Fetches zonal statistics from weather indicators that were calculated for each municipality in Brazil using data from the BR-DWGD and TerraClimate projects. Zonal statistics such as mean, maximum, minimum, standard deviation, and sum were computed by taking into account the data cells that intersect the boundaries of each municipality and stored in Parquet files. This procedure was carried out for all Brazilian municipalities, and for all available dates, for every indicator available in the weather products (BR-DWGD and TerraClimate projects). This package queries on-line the already calculated statistics on the Parquet files and returns easy-to-use data.frames.

Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in Heligman, L., & Pollard, J. H. (1980) <doi:10.1017/S0020268100040257> and Dellaportas, Petros, et al. (2001) <doi:10.1111/1467-985X.00202>, and dynamic linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009) <doi:10.1007/b135794_2>). While Heligman-Pollard has parameters with a straightforward interpretation yielding some rich analysis, the dynamic linear model provides a very flexible adjustment of the mortality curves by controlling the discount factor value. Closing methods for both Heligman-Pollard and dynamic linear model were also implemented according to Dodd, Erengul, et al. (2018) <https://www.jstor.org/stable/48547511>. The Bayesian Lee-Carter model is also implemented to fit historical mortality tables time series to predict the mortality in the following years and to do improvement analysis, as seen in Lee, R. D., & Carter, L. R. (1992) <doi:10.1080/01621459.1992.10475265> and Pedroza, C. (2006) <doi:10.1093/biostatistics/kxj024>. Journal publication available at <doi:10.18637/jss.v113.i09>.

This package provides tools for factor analysis in financial and econometric settings under Beta factor models. It includes functions to simulate factor-model data with Beta-distributed idiosyncratic components (e.g., standard Beta, scaled Beta, and truncated Beta distributions) and to conduct model diagnostic assessments such as likelihood ratio tests for factor number selection and goodness-of-fit tests for Beta distribution assumptions. Estimation routines encompass maximum likelihood estimation for finite-dimensional Beta factor models, regularized Beta factor analysis for high-dimensional datasets, and shrinkage-based estimation for robust Beta factor loading recovery in noisy or incomplete data environments. The package's methodological framework is detailed in Guo G. (2023) <doi:10.1007/s00180-022-01270-z>.

Easy-to-use, efficient, flexible and scalable tools for analyzing massive SNP arrays. Privé et al. (2018) <doi:10.1093/bioinformatics/bty185>.

Package binr (pronounced as "binner") provides algorithms for cutting numerical values exhibiting a potentially highly skewed distribution into evenly distributed groups (bins). This functionality can be applied for binning discrete values, such as counts, as well as for discretization of continuous values, for example, during generation of features used in machine learning algorithms.

This package provides methods for estimating the area under the concentration versus time curve (AUC) and its standard error in the presence of Below the Limit of Quantification (BLOQ) observations. Two approaches are implemented: direct estimation using censored maximum likelihood, and a two-step approach that first imputes BLOQ values using various methods and then computes the AUC using the imputed data. Technical details are described in Barnett et al. (2020), "Methods for Non-Compartmental Pharmacokinetic Analysis With Observations Below the Limit of Quantification," Statistics in Biopharmaceutical Research. <doi:10.1080/19466315.2019.1701546>.

Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for proportional hazards models with piecewise constant hazard. The methodology and examples of applying the package are detailed in <doi:10.48550/arXiv.2404.05118>. The Bayesian clinical trial design methodology is described in Chen et al. (2011) <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) <doi:10.1093/biostatistics/kxy009>. The proportional hazards model with piecewise constant hazard is detailed in Ibrahim et al. (2001) <doi:10.1007/978-1-4757-3447-8>.

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>.

An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 <doi:10.48550/arXiv.2503.13078>).

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 tools to create, validate, and export BioCompute Objects described in King et al. (2019) <doi:10.17605/osf.io/h59uh>. Users can encode information in data frames, and compose BioCompute Objects from the domains defined by the standard. A checksum validator and a JSON schema validator are provided. This package also supports exporting BioCompute Objects as JSON, PDF, HTML, or Word documents, and exporting to cloud-based platforms.

