This package performs genomic prediction of hybrid performance using eight statistical methods including GBLUP, BayesB, RKHS, PLS, LASSO, EN, LightGBM and XGBoost along with additive and additive-dominance models. Users are able to incorporate parental phenotypic information in all methods based on their specific needs. (Xu S et al(2017) <doi:10.1534/g3.116.038059>; Xu Y et al (2021) <doi: 10.1111/pbi.13458>).

Designed for prediction error estimation through resampling techniques, possibly accelerated by parallel execution on a compute cluster. Newly developed model fitting routines can be easily incorporated. Methods used in the package are detailed in Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062> and were used, for instance, in Porzelius Ch., Schumacher M.and Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.

An efficient tool designed for differential analysis of large-scale RNA sequencing (RNAseq) data and Bisulfite sequencing (BSseq) data in the presence of individual relatedness and population structure. PQLseq first fits a Generalized Linear Mixed Model (GLMM) with adjusted covariates, predictor of interest and random effects to account for population structure and individual relatedness, and then performs Wald tests for each gene in RNAseq or site in BSseq.

The base R data.frame, like any vector, is copied upon modification. This behavior is at odds with that of GUIs and interactive graphics. To rectify this, plumbr provides a mutable, dynamic tabular data model. Models may be chained together to form the complex plumbing necessary for sophisticated graphical interfaces. Also included is a general framework for linking datasets; an typical use case would be a linked brush.

This package provides functions for interacting directly with the Quandl API to offer data in a number of formats usable in R, downloading a zip with all data from a Quandl database, and the ability to search. This R package uses the Quandl API. For more information go to <https://docs.quandl.com>. For more help on the package itself go to <https://www.quandl.com/tools/r>.

This package provides functionality for working with tensors, alternating forms, wedge products, Stokes's theorem, and related concepts from the exterior calculus. Uses disordR discipline (Hankin, 2022, <doi:10.48550/arXiv.2210.03856>). The canonical reference would be M. Spivak (1965, ISBN:0-8053-9021-9) "Calculus on Manifolds". To cite the package in publications please use Hankin (2022) <doi:10.48550/arXiv.2210.17008>.

Enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements. Tools for analysis such as computation of various summary statistics and plotting functionality are also included. The package implements the SMC algorithm of McCartan and Imai (2023) <doi:10.1214/23-AOAS1763>, the enumeration algorithm of Fifield, Imai, Kawahara, and Kenny (2020) <doi:10.1080/2330443X.2020.1791773>, the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) <doi:10.1080/10618600.2020.1739532>, the Merge-split/Recombination algorithms of Carter et al. (2019) <arXiv:1911.01503> and DeFord et al. (2021) <doi:10.1162/99608f92.eb30390f>, and the Short-burst optimization algorithm of Cannon et al. (2020) <arXiv:2011.02288>.

Offers a set of functions to easily make predictions for univariate time series. autoTS is a wrapper of existing functions of the forecast and prophet packages, harmonising their outputs in tidy dataframes and using default values for each. The core function getBestModel() allows the user to effortlessly benchmark seven algorithms along with a bagged estimator to identify which one performs the best for a given time series.

Bayesian inference using the no-U-turn (NUTS) algorithm by Hoffman and Gelman (2014) <https://www.jmlr.org/papers/v15/hoffman14a.html>. Designed for AD Model Builder ('ADMB') models, or when R functions for log-density and log-density gradient are available, such as Template Model Builder models and other special cases. Functionality is similar to Stan', and the rstan and shinystan packages are used for diagnostics and inference.

Analyzes longitudinal Electronic Health Record (EHR) data with possibly informative observational time. These methods are grouped into two classes depending on the inferential task. One group focuses on estimating the effect of an exposure on a longitudinal biomarker while the other group assesses the impact of a longitudinal biomarker on time-to-diagnosis outcomes. The accompanying paper is Du et al (2024) <doi:10.48550/arXiv.2410.13113>.

This package provides models to fit the dynamics of a regulated system experiencing exogenous inputs. The underlying models use differential equations and linear mixed-effects regressions to estimate the coefficients of the equation. With them, the functions can provide an estimated signal. The package provides simulation and analysis functions and also print, summary, plot and predict methods, adapted to the function outputs, for easy implementation and presentation of results.

Fuzzy inference systems are based on fuzzy rules, which have a good capability for managing progressive phenomenons. This package is a basic implementation of the main functions to use a Fuzzy Inference System (FIS) provided by the open source software FisPro <https://www.fispro.org>. FisPro allows to create fuzzy inference systems and to use them for reasoning purposes, especially for simulating a physical or biological system.

