This package provides a new metric named dependency heaviness is proposed that measures the number of additional dependency packages that a parent package brings to its child package and are unique to the dependency packages imported by all other parents. The dependency heaviness analysis is visualized by a customized heatmap. The package is described in <doi:10.1093/bioinformatics/btac449>. We have also performed the dependency heaviness analysis on the CRAN/Bioconductor package ecosystem, described in <doi:10.1016/j.jss.2023.111610>.

Analyze spatial phylogenetic diversity patterns. Use your data on an evolutionary tree and geographic distributions of the terminal taxa to compute diversity and endemism metrics, test significance with null model randomization, analyze community turnover and biotic regionalization, and perform spatial conservation prioritizations. All functions support quantitative community data in addition to binary data.

This package implements Progressive Regularized Vine Copula (Prog-Vine) frameworks for high-dimensional dependent competing risks with masked failure causes under Progressive Type-II Censoring. Fits Weibull marginals, estimates pair-copula trees using Expectation-Maximization (EM) algorithms, computes Louis observed information confidence intervals, and implements Data Augmentation Gibbs Samplers for Bayesian credible intervals.

Adds different kinds of brackets to a plot, including braces, chevrons, parentheses or square brackets.

This package provides methods for building self-organizing maps (SOMs) with a number of distinguishing features such automatic centroid detection and cluster visualization using starbursts. For more details see the paper "Improved Interpretability of the Unified Distance Matrix with Connected Components" by Hamel and Brown (2011) in <ISBN:1-60132-168-6>. The package provides user-friendly access to two models we construct: (a) a SOM model and (b) a centroid based clustering model. The package also exposes a number of quality metrics for the quantitative evaluation of the map, Hamel (2016) <doi:10.1007/978-3-319-28518-4_4>. Finally, we reintroduced our fast, vectorized training algorithm for SOM with substantial improvements. It is about an order of magnitude faster than the canonical, stochastic C implementation <doi:10.1007/978-3-030-01057-7_60>.

Post-selection inference in linear regression models, constructing simultaneous confidence intervals across a user-specified universe of models. Implements the methodology described in Kuchibhotla, Kolassa, and Kuffner (2022) "Post-Selection Inference" <doi:10.1146/annurev-statistics-100421-044639> to ensure valid inference after model selection, with applications in high-dimensional settings like Lasso selection.

Loads and processes huge text corpora processed with the sally toolbox (<http://www.mlsec.org/sally/>). sally acts as a very fast preprocessor which splits the text files into tokens or n-grams. These output files can then be read with the PRISMA package which applies testing-based token selection and has some replicate-aware, highly tuned non-negative matrix factorization and principal component analysis implementation which allows the processing of very big data sets even on desktop machines.

This package provides a unified framework for principal component analysis (PCA) and principal component regression (PCR), including standard PCA, sparse PCA, robust PCA, and supervised PCA. The package supports automatic selection of the number of components using cumulative variance and elbow methods and integrates PCA with regression modelling through PCR models. It includes tools for PCA suitability assessment using Bartlett's test of sphericity and the Kaiser-Meyer-Olkin (KMO) measure. Visualisation utilities such as scree plots and biplots are provided for interpretation. The methods are designed to handle multicollinearity, outliers, and high-dimensional data, making them suitable for applied statistical modelling and data analysis. The methodology is based on established approaches described in Jolliffe (2002) <doi:10.1007/b98835>, Zou et al. (2006) <doi:10.1111/j.1467-9868.2005.00503.x>, and Hubert et al. (2005) <doi:10.1198/004017004000000563>.

Fits heterogeneous panel data models with interactive effects for linear regression, logistic, count, probit, quantile, and clustering. Based on Ando, T. and Bai, J. (2015) "A simple new test for slope homogeneity in panel data models with interactive effects" <doi: 10.1016/j.econlet.2015.09.019>, Ando, T. and Bai, J. (2015) "Asset Pricing with a General Multifactor Structure" <doi: 10.1093/jjfinex/nbu026> , Ando, T. and Bai, J. (2016) "Panel data models with grouped factor structure under unknown group membership" <doi: 10.1002/jae.2467>, Ando, T. and Bai, J. (2017) "Clustering huge number of financial time series: A panel data approach with high-dimensional predictors and factor structures" <doi: 10.1080/01621459.2016.1195743>, Ando, T. and Bai, J. (2020) "Quantile co-movement in financial markets" <doi: 10.1080/01621459.2018.1543598>, Ando, T., Bai, J. and Li, K. (2021) "Bayesian and maximum likelihood analysis of large-scale panel choice models with unobserved heterogeneity" <doi: 10.1016/j.jeconom.2020.11.013.>.

Proteins reside in either the cell plasma or in the cell membrane. A membrane protein goes through the membrane at least once. Given the amino acid sequence of a membrane protein, the tool PureseqTM (<https://github.com/PureseqTM/pureseqTM_package>, as described in "Efficient And Accurate Prediction Of Transmembrane Topology From Amino acid sequence only.", Wang, Qing, et al (2019), <doi:10.1101/627307>), can predict the topology of a membrane protein. This package allows one to use PureseqTM from R.

