Bayesian purity model to estimate tumor purity using methylation array data (DNA methylation Infinium 450K array data) without reference samples.

Calculates the necessary quantities to perform Bayesian multigroup equivalence testing. Currently the package includes the Bayesian models and equivalence criteria outlined in Pourmohamad and Lee (2023) <doi:10.1002/sta4.645>, but more models and equivalence testing features may be added over time.

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>).

Generates robust confidence intervals for standardized regression coefficients using heteroskedasticity-consistent standard errors for models fitted by lm() as described in Dudgeon (2017) <doi:10.1007/s11336-017-9563-z>. The package can also be used to generate confidence intervals for R-squared, adjusted R-squared, and differences of standardized regression coefficients. A description of the package and code examples are presented in Pesigan, Sun, and Cheung (2023) <doi:10.1080/00273171.2023.2201277>.

This package provides a "Shiny"" web application for creating interactive Bayesian Network models, learning the structure and parameters of Bayesian networks, and utilities for classic network analysis.

Set of functions to perform various bootstrap unit root tests for both individual time series (including augmented Dickey-Fuller test and union tests), multiple time series and panel data; see Smeekes and Wilms (2023) <doi:10.18637/jss.v106.i12>, Palm, Smeekes and Urbain (2008) <doi:10.1111/j.1467-9892.2007.00565.x>, Palm, Smeekes and Urbain (2011) <doi:10.1016/j.jeconom.2010.11.010>, Moon and Perron (2012) <doi:10.1016/j.jeconom.2012.01.008>, Smeekes and Taylor (2012) <doi:10.1017/S0266466611000387> and Smeekes (2015) <doi:10.1111/jtsa.12110> for key references.

Noise filter based on determining the proportion of neighboring points. A false point will be rejected if it has only few neighbors, but accepted if the proportion of neighbors in a rectangular frame is high. The size of the rectangular frame as well as the cut-off value, i.e. of a minimum proportion of neighbor-points, may be supplied or can be calculated automatically. Originally designed for the cleaning of heart rates, but suitable for filtering any slowly-changing physiological variable.For more information see Signer (2010)<doi:10.1111/j.2041-210X.2009.00010.x>.

This package provides an integrated data management solution for assets installed via the Biobricks.ai platform. Streamlines the process of loading and interacting with diverse datasets in a consistent manner. A list of bricks is available at <https://status.biobricks.ai>. Documentation for Biobricks.ai is available at <https://docs.biobricks.ai>.

Simulation and visualization depth-dependent integrated visual fields. Visual fields are measured monocularly at a single depth, yet real-life activities involve predominantly binocular vision at multiple depths. The package provides functions to simulate and visualize binocular visual field impairment in a depth-dependent fashion from monocular visual field results based on Ping Liu, Allison McKendrick, Anna Ma-Wyatt, Andrew Turpin (2019) <doi:10.1167/tvst.9.3.8>. At each location and depth plane, sensitivities are linearly interpolated from corresponding locations in monocular visual field and returned as the higher value of the two. Its utility is demonstrated by evaluating DD-IVF defects associated with 12 glaucomatous archetypes of 24-2 visual field pattern in the included shiny apps.

This package implements the Bayesian Clustering Factor Models (BCFM) for simultaneous clustering and latent factor analysis of multivariate longitudinal data. The model accounts for within-cluster dependence through shared latent factors while allowing heterogeneity across clusters, enabling flexible covariance modeling in high-dimensional settings. Inference is performed using Markov chain Monte Carlo (MCMC) methods with computationally intensive steps implemented via Rcpp'. Model selection and visualization tools are provided. The methodology is described in Shin, Ferreira, and Tegge (2018) <doi:10.1002/sim.70350>.

This package provides a convenience package for use while drafting code. It facilitates making stand-out comment lines decorated with bands of characters. The input text strings are converted into R comment lines, suitably formatted. These are then displayed in a console window and, if possible, automatically transferred to a clipboard ready for pasting into an R script. Designed to save time when drafting R scripts that will need to be navigated and maintained by other programmers.

This package provides the estimation algorithm to perform the demand estimation described in Berry, Levinsohn and Pakes (1995) <DOI:10.2307/2171802> . The routine uses analytic gradients and offers a large number of implemented integration methods and optimization routines.

Single linkage clustering and connected component analyses are often performed on biological images. Bioi provides a set of functions for performing these tasks. This functionality is implemented in several key functions that can extend to from 1 to many dimensions. The single linkage clustering method implemented here can be used on n-dimensional data sets, while connected component analyses are limited to 3 or fewer dimensions.

