric Z. Chen and Hongzhe Li (2016) <doi:10.1093/bioinformatics/btw308>.

The Rcpp package provides R functions as well as C++ classes which offer a seamless integration of R and C++. Many R data types and objects can be mapped back and forth to C++ equivalents which facilitates both writing of new code as well as easier integration of third-party libraries. Documentation about Rcpp is provided by several vignettes included in this package, via the Rcpp Gallery site at <http://gallery.rcpp.org>, the paper by Eddelbuettel and Francois (2011, JSS), and the book by Eddelbuettel (2013, Springer); see citation("Rcpp") for details on these last two.

Efficient framework for ridge redundancy analysis (rrda), tailored for high-dimensional omics datasets where the number of predictors exceeds the number of samples. The method leverages Singular Value Decomposition (SVD) to avoid direct inversion of the covariance matrix, enhancing scalability and performance. It also introduces a memory-efficient storage strategy for coefficient matrices, enabling practical use in large-scale applications. The package supports cross-validation for selecting regularization parameters and reduced-rank dimensions, making it a robust and flexible tool for multivariate analysis in omics research. Please refer to our article (Yoshioka et al., 2025) for more details.

This package provides functions for reading array comparative genomic hybridization (aCGH) data from image analysis output files and clone information files, creation of aCGH objects for storing these data. Basic methods are accessing/replacing, subsetting, printing and plotting aCGH objects.

R-scape discovers RNA secondary structure consensus elements. These elements include riboswitches and ribozymes. It utilizes probabilistic modeling of sequence alignments, explicitly considering folding dependencies. The tool enables the de novo search for new structural elements and facilitates comparative analysis of known RNA families.

This package provides tools for visualizing, smoothing and comparing receiver operating characteristic (ROC curves). The area under the curve (AUC) can be compared with statistical tests based on U-statistics or bootstrap. Confidence intervals can be computed for (p)AUC or ROC curves.

This package generates well-known integer sequences. The gmp package is adopted for computing with arbitrarily large numbers. Every function has a hyperlink to its corresponding item in the On-Line Encyclopedia of Integer Sequences (OEIS) in the function help page.

This package provides S3 classes and methods to create and work with year-quarter, year-month and year-isoweek vectors. Basic arithmetic operations (such as adding and subtracting) are supported, as well as formatting and converting to and from standard R date types.

Utility functions that provides wrapper to descriptive base functions like cor, mean and table. It makes use of the formula interface to pass variables to functions. It also provides operators to concatenate (%+%), to repeat (%n%) and manage character vectors for nice display.

Simulating multi-arm cluster-randomized, multi-site, and simple randomized trials. Includes functions for conducting multilevel analyses using both Bayesian and Frequentist methods. Supports futility and superiority analyses through Bayesian approaches, along with visualization tools to aid interpretation and presentation of results.

Build Open Geospatial Consortium GeoPackage files (<https://www.geopackage.org/>). GDAL utilities for reading and writing spatial data are provided by the terra package. Additional GeoPackage and SQLite features for attributes and tabular data are implemented with the RSQLite package.

This package provides a GraphQL client, with an R6 interface for initializing a connection to a GraphQL instance, and methods for constructing queries, including fragments and parameterized queries. Queries are checked with the libgraphqlparser C++ parser via the graphql package.

Hierarchical Modelling of Species Communities (HMSC) is a model-based approach for analyzing community ecological data. This package implements it in the Bayesian framework with Gibbs Markov chain Monte Carlo (MCMC) sampling (Tikhonov et al. (2020) <doi:10.1111/2041-210X.13345>).

Calculate expected relative risk and proportion protected assuming normally distributed log10 transformed antibody dose for a several component vaccine. Uses Hill models for each component which are combined under Bliss independence. See Saul and Fay, 2007 <DOI:10.1371/journal.pone.0000850>.

The Length-Biased Power Garima distribution for computes the probability density, the cumulative density distribution and the quantile function of the distribution, and generates sample values with random variables based on Kittipong and Sirinapa(2021)<DOI: 10.14456/sjst-psu.2021.89>.

Airborne LiDAR (Light Detection and Ranging) interface for data manipulation and visualization. Read/write las and laz files, computation of metrics in area based approach, point filtering, artificial point reduction, classification from geographic data, normalization, individual tree segmentation and other manipulations.

Animal abundance estimation via conventional, multiple covariate and mark-recapture distance sampling (CDS/MCDS/MRDS). Detection function fitting is performed via maximum likelihood. Also included are diagnostics and plotting for fitted detection functions. Abundance estimation is via a Horvitz-Thompson-like estimator.

Complements the book "Using R for Modelling and Quantitative Methods in Fisheries" ISBN 9780367469894, published in 2021 by Chapman & Hall in their "Using R series". There are numerous functions and data-sets that are used in the book's many practical examples.

Optimization algorithms implemented in R, including conjugate gradient (CG), Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the limited memory BFGS (L-BFGS) methods. Most internal parameters can be set through the call interface. The solvers hold up quite well for higher-dimensional problems.