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 R package offers block Gibbs samplers for the Bayesian (adaptive) graphical lasso, ridge, and naive elastic net priors. These samplers facilitate the simulation of the posterior distribution of precision matrices for Gaussian distributed data and were originally proposed by: Wang (2012) <doi:10.1214/12-BA729>; Smith et al. (2022) <doi:10.48550/arXiv.2210.16290> and Smith et al. (2023) <doi:10.48550/arXiv.2306.14199>, respectively.

Tutorials for statistics, aimed at biological scientists. Subjects range from basic descriptive statistics through to complex linear modelling. The tutorials include text, videos, interactive coding exercises and multiple choice quizzes. The package also includes 19 datasets which are used in the tutorials.

Includes algorithms to assess alpha and beta diversity in all their dimensions (taxonomic, phylogenetic and functional). It allows performing a number of analyses based on species identities/abundances, phylogenetic/functional distances, trees, convex-hulls or kernel density n-dimensional hypervolumes depicting species relationships. Cardoso et al. (2015) <doi:10.1111/2041-210X.12310>.

Efficient simulation of Brownian semistationary (BSS) processes using the hybrid simulation scheme, as described in Bennedsen, Lunde, Pakkannen (2017) <arXiv:1507.03004v4>, as well as functions to fit BSS processes to data, and functions to estimate the stochastic volatility process of a BSS process.

This package provides a curated collection of biodiversity and species-related datasets (birds, plants, reptiles, turtles, mammals, bees, marine data and related biological measurements), together with small utilities to load and explore them. The package gathers data sourced from public repositories (including Kaggle and well-known ecological/biological R packages) and standardizes access for researchers, educators, and data analysts working on biodiversity, biogeography, ecology and comparative biology. It aims to simplify reproducible workflows by packaging commonly used example datasets and metadata so they can be easily inspected, visualized, and used for teaching, testing, and prototyping analyses.

This package implements Bayesian spatio-temporal factor analysis models for multivariate data observed across space and time. The package provides tools for model fitting via Markov chain Monte Carlo (MCMC), spatial and temporal interpolation, and visualization of latent factors and loadings to support inference and exploration of underlying spatio-temporal patterns. Designed for use in environmental, ecological, or public health applications, with support for posterior prediction and uncertainty quantification. Includes functions such as BSTFA() for model fitting and plot_factor() to visualize the latent processes. Functions are based on and extended from methods described in Berrett, et al. (2020) <doi:10.1002/env.2609>.

This package provides a comprehensive statistical analysis of the accuracy of blood pressure devices based on the method of AAMI/ANSI SP10 standards developed by the AAMI Sphygmomanometer Committee for indirect measurement of blood pressure, incorporated into IS0 81060-2. The bpAcc package gives the exact probability of accepting a device D derived from the join distribution of the sample standard deviation and a non-linear transformation of the sample mean for a specified sample size introduced by Chandel et al. (2023) and by the Association for the Advancement of Medical Instrumentation (2003, ISBN:1-57020-183-8).

We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) <DOI:10.1002/sim.8859>. This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.

Geographically referenced data and statistics of nighttime lights from NASA Black Marble <https://blackmarble.gsfc.nasa.gov/>.

An implementation of functions to generate and plot postestimation quantities after estimating Bayesian regression models using Markov chain Monte Carlo (MCMC). Functionality includes the estimation of the Precision-Recall curves (see Beger, 2016 <doi:10.2139/ssrn.2765419>), the implementation of the observed values method of calculating predicted probabilities by Hanmer and Kalkan (2013) <doi:10.1111/j.1540-5907.2012.00602.x>, the implementation of the average value method of calculating predicted probabilities (see King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>), and the generation and plotting of first differences to summarize typical effects across covariates (see Long 1997, ISBN:9780803973749; King, Tomz, and Wittenberg, 2000 <doi:10.2307/2669316>). This package can be used with MCMC output generated by any Bayesian estimation tool including JAGS', BUGS', MCMCpack', and Stan'.

