The cmgnd implements the constrained mixture of generalized normal distributions model, a flexible statistical framework for modelling univariate data exhibiting non-normal features such as skewness, multi-modality, and heavy tails. By imposing constraints on model parameters, the cmgnd reduces estimation complexity while maintaining high descriptive power, offering an efficient solution in the presence of distributional irregularities. For more details see Duttilo and Gattone (2025) <doi:10.1007/s00180-025-01638-x> and Duttilo et al (2025) <doi:10.48550/arXiv.2506.03285>.

Implementation of Energy Trees, a statistical model to perform classification and regression with structured and mixed-type data. The model has a similar structure to Conditional Trees, but brings in Energy Statistics to test independence between variables that are possibly structured and of different nature. Currently, the package covers functions and graphs as structured covariates. It builds upon partykit to provide functionalities for fitting, printing, plotting, and predicting with Energy Trees. Energy Trees are described in Giubilei et al. (2022) <arXiv:2207.04430>.

Interactive tools to explore topographic-like data sets. Such data sets take the form of a matrix in which the rows and columns provide location/frequency information, and the matrix elements contain altitude/response information. Such data is found in cartography, 2D spectroscopy and chemometrics. The functions in this package create interactive web pages showing the contoured data, possibly with slices from the original matrix parallel to each dimension. The interactive behavior is created using the D3.js JavaScript library by Mike Bostock.

This package implements readers and writers for file formats associated with genetics data. Reading and writing Plink BED/BIM/FAM and GCTA binary GRM formats is fully supported, including a lightning-fast BED reader and writer implementations. Other functions are readr wrappers that are more constrained, user-friendly, and efficient for these particular applications; handles Plink and Eigenstrat tables (FAM, BIM, IND, and SNP files). There are also make functions for FAM and BIM tables with default values to go with simulated genotype data.

This package provides a functional programming based implementation of the super learner algorithm with an emphasis on supporting the use of formulas to specify learners. This approach offers several improvements compared to past implementations including the ability to easily use random-effects specified in formulas (like y ~ (age | strata) + ...) and construction of new learners is as simple as writing and passing a new function. The super learner algorithm was originally described in van der Laan et al. (2007) <https://biostats.bepress.com/ucbbiostat/paper222/>.

This package provides functions for the analysis of occupational and environmental data with non-detects. Maximum likelihood (ML) methods for censored log-normal data and non-parametric methods based on the product limit estimate (PLE) for left censored data are used to calculate all of the statistics recommended by the American Industrial Hygiene Association (AIHA) for the complete data case. Functions for the analysis of complete samples using exact methods are also provided for the lognormal model. Revised from 2007-11-05 survfit~1'.

The AnVIL is a cloud computing resource developed in part by the National Human Genome Research Institute. The AnVIL package provides end-user and developer functionality. AnVIL provides fast binary package installation, utilities for working with Terra/AnVIL table and data resources, and convenient functions for file movement to and from Google cloud storage. For developers, AnVIL provides programmatic access to the Terra, Leonardo, Rawls, Dockstore, and Gen3 RESTful programming interface, including helper functions to transform JSON responses to formats more amenable to manipulation in R.

XBSeq is a novel algorithm for testing RNA-seq differential expression (DE), where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measurable observed signal and background noise from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes.

This package provides various R programming tools for data manipulation, including:

• medical unit conversions

• combining objects

• character vector operations

• factor manipulation

• obtaining information about R objects

• generating fixed-width format files

• extricating components of date and time objects

• operations on columns of data frames

• matrix operations

• operations on vectors and data frames

• value of last evaluated expression

• wrapper for sample that ensures consistent behavior for both scalar and vector arguments

Designed for the development and application of hidden Markov models and profile HMMs for biological sequence analysis. Contains functions for multiple and pairwise sequence alignment, model construction and parameter optimization, file import/export, implementation of the forward, backward and Viterbi algorithms for conditional sequence probabilities, tree-based sequence weighting, and sequence simulation. Features a wide variety of potential applications including database searching, gene-finding and annotation, phylogenetic analysis and sequence classification. Based on the models and algorithms described in Durbin et al (1998, ISBN: 9780521629713).

This package provides the functions for planning and conducting a clinical trial with adaptive sample size determination. Maximal statistical efficiency will be exploited even when dramatic or multiple adaptations are made. Such a trial consists of adaptive determination of sample size at an interim analysis and implementation of frequentist statistical test at the interim and final analysis with a prefixed significance level. The required assumptions for the stage-wise test statistics are independent and stationary increments and normality. Predetermination of adaptation rule is not required.

Distances on dual-weighted directed graphs using priority-queue shortest paths (Padgham (2019) <doi:10.32866/6945>). Weighted directed graphs have weights from A to B which may differ from those from B to A. Dual-weighted directed graphs have two sets of such weights. A canonical example is a street network to be used for routing in which routes are calculated by weighting distances according to the type of way and mode of transport, yet lengths of routes must be calculated from direct distances.

This package implements a likelihood-based method for genome polarization, identifying which alleles of SNV markers belong to either side of a barrier to gene flow. The approach co-estimates individual assignment, barrier strength, and divergence between sides, with direct application to studies of hybridization. Includes VCF-to-diem conversion and input checks, support for mixed ploidy and parallelization, and tools for visualization and diagnostic outputs. Based on diagnostic index expectation maximization as described in Baird et al. (2023) <doi:10.1111/2041-210X.14010>.

