Download and read US Census Bureau data relationship files. Provides support for cleaning and using block assignment files since 2010, as described in <https://www.census.gov/geographies/reference-files/time-series/geo/block-assignment-files.html>. Also includes support for working with block equivalency files, used for years outside of decennial census years.

This package provides tools for 3D imaging, mostly for biology/microscopy. Read and write TIFF stacks. Functions for segmentation, filtering and analyzing 3D point patterns.

Gene symbols, ontology terms, variant formats, and database accessions are validated through one small application programming interface (API), either offline against identifier patterns and bundled snapshots or live against a source web service. Results are designed to match those of the companion Python package for the same inputs. Input order and length are preserved, and errors are reported explicitly rather than as a silent failure. Identifier patterns follow the registry and nomenclature described in Hoyt et al. (2022) <doi:10.1038/s41597-022-01807-3> and den Dunnen et al. (2016) <doi:10.1002/humu.22981>.

Instructor-developed tools for Analytics and Quantitative Methods (AQM) courses at Babson College. Included are compact descriptive statistics for data frames and lists, expanded reporting and graphics for linear regressions, and formatted reports for best subsets analyses.

Package for Breed Wheat Genomic Selection Pipeline. The R package BWGS is developed by Louis Gautier Tran <louis.gautier.tran@gmail.com> and Gilles Charmet <gilles.charmet@inra.fr>. This repository is forked from original repository <https://forgemia.inra.fr/umr-gdec/bwgs> and modified as a R package.

Set of functions to calculate Benthic Biotic Indices from composition data, obtained whether from morphotaxonomic inventories or sequencing data. Based on reference ecological weights publicly available for a set of commonly used marine biotic indices, such as AMBI (A Marine Biotic Index, Borja et al., 2000) <doi:10.1016/S0025-326X(00)00061-8> NSI (Norwegian Sensitivity Index) and ISI (Indicator Species Index) (Rygg 2013, <ISBN:978-82-577-6210-0>). It provides the ecological quality status of the samples based on each BBI as well as the normalized Ecological Quality Ratio.

Posterior sampling and inference for Bayesian Poisson regression models. The model specification makes use of Gaussian (or conditionally Gaussian) prior distributions on the regression coefficients. Details on the algorithm are found in D'Angelo and Canale (2023) <doi:10.1080/10618600.2022.2123337>.

This package provides a collection of integrated tools designed to seamlessly interact with each other for the analysis of biogenic silica bSi in inland and marine sediments. These tools share common data representations and follow a consistent API design. The primary goal of the bSi package is to simplify the installation process, facilitate data loading, and enable the analysis of multiple samples for biogenic silica fluxes. This package is designed to enhance the efficiency and coherence of the entire bSi analytic workflow, from data loading to model construction and visualization tailored towards reconstructing productivity in aquatic ecosystems.

Calculates the bidimensional regression between two 2D configurations following the approach by Tobler (1965).

Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) <doi:10.1016/j.spl.2005.04.024> and Elbayoumi and Mostafa (2020) <doi:10.1002/sta4.342>. Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023) <doi:10.6339/23-JDS1092>.

This package provides tools to read, write, parse, and analyze forest fire history data (e.g. FHX). Described in Malevich et al. (2018) <doi:10.1016/j.dendro.2018.02.005>.

An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>; Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>; Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>; Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule, Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the posterior distributions of parameters are provided. The package also provides functions for estimating the partition function (normalizing constant) of the Mallows rank model, both with the importance sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).

This package contains several plotting functions such as barplots, scatterplots, heatmaps, as well as functions to combine plots and assist in the creation of these plots. These functions will give users great ease of use and customization options in broad use for biomedical applications, as well as general purpose plotting. Each of the functions also provides valid default settings to make plotting data more efficient and producing high quality plots with standard colour schemes simpler. All functions within this package are capable of producing plots that are of the quality to be presented in scientific publications and journals. P'ng et al.; BPG: Seamless, automated and interactive visualization of scientific data; BMC Bioinformatics 2019 <doi:10.1186/s12859-019-2610-2>.

Fits bootstrap with univariate spatial regression models using Bootstrap for Rapid Inference on Spatial Covariances (BRISC) for large datasets using nearest neighbor Gaussian processes detailed in Saha and Datta (2018) <doi:10.1002/sta4.184>.

Implementing the Block Coordinate Ascent with One-Step Generalized Rosen (BCA1SG) algorithm on the semiparametric models for panel count data, interval-censored survival data, and degradation data. A comprehensive description of the BCA1SG algorithm can be found in Wang et al. (2020) <https://github.com/yudongstat/BCA1SG/blob/master/BCA1SG.pdf>. For details of the semiparametric models for panel count data, interval-censored survival data, and degradation data, please see Wellner and Zhang (2007) <doi:10.1214/009053607000000181>, Huang and Wellner (1997) <ISBN:978-0-387-94992-5>, and Wang and Xu (2010) <doi:10.1198/TECH.2009.08197>, respectively.

Bit-level reading and writing are necessary when dealing with many file formats e.g. compressed data and binary files. Currently, R connections are manipulated at the byte level. This package wraps existing connections and raw vectors so that it is possible to read bits, bit sequences, unaligned bytes and low-bit representations of integers.

An implementation of Jon Kleinberg's burst detection algorithm (Kleinberg (2003) <doi:10.1023/A:1024940629314>). Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results.

This package provides functions to plot and help understand positive and negative predictive values (PPV and NPV), and their relationship with sensitivity, specificity, and prevalence. See Akobeng, A.K. (2007) <doi:10.1111/j.1651-2227.2006.00180.x> for a theoretical overview of the technical concepts and Navarrete et al. (2015) for a practical explanation about the importance of their understanding <doi:10.3389/fpsyg.2015.01327>.

This includes functions for creating 3D and 4D images using WebGL', rgl', and JavaScript commands. This package relies on the X toolkit ('XTK', <https://github.com/xtk/X#readme>).

This package provides classes and functions to work with biological sequences (DNA, RNA and amino acid sequences). Implements S3 infrastructure to work with biological sequences as described in Keck (2020) <doi:10.1111/2041-210X.13490>. Provides a collection of functions to perform biological conversion among classes (transcription, translation) and basic operations on sequences (detection, selection and replacement based on positions or patterns). The package also provides functions to import and export sequences from and to other package formats.

This package provides memory-efficient biweight midcorrelation and exact bicor-based k-nearest-neighbor graph construction for dense and sparse numeric matrices. Dense, sparse, and mixed-input paths avoid materializing full dense similarity matrices for tidy and k-nearest-neighbor workflows where possible. The implementation supports pairwise finite-overlap handling and robust correlation-based graph construction for biological expression matrices and other high-dimensional numeric data.

Run other estimation and simulation software via the nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>) interface including PKNCA', NONMEM and Monolix'. While not required, you can get/install the lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, Monolix can be run directly instead of setting up command line usage.

Provide early termination phase II trial designs with a decreasingly informative prior (DIP) or a regular Bayesian prior chosen by the user. The program can determine the minimum planned sample size necessary to achieve the user-specified admissible designs. The program can also perform power and expected sample size calculations for the tests in early termination Phase II trials. See Wang C and Sabo RT (2022) <doi:10.18203/2349-3259.ijct20221110>; Sabo RT (2014) <doi:10.1080/10543406.2014.888441>.

Persist, restore and manage blockr boards from within a running app. Provides a manage_project plugin for blockr.core that replaces the built-in file upload and download interface with storage backed by the pins package. On Posit Connect each visitor reads and writes pins under their own account, with board sharing, visibility controls and version history.