Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.

Create regular pivot tables with just a few lines of R. More complex pivot tables can also be created, e.g. pivot tables with irregular layouts, multiple calculations and/or derived calculations based on multiple data frames. Pivot tables are constructed using R only and can be written to a range of output formats (plain text, HTML', Latex and Excel'), including with styling/formatting.

This package performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM. GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>). A complete manual for this package is provided in the manual folder of the package installation directory. You can locate the manual by running the following command in R: system.file("manual", package = "predhy.GUI").

This package implements (1) panel cointegration rank tests, (2) estimators for panel vector autoregressive (VAR) models, and (3) identification methods for panel structural vector autoregressive (SVAR) models as described in the accompanying vignette. The implemented functions allow to account for cross-sectional dependence and for structural breaks in the deterministic terms of the VAR processes. Among the large set of functions, particularly noteworthy are those that implement (1) the correlation-augmented inverse normal test on the cointegration rank by Arsova and Oersal (2021, <doi:10.1016/j.ecosta.2020.05.002>), (2) the two-step estimator for pooled cointegrating vectors by Breitung (2005, <doi:10.1081/ETC-200067895>), and (3) the pooled identification based on independent component analysis by Herwartz and Wang (2024, <doi:10.1002/jae.3044>).

Estimate large covariance matrices in approximate factor models by thresholding principal orthogonal complements.

This package provides methods to detect genetic markers involved in biological adaptation. pcadapt provides statistical tools for outlier detection based on Principal Component Analysis. Implements the method described in (Luu, 2016) <DOI:10.1111/1755-0998.12592> and later revised in (Privé, 2020) <DOI:10.1093/molbev/msaa053>.

An R6 class to set up, run, monitor, collate, and debug large simulation studies comprising many small independent replications and treatment configurations. Parallel processing, reproducibility, fault- and error-tolerance, and ability to resume an interrupted or timed-out simulation study are built in.

This package provides a collection of tools to explore the phylogenetic signal in univariate and multivariate data. The package provides functions to plot traits data against a phylogenetic tree, different measures and tests for the phylogenetic signal, methods to describe where the signal is located and a phylogenetic clustering method.

Evaluate the predictive performance of an existing (i.e. previously developed) prediction/ prognostic model given relevant information about the existing prediction model (e.g. coefficients) and a new dataset. Provides a range of model updating methods that help tailor the existing model to the new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques to aggregate multiple existing prediction models on the new data are also provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. (2018) <doi:10.1002/sim.7586>).

Wrangle and annotate different types of political texts. It also introduces Urgency Analysis, a new method for the analysis of urgency in political texts.

An implementation of reliability estimation methods described in the paper (Bosnic, Z., & Kononenko, I. (2008) <doi:10.1007/s10489-007-0084-9>), which allows you to test the reliability of a single predicted instance made by your model and prediction function. It also allows you to make a correlation test to estimate which reliability estimate is the most accurate for your model.

This package contains functions developed to combine the results of querying a plasmid database using short-read sequence typing with the results of a blast analysis against the query results.

The Prize-Collecting Steiner Tree problem asks to find a subgraph connecting a given set of vertices with the most expensive nodes and least expensive edges. Since it is proven to be NP-hard, exact and efficient algorithm does not exist. This package provides convenient functionality for obtaining an approximate solution to this problem using loopy belief propagation algorithm.

Permutation based Kolmogorov-Smirnov test for paired samples. The test was proposed by Wang W.S., Amsler C. and Schmidt, P. (2025) <doi:10.1007/s00181-025-02779-0>.

Estimate penalized synthetic control models and perform hold-out validation to determine their penalty parameter. This method is based on the work by Abadie & L'Hour (2021) <doi:10.1080/01621459.2021.1971535>. Penalized synthetic controls smoothly interpolate between one-to-one matching and the synthetic control method.

This package provides tools to sort, edit and prune pedigrees and to extract the inbreeding coefficients and the relationship matrix (includes code for pedigrees from self-pollinated species). The use of pedigree data is central to genetics research within the animal and plant breeding communities to predict breeding values. The relationship matrix between the individuals can be derived from pedigree structure ('Vazquez et al., 2010') <doi:10.2527/jas.2009-1952>.

Interactively annotate base R graphics plots with freehand drawing, symbols (points, lines, arrows, rectangles, circles, ellipses), and text. This is useful for teaching, for example to visually explain certain plot elements, and creating quick sketches.

Inspects provenance collected by the rdt or rdtLite packages, or other tools providing compatible PROV JSON output created by the execution of a script, and find differences between two provenance collections. Factors under examination included the hardware and software used to execute the script, versions of attached libraries, use of global variables, modified inputs and outputs, and changes in main and sourced scripts. Based on detected changes, provExplainR can be used to study how these factors affect the behavior of the script and generate a promising diagnosis of the causes of different script results. More information about rdtLite and associated tools is available at <https://github.com/End-to-end-provenance/> and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, <doi:10.3390/informatics5010012>.

This package provides tools for profiling a user-supplied log-likelihood function to calculate confidence intervals for model parameters. Speed of computation can be improved by adjusting the step sizes in the profiling and/or starting the profiling from limits based on the approximate large sample normal distribution for the maximum likelihood estimator of a parameter. The accuracy of the limits can be set by the user. A plot method visualises the log-likelihood and confidence interval. Cases where the profile log-likelihood flattens above the value at which a confidence limit is defined can be handled, leading to a limit at plus or minus infinity. Disjoint confidence intervals will not be found.

Interactively explore various dependencies of a package(s) (on the Comprehensive R Archive Network Like repositories) and perform analysis using tidy philosophy. Most of the functions return a tibble object (enhancement of dataframe') which can be used for further analysis. The package offers functions to produce network and igraph dependency graphs. The plot method produces a static plot based on ggnetwork and plotd3 function produces an interactive D3 plot based on networkD3'.

This package provides tools for downloading, reading and analyzing the National Survey of Health - PNS, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analysis must be made using package survey'.

This package provides a versatile R visualization package that empowers researchers with comprehensive visualization tools for seamlessly mapping peptides to protein sequences, identifying distinct domains and regions of interest, accentuating mutations, and highlighting post-translational modifications, all while enabling comparisons across diverse experimental conditions. Potential applications of PepMapViz include the visualization of cross-software mass spectrometry results at the peptide level for specific protein and domain details in a linearized format and post-translational modification coverage across different experimental conditions; unraveling insights into disease mechanisms. It also enables visualization of Major histocompatibility complex-presented peptide clusters in different antibody regions predicting immunogenicity in antibody drug development.

An implementation of the parameter cascade method in Ramsay, J. O., Hooker,G., Campbell, D., and Cao, J. (2007) for estimating ordinary differential equation models with missing or complete observations. It combines smoothing method and profile estimation to estimate any non-linear dynamic system. The package also offers variance estimates for parameters of interest based on either bootstrap or Delta method.

Multivariate ordered probit model, i.e. the extension of the scalar ordered probit model where the observed variables have dimension greater than one. Estimation of the parameters is done via maximization of the pairwise likelihood, a special case of the composite likelihood obtained as product of bivariate marginal distributions. The package uses the Fortran 77 subroutine SADMVN by Alan Genz, with minor adaptations made by Adelchi Azzalini in his "mvnormt" package for evaluating the two-dimensional Gaussian integrals involved in the pairwise log-likelihood. Optimization of the latter objective function is performed via quasi-Newton box-constrained optimization algorithm, as implemented in nlminb.