These are harmonized datasets produced as part of the Clinical Trials Network (CTN) protocol number 0094. This is a US National Institute of Drug Abuse (NIDA) funded project; to learn more go to <https://ctnlibrary.org/protocol/ctn0094/>. These are datasets which have the data harmonized from CTN-0027 (<https://ctnlibrary.org/protocol/ctn0027/>), CTN-0030 (<https://ctnlibrary.org/protocol/ctn0030/>), and CTN-0051 (<https://ctnlibrary.org/protocol/ctn0051/>).

Multi-state models are essential tools in longitudinal data analysis. One primary goal of these models is the estimation of transition probabilities, a critical metric for predicting clinical prognosis across various stages of diseases or medical conditions. Traditionally, inference in multi-state models relies on the Aalen-Johansen (AJ) estimator which is consistent under the Markov assumption. However, in many practical applications, the Markovian nature of the process is often not guaranteed, limiting the applicability of the AJ estimator in more complex scenarios. This package extends the landmark Aalen-Johansen estimator (Putter, H, Spitoni, C (2018) <doi:10.1177/0962280216674497>) incorporating presmoothing techniques described by Soutinho, Meira-Machado and Oliveira (2020) <doi:10.1080/03610918.2020.1762895>, offering a robust alternative for estimating transition probabilities in non-Markovian multi-state models with multiple states and potential reversible transitions.

This package provides functions for graph-based multiple-sample testing and visualization of microbiome data, in particular data stored in phyloseq objects. The tests are based on those described in Friedman and Rafsky (1979) <http://www.jstor.org/stable/2958919>, and the tests are described in more detail in Callahan et al. (2016) <doi:10.12688/f1000research.8986.1>.

Given a set of source zone polygons such as census tracts or city blocks alongside with population counts and a target zone of incogruent yet superimposed polygon features (such as individual buildings) populR transforms population counts from the former to the latter using Areal Interpolation methods.

To build a shiny app for visualization of the hierarchy of PheCode Mapping with International Classification of Diseases (ICD). The same PheCode hierarchy is displayed in two ways: as a sunburst plot and as a tree.

This package provides functions for generating pseudo-random numbers that follow a uniform distribution [0,1]. Randomness tests were conducted using the National Institute of Standards and Technology test suite<https://csrc.nist.gov/pubs/sp/800/22/r1/upd1/final>, along with additional tests. The sequence generated depends on the initial values and parameters. The package includes a linear congruence map as the decision map and three chaotic maps to generate the pseudo-random sequence, which follow a uniform distribution. Other distributions can be generated from the uniform distribution using the Inversion Principle Method and BOX-Muller transformation. Small perturbations in seed values result in entirely different sequences of numbers due to the sensitive nature of the maps being used. The chaotic nature of the maps helps achieve randomness in the generator. Additionally, the generator is capable of producing random bits.

Estimation of panel models for glm-like models: this includes binomial models (logit and probit), count models (poisson and negbin) and ordered models (logit and probit), as described in: Baltagi (2013) Econometric Analysis of Panel Data, ISBN-13:978-1-118-67232-7, Hsiao (2014) Analysis of Panel Data <doi:10.1017/CBO9781139839327> and Croissant and Millo (2018), Panel Data Econometrics with R, ISBN:978-1-118-94918-4.

Fits Bayesian nonparametric models based on Pólya tree processes, including adaptive Pólya trees, Markov adaptive Pólya trees, optional Pólya trees, and their conditional-density counterparts. Methods are described in Ma (2017) <doi:10.1214/16-BA1021>, Ma (2017) <doi:10.1214/17-EJS1254>, and Wong and Ma (2010) <doi:10.1214/09-AOS755>.

This package provides a phylogenetic comparative method for finding associations between biological traits and molecular evolutionary rates. The method samples pairs from a phylogeny such that each pair has non-overlapping edge paths, and can therefore be treated as statistically independent observations. Linear regression is performed on the pair contrasts. This approach is similar to phylogenetically independent contrasts (PIC) but without reconstructing the traits at internal nodes, and is better suited for finding trait-rate associations than phylogenetic generalised least squares (PGLS). Refer to Douglas and Bromham (2026) <doi:10.64898/2026.08.13.744736> for further details.

This package provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <doi:10.48550/arXiv.1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

This package provides a set of raw datasets used to create SDTM domains in pharmaversesdtm package.

Construct parser combinator functions, higher order functions that parse input. Construction of such parsers is transparent and easy. Their main application is the parsing of structured text files like those generated by laboratory instruments. Based on a paper by Hutton (1992) <doi:10.1017/S0956796800000411>.

This package implements permutation tests for any test statistic and randomization scheme and constructs associated confidence intervals as described in Glazer and Stark (2024) <doi:10.48550/arXiv.2405.05238>.

This package provides a collection of color palettes inspired by the enormous diversity of skin colors in Neotropical poison frog species. Suitable for use with ggplot2 and base R graphics.