This package implements the four-parameter Beta-Danish distribution and its three-parameter Exponentiated Danish submodel for survival, reliability and lifetime data analysis, following Ahmad and Danish (2025) <doi:10.2478/jamsi-2025-0010>. Density, distribution, quantile, survival, hazard and random generation functions are evaluated so as to retain accuracy in the heavy upper tail, where the survival function is regularly varying. Estimation covers maximum likelihood for complete and right-censored samples, ridge-penalized fitting for weakly identified regimes, a grouped likelihood for times recorded on a coarse grid, and Bayesian sampling. Inference provides log-scale Wald and profile likelihood intervals, together with a reparameterization in terms of the identified composite of the two shape parameters. Structural properties include raw, incomplete and conditional moments with their existence conditions, Shannon, Renyi and Tsallis entropies, mean residual life, mean deviations, Lorenz and Bonferroni curves, probability weighted moments, order statistics, stress-strength reliability, hazard shape classification and the tail index. Regression modules cover accelerated failure time models, mixture and promotion-time cure models, and competing risks with Aalen-Johansen comparison and Gray's test. Analyses can be run directly from a delimited text file or spreadsheet.

The bias-corrected estimation methods for the receiver operating characteristics ROC surface and the volume under ROC surfaces (VUS) under missing at random (MAR) assumption.

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.

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>.

Fits Beta Autoregressive Moving Average (BARMA) models for time series data distributed in the standard unit interval (0, 1). The estimation is performed via the conditional maximum likelihood method using the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton algorithm. A ridge penalization scheme is available to improve numerical stability of the estimation, as proposed by Cribari-Neto, Costa and Fonseca (2025) <doi:10.1214/25-BJPS645>. The package includes tools for model fitting, diagnostic checking, and forecasting, along with two hydro-environmental datasets from Brazil. Based on the work of Rocha and Cribari-Neto (2009) <doi:10.1007/s11749-008-0112-z> and the associated erratum Rocha and Cribari-Neto (2017) <doi:10.1007/s11749-017-0528-4>. The original code was developed by Fabio M. Bayer.

This package provides a curated collection of image-inspired color palettes for biomedical visualization. The palettes are organized as qualitative, sequential, or diverging scales and include documented source context and intended use. The package provides functions to retrieve, inspect, preview, and apply these palettes in base R and ggplot2 graphics, together with utilities for working with palette definitions.

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>).

Perform seasonal adjustment and forecasting of weekly data. The package provides a user-friendly interface for computing seasonally adjusted estimates and forecasts of weekly time series and includes functions for the construction of country-specific prior adjustment variables, as well as diagnostic tools to assess the quality of the adjustments. The methodology is described in more detail in Ginker (2024) <doi:10.13140/RG.2.2.12221.44000>.

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.

Bayesian seemingly unrelated regression with general variable selection and dense/sparse covariance matrix. The sparse seemingly unrelated regression is described in Bottolo et al. (2021) <doi:10.1111/rssc.12490>, the software paper is in Zhao et al. (2021) <doi:10.18637/jss.v100.i11>, and the model with random effects is described in Zhao et al. (2024) <doi:10.1093/jrsssc/qlad102>.

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 methods for the group testing identification problem: 1) Operating characteristics (e.g., expected number of tests) for commonly used hierarchical and array-based algorithms, and 2) Optimal testing configurations for these same algorithms. Methods for the group testing estimation problem: 1) Estimation and inference procedures for an overall prevalence, and 2) Regression modeling for commonly used hierarchical and array-based algorithms.

This package provides significance tests for second-order stationarity for time series using bootstrap wavelet packet tests. Provides functionality to visualize the time series with the results of the hypothesis tests superimposed. The methodology is described in Cardinali, A and Nason, G P (2016) "Practical powerful wavelet packet tests for second-order stationarity." Applied and Computational Harmonic Analysis, 44, 558-585 <doi:10.1016/j.acha.2016.06.006>.