This package provides a computationally efficient and statistically rigorous fast Kernel Machine method for multi-kernel analysis. The approach is based on a low-rank approximation to the nuisance effect kernel matrices. The algorithm is applicable to continuous, binary, and survival traits and is implemented using the existing single-kernel analysis software SKAT and coxKM'. coxKM can be obtained from <https://github.com/lin-lab/coxKM>.

Offers a generalization of the scatterplot matrix based on the recognition that most datasets include both categorical and quantitative information. Traditional grids of scatterplots often obscure important features of the data when one or more variables are categorical but coded as numerical. The generalized pairs plot offers a range of displays of paired combinations of categorical and quantitative variables. Emerson et al. (2013) <DOI:10.1080/10618600.2012.694762>.

This package provides functions which make using the Generalized Regression Estimator(GREG) J.N.K. Rao, Isabel Molina, (2015) <doi:10.3390/f11020244> and the Generalized Regression Estimator Operating on Resolutions of Y (GREGORY) easier. The functions are designed to work well within a forestry context, and estimate multiple estimation units at once. Compared to other survey estimation packages, this function has greater flexibility when describing the linear model.

This package provides a Jordan algebra is an algebraic object originally designed to study observables in quantum mechanics. Jordan algebras are commutative but non-associative; they satisfy the Jordan identity. The package follows the ideas and notation of K. McCrimmon (2004, ISBN:0-387-95447-3) "A Taste of Jordan Algebras". To cite the package in publications, please use Hankin (2023) <doi:10.48550/arXiv.2303.06062>.

This package provides test of second-order stationarity for time series (for dyadic and arbitrary-n length data). Provides localized autocovariance, with confidence intervals, for locally stationary (nonstationary) time series. See Nason, G P (2013) "A test for second-order stationarity and approximate confidence intervals for localized autocovariance for locally stationary time series." Journal of the Royal Statistical Society, Series B, 75, 879-904. <doi:10.1111/rssb.12015>.

The current version of the MixSAL package allows users to generate data from a multivariate SAL distribution or a mixture of multivariate SAL distributions, evaluate the probability density function of a multivariate SAL distribution or a mixture of multivariate SAL distributions, and fit a mixture of multivariate SAL distributions using the Expectation-Maximization (EM) algorithm (see Franczak et. al, 2014, <doi:10.1109/TPAMI.2013.216>, for details).

Infer system functioning with empirical NETwork COMparisons. These methods are part of a growing paradigm in network science that uses relative comparisons of networks to infer mechanistic classifications and predict systemic interventions. They have been developed and applied in Langendorf and Burgess (2021) <doi:10.1038/s41598-021-99251-7>, Langendorf (2020) <doi:10.1201/9781351190831-6>, and Langendorf and Goldberg (2019) <doi:10.48550/arXiv.1912.12551>.

Fast functions implemented in C++ via Rcpp to support the NeuroAnatomy Toolbox ('nat') ecosystem. These functions provide large speed-ups for basic manipulation of neuronal skeletons over pure R functions found in the nat package. The expectation is that end users will not use this package directly, but instead the nat package will automatically use routines from this package when it is available to enable large performance gains.

Add-on for the scan package that creates plots from single-case data frames ('scdf'). It includes functions for styling single-case plots, adding phase-based lines to indicate various statistical parameters, and predefined themes for presentations and publications. More information and in depth examples can be found in the online book "Analyzing Single-Case Data with R and scan" Jürgen Wilbert (2025) <https://jazznbass.github.io/scan-Book/>.

Implementation of the family of generalised age-period-cohort stochastic mortality models. This family of models encompasses many models proposed in the actuarial and demographic literature including the Lee-Carter (1992) <doi:10.2307/2290201> and the Cairns-Blake-Dowd (2006) <doi:10.1111/j.1539-6975.2006.00195.x> models. It includes functions for fitting mortality models, analysing their goodness-of-fit and performing mortality projections and simulations.

Tidy tools for NetCDF data sources. Explore the contents of a NetCDF source (file or URL) presented as variables organized by grid with a database-like interface. The hyper_filter() interactive function translates the filter value or index expressions to array-slicing form. No data is read until explicitly requested, as a data frame or list of arrays via hyper_tibble() or hyper_array().

Matching terminal restriction fragment length polymorphism ('TRFLP') profiles between unknown samples and a database of known samples. TRAMPR facilitates analysis of many unknown profiles at once, and provides tools for working directly with electrophoresis output through to generating summaries suitable for community analyses with R's rich set of statistical functions. TRAMPR also resolves the issues of multiple TRFLP profiles within a species, and shared TRFLP profiles across species.