Estimation, inference and diagnostics for Univariate Autoregressive Markov Switching Models for Linear and Generalized Models. Distributions for the series include gaussian, Poisson, binomial and gamma cases. The EM algorithm is used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).

Package to select best model among several linear and nonlinear models. The main function uses the gnls() function from the nlme package to fit the data to nine regression models, named: "linear", "quadratic", "cubic", "logistic", "exponential", "power", "monod", "haldane", "logit".

Computes the pdf, cdf, quantile function, hazard function and generating random numbers for Odd log-logistic family (OLL-G). This family have been developed by different authors in the recent years. See Alizadeh (2019) <doi:10.31801/cfsuasmas.542988> for example.

Creates, fits and samples Pair-Copula Bayesian networks (PCBN) under some restrictions on the underlying Directed Acyclic Graph (DAG), that is, no active cycles nor interfering v-structures, following Derumigny, Horsman and Kurowicka (2025) <doi:10.48550/arXiv.2510.03518>.

This package provides functions for coarse-to-fine spatial modeling (CFSM), enabling fast spatial prediction, regression, and uncertainty quantification. This method is suitable for moderate to large samples. For further details, see Murakami et al. (2026) <doi:10.1111/gean.70034>.

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>.

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.

Token-Oriented Object Notation (TOON) is a compact, human-readable serialization format designed for passing structured data to Large Language Models with significantly reduced token usage. It's intended for LLM input as a lossless, drop-in representation of JSON data.

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.

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.

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>.

Two tests for the well-specification of the linear instrumental variable model. The first test is based on trying to predict the residuals of a two-stage least-squares regression using a random forest. The second test is robust to weak-identification and based on trying to predict the residuals for a particular candidate parameter and can also be used to construct confidence sets with an Anderson-Rubin-type inversion. Details can be found in Scheidegger, Londschien and Bühlmann (2025) "Machine-learning-powered specification testing in linear instrumental variable models" <doi:10.48550/arXiv.2506.12771>.

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.

Supports modelling case data to facilitate. The package provides automated computational grid generation over an area of interest with methods to map covariates between geographies, model fitting including spatially aggregated case counts, and predictions and visualisation. Monte Carlo maximum likelihood is the main fitting method with a low-rank approximation for Gaussian processes described by Solin and Särkkä (2020) <doi:10.1007/s11222-019-09886-w> and a stochastic partial differential equation approximation. Bayesian methods are also provided for some methods. Log-Gaussian Cox Processes are described by Diggle et al. (2013) <doi:10.1214/13-STS441>.

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 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.

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 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.

rocHPL is a benchmark based on the HPL benchmark, a reference linear-algebra benchmark, implemented on top of AMD's Radeon Open Compute (ROCm) platform. rocHPL is created using the HIP programming language and optimized for AMD's discrete GPUs.

Package that implements the FGGA algorithm. This package provides a hierarchical ensemble method based ob factor graphs for the consistent cross-ontology annotation of protein coding genes. FGGA embodies elements of predicate logic, communication theory, supervised learning and inference in graphical models.

Package to integrate methylation and expression data. It can also perform methylation or expression analysis alone. Several plotting functionalities are included as well as a new region analysis based on redundancy analysis. Effect of SNPs on a region can also be estimated.