Bayesian analysis for stochastic extensions of non-linear dynamic systems using advanced computational algorithms. Described in Bouranis, L., Demiris, N., Kalogeropoulos, K., and Ntzoufras, I. (2022) <doi:10.48550/arXiv.2211.15229>.

This package implements a bootstrap-based heterogeneity test for standardized mean differences (d), Fisher-transformed Pearson's correlations (r), and natural-logarithm-transformed odds ratio (or) in meta-analysis studies. Depending on the presence of moderators, this Monte Carlo based test can be implemented in the random- or mixed-effects model. This package uses rma() function from the R package metafor to obtain parameter estimates and likelihoods, so installation of R package metafor is required. This approach refers to the studies of Anscombe (1956) <doi:10.2307/2332926>, Haldane (1940) <doi:10.2307/2332614>, Hedges (1981) <doi:10.3102/10769986006002107>, Hedges & Olkin (1985, ISBN:978-0123363800), Silagy, Lancaster, Stead, Mant, & Fowler (2004) <doi:10.1002/14651858.CD000146.pub2>, Viechtbauer (2010) <doi:10.18637/jss.v036.i03>, and Zuckerman (1994, ISBN:978-0521432009).

These data contain morphological image measurements for dried beans from Koklu and Ozkan (2020) <doi:10.1016/j.compag.2020.105507>.

Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

This package provides a random forest variant block forest ('BlockForest') tailored to the prediction of binary, survival and continuous outcomes using block-structured covariate data, for example, clinical covariates plus measurements of a certain omics data type or multi-omics data, that is, data for which measurements of different types of omics data and/or clinical data for each patient exist. Examples of different omics data types include gene expression measurements, mutation data and copy number variation measurements. Block forest are presented in Hornung & Wright (2019). The package includes four other random forest variants for multi-omics data: RandomBlock', BlockVarSel', VarProb', and SplitWeights'. These were also considered in Hornung & Wright (2019), but performed worse than block forest in their comparison study based on 20 real multi-omics data sets. Therefore, we recommend to use block forest ('BlockForest') in applications. The other random forest variants can, however, be consulted for academic purposes, for example, in the context of further methodological developments. Reference: Hornung, R. & Wright, M. N. (2019) Block Forests: random forests for blocks of clinical and omics covariate data. BMC Bioinformatics 20:358. <doi:10.1186/s12859-019-2942-y>.

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.

Various layers of B.C., including administrative boundaries, natural resource management boundaries, census boundaries etc. All layers are available in BC Albers (<https://spatialreference.org/ref/epsg/3005/>) equal-area projection, which is the B.C. government standard. The layers are sourced from the British Columbia and Canadian government under open licenses, including B.C. Data Catalogue (<https://data.gov.bc.ca>), the Government of Canada Open Data Portal (<https://open.canada.ca/en/using-open-data>), and Statistics Canada (<https://www.statcan.gc.ca/en/terms-conditions/open-licence>).

Allows the user to manage easily R packages removal and installation. It offers many functions to display installed packages according to specific dates and removes them if needed. The user is always prompted when running the removal functions in order to confirm the required action. It also provides functions that will install Github starred R packages whether available on CRAN or not.

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).

Some elementary matrix algebra tools are implemented to manage block matrices or partitioned matrix, i.e. "matrix of matrices" (http://en.wikipedia.org/wiki/Block_matrix). The block matrix is here defined as a new S3 object. In this package, some methods for "matrix" object are rewritten for "blockmatrix" object. New methods are implemented. This package was created to solve equation systems with block matrices for the analysis of environmental vector time series . Bugs/comments/questions/collaboration of any kind are warmly welcomed.

Tests for a linear relationship in the log ratio between an observed and simulated series and an independent variable. Typically this the error in modelled streamflow at an annual time scale, and a rainfall input. The approach allows for multiple sites as random factors and for multiple replicates of the simulated values. The approach is outlined in Gibbs et al. (2026) in review.