This package provides comprehensive methods to calculate posterior probabilities, posterior predictive probabilities, and Go/NoGo/Gray decision probabilities for quantitative decision-making under a Bayesian paradigm in clinical trials. The package supports both single and two-endpoint analyses for binary and continuous outcomes, with controlled, uncontrolled, and external designs. For single continuous endpoints, three calculation methods are available: numerical integration (NI), Monte Carlo simulation (MC), and Moment-Matching approximation (MM). For two continuous endpoints, a bivariate Normal-Inverse-Wishart conjugate model is implemented with MC and MM methods. For two binary endpoints, a Dirichlet-multinomial model is implemented. External designs incorporate historical data through power priors using exact conjugate representations (Normal-Inverse-Chi-squared for single continuous, Normal-Inverse-Wishart for two continuous, and Dirichlet for binary endpoints), enabling closed-form posterior computation without Markov chain Monte Carlo (MCMC) sampling. This approach significantly reduces computational burden while preserving complete Bayesian rigor. The package also provides grid-search functions to find optimal Go and NoGo thresholds that satisfy user-specified operating characteristic criteria for all supported endpoint types and study designs. S3 print() and plot() methods are provided for all decision probability classes, enabling formatted display and visualisation of Go/NoGo/Gray operating characteristics across treatment scenarios. See Kang, Yamaguchi, and Han (2026) <doi:10.1080/10543406.2026.2655410> for the methodological framework.

This package provides a maximum likelihood estimation of Bivariate Zero-Inflated Negative Binomial (BZINB) model or the nested model parameters. Also estimates the underlying correlation of the a pair of count data. See Cho, H., Liu, C., Preisser, J., and Wu, D. (In preparation) for details.

The BACCO bundle of packages is replaced by the BACCO package, which provides a vignette that illustrates the constituent packages (emulator, approximator, calibrator) in use.

This package provides comprehensive tools for blinded sample size re-estimation (BSSR) in two-arm clinical trials with binary endpoints. Unlike traditional fixed-sample designs, BSSR allows adaptive sample size adjustments during trials while maintaining statistical integrity and study blinding. Implements five exact statistical tests: Pearson chi-squared, Fisher exact, Fisher mid-p, Z-pooled exact unconditional, and Boschloo exact unconditional tests. Supports restricted, unrestricted, and weighted BSSR approaches with exact Type I error control. Statistical methods based on Mehrotra et al. (2003) <doi:10.1111/1541-0420.00051> and Kieser (2020) <doi:10.1007/978-3-030-49528-2_21>.

This package provides a platform for interactive data analysis designed to simplify development, deployment, interaction, and exploration (TEDDIE). The package enables users to create customized analyses and deploy them to end users, who can perform interactive analyses and export results to RTF or HTML files. It allows developers to focus on R code for analysis rather than managing HTML or Shiny application code.

Interface to a high-performance implementation of k-medoids clustering described in Tiwari, Zhang, Mayclin, Thrun, Piech and Shomorony (2020) "BanditPAM: Almost Linear Time k-medoids Clustering via Multi-Armed Bandits" <https://proceedings.neurips.cc/paper/2020/file/73b817090081cef1bca77232f4532c5d-Paper.pdf>.

Extend lasso and elastic-net model fitting for large data sets that cannot be loaded into memory. Designed to be more memory- and computation-efficient than existing lasso-fitting packages like glmnet and ncvreg', thus allowing the user to analyze big data with limited RAM <doi:10.32614/RJ-2021-001>.

This package provides tools and code snippets for summarizing nested data, adverse events and REDCap study information.

This package implements the distance measure for mixed-scale variables proposed by Buttler and Fickel (1995), based on normalized mean pairwise distances (Gini mean difference), and an R2 statistic to assess clustering quality.

This package performs estimation of marginal treatment effects for binary outcomes when using logistic regression working models with covariate adjustment (see discussions in Magirr et al (2024) <https://osf.io/9mp58/>). Implements the variance estimators of Ge et al (2011) <doi:10.1177/009286151104500409> and Ye et al (2023) <doi:10.1080/24754269.2023.2205802>.

Script search, corner, genetic optimization, permutation tests, write expect test.

This package provides methods for frontier analysis, Data Envelopment Analysis (DEA), under different technology assumptions (fdh, vrs, drs, crs, irs, add/frh, and fdh+), and using different efficiency measures (input based, output based, hyperbolic graph, additive, super, and directional efficiency). Peers and slacks are available, partial price information can be included, and optimal cost, revenue and profit can be calculated. Evaluation of mergers is also supported. Methods for graphing the technology sets are also included. There is also support for comparative methods based on Stochastic Frontier Analyses (SFA) and for convex nonparametric least squares of convex functions (STONED). In general, the methods can be used to solve not only standard models, but also many other model variants. It complements the book, Bogetoft and Otto, Benchmarking with DEA, SFA, and R, Springer-Verlag, 2011, but can of course also be used as a stand-alone package.

Build and compare nested statistical models with sets of equal and different independent variables. An analysis using this package is Marquardt et al. (2021) <https://github.com/p-mq/Percentile_based_averaging>.