The BioTIME database was first published in 2018 and inspired ideas, questions, project and research article. To make it even more accessible, an R package was created. The BioTIMEr package provides tools designed to interact with the BioTIME database. The functions provided include the BioTIME recommended methods for preparing (gridding and rarefaction) time series data, a selection of standard biodiversity metrics (including species richness, numerical abundance and exponential Shannon) alongside examples on how to display change over time. It also includes a sample subset of both the query and meta data, the full versions of which are freely available on the BioTIME website <https://biotime.st-andrews.ac.uk/home.php>.

Managing and generating standardised text for methods and results sections of scientific reports. It handles template variable substitution and supports hierarchical organisation of text through dot-separated paths. The package supports both RDS and JSON database formats, enabling version control and cross-language compatibility.

Fits smoothing spline regression models using scalable algorithms designed for large samples. Seven marginal spline types are supported: linear, cubic, different cubic, cubic periodic, cubic thin-plate, ordinal, and nominal. Random effects and parametric effects are also supported. Response can be Gaussian or non-Gaussian: Binomial, Poisson, Gamma, Inverse Gaussian, or Negative Binomial.

This package provides high-level modeling functions to define and train models using the torch R package. Models include linear, logistic, and multinomial regression as well as multilayer perceptrons.

Fits and simulates multi-optima Ornstein-Uhlenbeck models to phylogenetic comparative data using Bayesian reversible-jump methods. See Uyeda and Harmon (2014) <DOI:10.1093/sysbio/syu057>.

Real-time quantitative polymerase chain reaction (qPCR) data sets by Batsch et al. (2008) <doi:10.1186/1471-2105-9-95>. This package provides five data sets, one for each PCR target: (i) rat SLC6A14, (ii) human SLC22A13, (iii) pig EMT, (iv) chicken ETT, and (v) human GAPDH. Each data set comprises a five-point, four-fold dilution series. For each concentration there are three replicates. Each amplification curve is 45 cycles long. Original raw data file: <https://static-content.springer.com/esm/art%3A10.1186%2F1471-2105-9-95/MediaObjects/12859_2007_2080_MOESM5_ESM.xls>.

This package provides functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks <doi:10.1093/bioinformatics/btq124>.

This package provides functions to fit, via Expectation-Maximization (EM) algorithm, the Bessel and Beta regressions to a data set with a bounded continuous response variable. The Bessel regression is a new and robust approach proposed in the literature. The EM version for the well known Beta regression is another major contribution of this package. See details in the references Barreto-Souza, Mayrink and Simas (2022) <doi:10.1111/anzs.12354> and Barreto-Souza, Mayrink and Simas (2020) <arXiv:2003.05157>.

Package BHMSMAfMRI performs Bayesian hierarchical multi-subject multiscale analysis of fMRI data as described in Sanyal & Ferreira (2012) <DOI:10.1016/j.neuroimage.2012.08.041>, or other multiscale data, using wavelet-based prior that borrows strength across subjects and provides posterior smoothed images of the effect sizes and samples from the posterior distribution.

This package implements efficient NumPy'-like broadcasted operations for atomic and recursive arrays. In the context of operations involving 2 (or more) arrays, â broadcastingâ (AKA singleton expansion) refers to efficiently recycling array dimensions, without making copies. Besides linking to Rcpp', broadcast does not use any external libraries in any way; broadcast was essentially made from scratch and can be installed out-of-the-box. The implementations available in broadcast include, but are not limited to, the following. 1) Broadcasted element-wise operations on any 2 arrays; they support a large set of relational, arithmetic, Boolean, string, and bit-wise operations. 2) A faster, more memory efficient, and broadcasted abind-like function, for binding arrays along an arbitrary dimension. 3) Broadcasted ifelse-like and apply-like functions. 4) Casting functions, that cast subset-groups of an array to a new dimension, cast nested lists to dimensional lists, and vice-versa. 5) A few linear algebra functions for statistics. The functions in the broadcast package strive to minimize computation time and memory usage (which is not just better for efficient computing, but also for the environment).

Estimate fish length-at-age models using MCMC analysis with rstan models. This package allows a multimodel approach to growth fitting to be applied to length-at-age data and is supported by further analyses to determine model selection and result presentation. The core methods of this package are presented in Smart and Grammer (2021) "Modernising fish and shark growth curves with Bayesian length-at-age models". PLOS ONE 16(2): e0246734 <doi:10.1371/journal.pone.0246734>.