This package provides simplified access to selected Brazilian macroeconomic and financial time series from official sources, primarily the Central Bank of Brazil through the SGS (Sistema Gerenciador de Séries Temporais) API. The package enables users to quickly retrieve and visualize indicators such as the unemployment rate and the Selic interest rate using a standardized data structure. It is designed for data access and visualization purposes, without performing forecasts or statistical modeling. For more information, see the official API: <https://dadosabertos.bcb.gov.br/dataset/>.

Bayesian model and associated tools for generating estimates of total naloxone kit numbers distributed and used from naloxone kit orders data. Provides functions for generating simulated data of naloxone kit use and functions for generating samples from the posterior.

This package performs goodness of fit test for the Birnbaum-Saunders distribution and provides the maximum likelihood estimate and the method-of-moments estimate. For more details, see Park and Wang (2013) <arXiv:2308.10150>. This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea government (MSIT) (No. 2022R1A2C1091319, RS-2023-00242528).

Predicts survival for patients with severe limb ischaemia using the prognostic model developed from the Bypass versus Angioplasty in Severe Ischaemia of the Leg (BASIL) trial (Bradbury and others (2010) <doi:10.1016/j.jvs.2010.01.077>). The model is an accelerated failure time Weibull regression. The package is intended for research and audit; it is not a substitute for clinical judgement and its predictions should not be used as the sole basis for clinical decisions.

This package provides a family of novel beta mixture models (BMMs) has been developed by Majumdar et al. (2022) <doi:10.48550/arXiv.2211.01938> to appositely model the beta-valued cytosine-guanine dinucleotide (CpG) sites, to objectively identify methylation state thresholds and to identify the differentially methylated CpG (DMC) sites using a model-based clustering approach. The family of beta mixture models employs different parameter constraints applicable to different study settings. The EM algorithm is used for parameter estimation, with a novel approximation during the M-step providing tractability and ensuring computational feasibility.

This package provides statistical tools for Bayesian estimation of mixture distributions, mainly a mixture of Gamma, Normal, and t-distributions. The package is implemented based on the Bayesian literature for the finite mixture of distributions, including Mohammadi and et al. (2013) <doi:10.1007/s00180-012-0323-3> and Mohammadi and Salehi-Rad (2012) <doi:10.1080/03610918.2011.588358>.

Estimates Boltzmannâ Lotkaâ Volterra (BLV) interaction model efficiently. Enables programmatic and graphical exploration of the solution space of BLV models when parameters are varied. See Wilson, A. (2008) <dx.doi.org/10.1098/rsif.2007.1288>.

This package implements the Bayesian paradigm for fractional polynomial models under the assumption of normally distributed error terms, see Sabanes Bove, D. and Held, L. (2011) <doi:10.1007/s11222-010-9170-7>.

Specify and fit the Bradley-Terry model, including structured versions in which the parameters are related to explanatory variables through a linear predictor and versions with contest-specific effects, such as a home advantage.

From R 4.5.0, the datasets package includes the penguins and penguins_raw data sets popularised in the palmerpenguins package. basepenguins takes files that use the palmerpenguins package and converts them to work with the versions from datasets ('R >= 4.5.0). It does this by removing calls to library(palmerpenguins) and making the necessary changes to column names. Additionally, it provides helper functions to define new files paths for saving the output and a directory of example files to experiment with.

This package provides a collection of functions for downloading and processing automatic weather station (AWS) data from INMET (Brazilâ s National Institute of Meteorology), designed to support the estimation of reference evapotranspiration (ETo). The package facilitates streamlined access to meteorological data and aims to simplify analyses in agricultural and environmental contexts.

This package provides functions for analyzing and visualizing complex macroevolutionary dynamics on phylogenetic trees. It is a companion package to the command line program BAMM (Bayesian Analysis of Macroevolutionary Mixtures) and is entirely oriented towards the analysis, interpretation, and visualization of evolutionary rates. Functionality includes visualization of rate shifts on phylogenies, estimating evolutionary rates through time, comparing posterior distributions of evolutionary rates across clades, comparing diversification models using Bayes factors, and more.

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.