This package provides methods and tools designed to improve the forecast accuracy for a linearly constrained multiple time series, while fulfilling the linear/aggregation relationships linking the components (Girolimetto and Di Fonzo, 2024 <doi:10.48550/arXiv.2412.03429>). FoCo2 offers multi-task forecast combination and reconciliation approaches leveraging input from multiple forecasting models or experts and ensuring that the resulting forecasts satisfy specified linear constraints. In addition, linear inequality constraints (e.g., non-negativity of the forecasts) can be imposed, if needed.

Computes the power and sample size (PASS) required to test for the difference in the mean function between two groups under a repeatedly measured longitudinal or sparse functional design. See the manuscript by Koner and Luo (2023) <https://salilkoner.github.io/assets/PASS_manuscript.pdf> for details of the PASS formula and computational details. The details of the testing procedure for univariate and multivariate response are presented in Wang (2021) <doi:10.1214/21-EJS1802> and Koner and Luo (2023) <arXiv:2302.05612> respectively.

Joint mean and dispersion effects models fit the mean and dispersion parameters of a response variable by two separate linear models, the mean and dispersion submodels, simultaneously. It also allows the users to choose either the deviance or the Pearson residuals as the response variable of the dispersion submodel. Furthermore, the package provides the possibility to nest the submodels in one another, if one of the parameters has significant explanatory power on the other. Wu & Li (2016) <doi:10.1016/j.csda.2016.04.015>.

Calculate a multivariate functional principal component analysis for data observed on different dimensional domains. The estimation algorithm relies on univariate basis expansions for each element of the multivariate functional data (Happ & Greven, 2018) <doi:10.1080/01621459.2016.1273115>. Multivariate and univariate functional data objects are represented by S4 classes for this type of data implemented in the package funData'. For more details on the general concepts of both packages and a case study, see Happ-Kurz (2020) <doi:10.18637/jss.v093.i05>.

This package provides an interface to the Mapbox GL JS (<https://docs.mapbox.com/mapbox-gl-js/guides>) and the MapLibre GL JS (<https://maplibre.org/maplibre-gl-js/docs/>) interactive mapping libraries to help users create custom interactive maps in R. Users can create interactive globe visualizations; layer sf objects to create filled maps, circle maps, heatmaps', and three-dimensional graphics; and customize map styles and views. The package also includes utilities to use Mapbox and MapLibre maps in Shiny web applications.

Asymptotic efficient closed-form estimators (MLEces) are provided in this package for three multivariate distributions(gamma, Weibull and Dirichlet) whose maximum likelihood estimators (MLEs) are not in closed forms. Closed-form estimators are strong consistent, and have the similar asymptotic normal distribution like MLEs. But the calculation of MLEces are much faster than the corresponding MLEs. Further details and explanations of MLEces can be found in. Jang, et al. (2023) <doi:10.1111/stan.12299>. Kim, et al. (2023) <doi:10.1080/03610926.2023.2179880>.

This package implements methods for estimating generalized estimating equations (GEE) with advanced options for flexible modeling and handling missing data. This package provides tools to fit and analyze GEE models for longitudinal data, allowing users to address missingness using a variety of imputation techniques. It supports both univariate and multivariate modeling, visualization of missing data patterns, and facilitates the transformation of data for efficient statistical analysis. Designed for researchers working with complex datasets, it ensures robust estimation and inference in longitudinal and clustered data settings.

This ONEST software implements the method of assessing the pathologist agreement in reading PD-L1 assays (Reisenbichler et al. (2020 <doi:10.1038/s41379-020-0544-x>)), to determine the minimum number of evaluators needed to estimate agreement involving a large number of raters. Input to the program should be binary(1/0) pathology data, where â 0â may stand for negative and â 1â for positive. Additional examples were given using the data from Rimm et al. (2017 <doi:10.1001/jamaoncol.2017.0013>).

This software is useful for loading .fasta or .gbk files, and for retrieving sequences from GenBank dataset <https://www.ncbi.nlm.nih.gov/genbank/>. This package allows to detect differences or asymmetries based on nucleotide composition by using local linear kernel smoothers. Also, it is possible to draw inference about critical points (i. e. maximum or minimum points) related with the derivative curves. Additionally, bootstrap methods have been used for estimating confidence intervals and speed computational techniques (binning techniques) have been implemented in seq2R'.

Augmenting a matched data set by generating multiple stochastic, matched samples from the data using a multi-dimensional histogram constructed from dropping the input matched data into a multi-dimensional grid built on the full data set. The resulting stochastic, matched sets will likely provide a collectively higher coverage of the full data set compared to the single matched set. Each stochastic match is without duplication, thus allowing downstream validation techniques such as cross-validation to be applied to each set without concern for overfitting.

This package provides helper functions and wrappers to simplify authentication, data retrieval, and result processing from the VALD APIs'. Designed to streamline integration for analysts and researchers working with VALD's external APIs'. For further documentation on integrating with VALD APIs', see: <https://support.vald.com/hc/en-au/articles/23415335574553-How-to-integrate-with-VALD-APIs>. For a step-by-step guide to using this package, see: <https://support.vald.com/hc/en-au/articles/48730811824281-A-guide-to-using-the-valdr-R-package>.