Draw horizontal histograms, color scattered points by 3rd dimension, enhance date- and log-axis plots, zoom in X11 graphics, trace errors and warnings, use the unit hydrograph in a linear storage cascade, convert lists to data.frames and arrays, fit multiple functions.

Extract, visualize and summarize aerial movements of birds and insects from weather radar data. See Dokter, A. M. et al. (2018) "bioRad: biological analysis and visualization of weather radar data" <doi:10.1111/ecog.04028> for a software paper describing package and methodologies.

Resurrects the standard plot for shapes established by the base and graphics packages. This is suited to workflows that require plotting using the established and traditional idioms of plotting spatially coincident data where it belongs. This package depends on sf and only replaces the plot method.

This package provides a collection of R functions were implemented from published and available analytic solutions for the One-Dimensional Boussinesq Equation (ground-water). In particular, the function "beq.lin()" is the analytic solution of the linearized form of Boussinesq Equation between two different head-based boundary (Dirichlet) conditions; "beq.song" is the non-linear power-series analytic solution of the motion of a wetting front over a dry bedrock (Song at al, 2007, see complete reference on function documentation). Bugs/comments/questions/collaboration of any kind are warmly welcomed.

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>.

An interface for the Neo4j database providing mapping between different identifiers of biological entities. This Biological Entity Dictionary (BED) has been developed to address three main challenges. The first one is related to the completeness of identifier mappings. Indeed, direct mapping information provided by the different systems are not always complete and can be enriched by mappings provided by other resources. More interestingly, direct mappings not identified by any of these resources can be indirectly inferred by using mappings to a third reference. For example, many human Ensembl gene ID are not directly mapped to any Entrez gene ID but such mappings can be inferred using respective mappings to HGNC ID. The second challenge is related to the mapping of deprecated identifiers. Indeed, entity identifiers can change from one resource release to another. The identifier history is provided by some resources, such as Ensembl or the NCBI, but it is generally not used by mapping tools. The third challenge is related to the automation of the mapping process according to the relationships between the biological entities of interest. Indeed, mapping between gene and protein ID scopes should not be done the same way than between two scopes regarding gene ID. Also, converting identifiers from different organisms should be possible using gene orthologs information. The method has been published by Godard and van Eyll (2018) <doi:10.12688/f1000research.13925.3>.

This package provides a collection of models for bivariate alternating recurrent event data analysis. Includes non-parametric and semi-parametric methods.

Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>, N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>, D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>.

Laplace approximations and penalized B-splines are combined for fast Bayesian inference in latent Gaussian models. The routines can be used to fit survival models, especially proportional hazards and promotion time cure models (Gressani, O. and Lambert, P. (2018) <doi:10.1016/j.csda.2018.02.007>). The Laplace-P-spline methodology can also be implemented for inference in (generalized) additive models (Gressani, O. and Lambert, P. (2021) <doi:10.1016/j.csda.2020.107088>). See the associated website for more information and examples.

Forecasters predicting the chances of a future event may disagree due to differing evidence or noise. To harness the collective evidence of the crowd, Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions" <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945> proposes a Bayesian aggregator that is regularized by analyzing the forecasters disagreement and ascribing over-dispersion to noise. This aggregator requires no user intervention and can be computed efficiently even for a large numbers of predictions. The author evaluates the aggregator on subjective probability predictions collected during a four-year forecasting tournament sponsored by the US intelligence community. The aggregator improves the accuracy of simple averaging by around 20% and other state-of-the-art aggregators by 10-25%. The advantage stems almost exclusively from improved calibration. This aggregator -- know as "the revealed aggregator" -- inputs a) forecasters probability predictions (p) of a future binary event and b) the forecasters common prior (p0) of the future event. In this R-package, the function sample_aggregator(p,p0,...) allows the user to calculate the revealed aggregator. Its use is illustrated with a simple example.

Analysis of gene expression RNA-seq data using Bartlett-Adjusted Likelihood-based LInear model (BALLI). Based on likelihood ratio test, it provides comparisons for effect of one or more variables. See Kyungtaek Park (2018) <doi:10.1101/344929> for more information.

This package provides tools that make it easier to validate data using Benford's Law.

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.