Analysis of relative cell type proportions in bulk gene expression data. Provides a well-validated set of brain cell type-specific marker genes derived from multiple types of experiments, as described in McKenzie (2018) <doi:10.1038/s41598-018-27293-5>. For brain tissue data sets, there are marker genes available for astrocytes, endothelial cells, microglia, neurons, oligodendrocytes, and oligodendrocyte precursor cells, derived from each of human, mice, and combination human/mouse data sets. However, if you have access to your own marker genes, the functions can be applied to bulk gene expression data from any tissue. Also implements multiple options for relative cell type proportion estimation using these marker genes, adapting and expanding on approaches from the CellCODE R package described in Chikina (2015) <doi:10.1093/bioinformatics/btv015>. The number of cell type marker genes used in a given analysis can be increased or decreased based on your preferences and the data set. Finally, provides functions to use the estimates to adjust for variability in the relative proportion of cell types across samples prior to downstream analyses.

This package provides a fast and intuitive batch effect removal tool for single-cell data. BBKNN is originally used in the scanpy python package, and now can be used with Seurat seamlessly.

This package provides a set of R functions and data sets for the book Introduction to Bayesian Statistics, Bolstad, W.M. (2017), John Wiley & Sons ISBN 978-1-118-09156-2.

Disaggregates an observed aggregate price index into sectoral components with a Bayesian state-space model in which the aggregate enters as a genuine observation density rather than as a renormalization identity. A random-walk-with-drift transition in log space (with partial pooling on the drift and the innovation scale) and an estimable cross-sectional concentration produce posterior draws of the sectoral indices with credible intervals, suitable as multiple-imputation input for downstream dynamic models. The Hamiltonian Monte Carlo engine follows Stan (Carpenter et al., 2017) <doi:10.18637/jss.v076.i01>; model comparison uses Pareto Smoothed Importance Sampling Leave-One-Out cross-validation (Vehtari, Gelman and Gabry, 2017) <doi:10.1007/s11222-016-9696-4>. A closed-form linear-Gaussian Kalman/RTS smoother provides an exact, MCMC-free Bayesian alternative for the same aggregate evidence.

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. Databases are stored as JSON by default for version control and cross-language compatibility; trusted legacy RDS databases remain readable through import and migration utilities.

Estimation of latent variable models using Bayesian methods. Currently estimates the loglinear cognitive diagnosis model of Henson, Templin, and Willse (2009) <doi:10.1007/s11336-008-9089-5>.

This package provides functionality to automatically detect groove locations via a Bayesian changepoint detection method to be used in the data preprocessing step of forensic bullet matching algorithms. The methods in this package are based on those in Stephens (1994) <doi:10.2307/2986119>. Bayesian changepoint detection will simply be an option in the function from the package bulletxtrctr which identifies the groove locations.

We utilize the Bradley-Terry Model to estimate the abilities of teams using paired comparison data. For dynamic approximation of current rankings, we employ the Exponential Decayed Log-likelihood function, and we also apply the Lasso penalty for variance reduction and grouping. The main algorithm applies the Augmented Lagrangian Method described by Masarotto and Varin (2012) <doi:10.1214/12-AOAS581>.

The Bayesian Adjustment for Confounding (BAC) algorithm (Wang et al., 2012) can be used to estimate the causal effect of a continuous exposure on a continuous outcome. This package provides an approximate sensitivity analysis of BAC with regards to the hyperparameter omega. BACprior also provides functions to guide the user in their choice of an appropriate omega value. The method is based on Lefebvre, Atherton and Talbot (2014).

This package implements a fully Bayesian Markov chain Monte Carlo (MCMC) approach for inferring the topology and Boolean logic transition functions of gene regulatory networks from noisy, binary time-series expression data. Network structure and Boolean rules are sampled jointly from their posterior distribution, providing principled uncertainty quantification rather than a single point estimate. Method described in Han et al. (2014) <doi:10.1371/journal.pone.0115806>.

Constructs treatment and block designs for linear treatment models with crossed or nested block factors. The treatment design can be any feasible linear model and the block design can be any feasible combination of crossed or nested block factors. The block design is a sum of one or more block factors and the block design is optimized sequentially with the levels of each successive block factor optimized conditional on all previously optimized block factors. D-optimality is used throughout except for square or rectangular lattice block designs which are constructed algebraically using mutually orthogonal Latin squares. Crossed block designs with interaction effects are optimized using a weighting scheme which allows for differential weighting of first and second-order block effects. Outputs include a table showing the allocation of treatments to blocks and tables showing the achieved D-efficiency factors for each block and treatment design. Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500â 522 (2020) <doi:10.1007/s13253-020-00416-0>.