This package provides functions provide risk projections of invasive breast cancer based on Gail model according to National Cancer Institute's Breast Cancer Risk Assessment Tool algorithm for specified race/ethnic groups and age intervals. Gail MH, Brinton LA, et al (1989) <doi:10.1093/jnci/81.24.1879>. Marthew PB, Gail MH, et al (2016) <doi:10.1093/jnci/djw215>.

Collection of functions, data sets and code examples for evaluations of field trials with the objective of equivalence assessment.

This package provides the C++ header-only library barry for use in R packages. barry is a C++ template library for counting sufficient statistics on binary arrays and building discrete exponential-family models. It provides tools for sparse arrays, user-defined count statistics, support set constraints, power set generation, and includes modules for Discrete Exponential Family Models (DEFMs) and network statistics. By placing these headers in this package, we offer an efficient distribution system for CRAN as replication of this code in the sources of other packages is avoided. This package follows the same approach as the BH package which provides Boost headers for R packages.

This package provides functions to get and download city bike data from the website and API service of each city bike service in Norway. The package aims to reduce time spent on getting Norwegian city bike data, and lower barriers to start analyzing it. The data is retrieved from Oslo City Bike, Bergen City Bike, and Trondheim City Bike. The data is made available under NLOD 2.0 <https://data.norge.no/nlod/en/2.0>.

Fits boundary line models to datasets as proposed by Webb (1972) <doi:10.1080/00221589.1972.11514472> and makes statistical inferences about their parameters. Provides additional tools for testing datasets for evidence of boundary presence and selecting initial starting values for model optimization prior to fitting the boundary line models. It also includes tools for conducting post-hoc analyses such as predicting boundary values and identifying the most limiting factor (Miti, Milne, Giller, Lark (2024) <doi:10.1016/j.fcr.2024.109365>). This ensures a comprehensive analysis for datasets that exhibit upper boundary structures.

Implementation of default Bayes factors for testing statistical hypotheses under various statistical models. The package is intended for applied quantitative researchers in the social and behavioral sciences, medical research, and related fields. The Bayes factor tests can be executed for statistical models such as univariate and multivariate normal linear models, correlation analysis, generalized linear models, special cases of linear mixed models, survival models, relational event models. Parameters that can be tested are location parameters (e.g., group means, regression coefficients), variances (e.g., group variances), and measures of association (e.g,. polychoric/polyserial/biserial/tetrachoric/product moments correlations), among others. Relevant references on the methodology The statistical underpinnings are described in O'Hagan (1995) <DOI:10.1111/j.2517-6161.1995.tb02017.x>, Mulder and Xin (2022) <DOI:10.1080/00273171.2021.1904809>, Mulder and Gelissen (2019) <DOI:10.1080/02664763.2021.1992360>, Mulder and Fox (2019) <DOI:10.1214/18-BA1115>, Boeing-Messing, van Assen, Hofman, Hoijtink, and Mulder (2017) <DOI:10.1037/met0000116>, Hoijtink, Mulder, van Lissa, and Gu (2018) <DOI:10.1037/met0000201>, Gu, Mulder, and Hoijtink (2018) <DOI:10.1111/bmsp.12110>, Hoijtink, Gu, and Mulder (2018) <DOI:10.1111/bmsp.12145>, and Hoijtink, Gu, Mulder, and Rosseel (2018) <DOI:10.1037/met0000187>. When using the packages, please refer to the package Mulder et al. (2021) <DOI:10.18637/jss.v100.i18> and the relevant methodological papers.

Time series regression using dynamic linear models fit using MCMC. See Scott and Varian (2014) <DOI:10.1504/IJMMNO.2014.059942>, among many other sources.

Plotting package based on the grid system, combining elements of a bubble plot and heatmap to conveniently display two numerical variables, (represented by color and size) grouped by categorical variables on the x and y axes. This is a useful alternative to a forest plot when the data can be grouped in two dimensions, such as predictors x outcomes. It has particular advantages for visualising the metabolic measures produced by the Nightingale Health metabolomics platform, and templates are included for automatically generating figures from these datasets.

This package provides functions to produce MCMC samples for posterior inference in semiparametric Bayesian discrete time competing risks recurrent events models and multistate models.

Bayesian MCPMod (Fleischer et al. (2022) <doi:10.1002/pst.2193>) is an innovative method that improves the traditional MCPMod by systematically incorporating historical data, such as previous placebo group data. This package offers functions for simulating, analyzing, and evaluating Bayesian MCPMod trials with normally and binary distributed endpoints. It enables the assessment of trial designs incorporating historical data across various true dose-response relationships and sample sizes. Robust mixture prior distributions, such as those derived with the Meta-Analytic-Predictive approach (Schmidli et al. (2014) <doi:10.1111/biom.12242>), can be specified for each dose group. Resulting mixture posterior distributions are used in the Bayesian Multiple Comparison Procedure and modeling steps. The modeling step also includes a weighted model averaging approach (Pinheiro et al. (2014) <doi:10.1002/sim.6052>). Estimated dose-response relationships can be bootstrapped and visualized.

Deals with the braid groups. Includes creation of some specific braids, group operations, free reduction, and Bronfman polynomials. Braid theory has applications in fluid mechanics and quantum physics. The code is adapted from the Haskell library combinat', and is based on Birman and Brendle (2005) <doi:10.48550/arXiv.math/0409205>.

This data package contains a subset of the Bodenmiller et al, Nat Biotech 2012 dataset for testing single cell, high dimensional analysis and visualization methods.

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>.

Fits finite mixture models of univariate Gaussian distributions using JAGS within a Bayesian framework.

Approximate Bayesian inference and Monte Carlo validation for small-N repeated-measures designs with two time points and two raters. The package is intended for applications in which sample size is limited and the observed outcome may be affected by rater-specific bias. User-supplied data are standardised into a common long-format structure. Pre-post effects are analysed using difference scores in a linear model with a rater indicator as covariate. Posterior summaries for the regression coefficients are obtained from a large-sample normal approximation centred at the least-squares estimate with plug-in covariance under a flat improper prior. Evidence for a non-zero pre-post effect, adjusted for rater differences, is summarised using a BIC-based approximation to the Bayes factor for comparison between models with and without the pre-post effect. Monte Carlo validation uses design quantities estimated from the observed data, including sample size, mean pre-post change, and second-rater additive discrepancy, and summarises inferential performance in terms of bias, root mean squared error, credible interval coverage, posterior tail probabilities, and mean Bayes factor values. For background on the BIC approximation and Bayes factors, see Schwarz (1978) <doi:10.1214/aos/1176344136> and Kass and Raftery (1995) <doi:10.1080/01621459.1995.10476572>.

R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.

Data about the bakers, challenges, and ratings for "The Great British Bake Off", from Wikipedia <https://en.wikipedia.org/wiki/The_Great_British_Bake_Off>.

These are miscellaneous functions for working with panel data, quantiles, and printing results. For panel data, the package includes functions for making a panel data balanced (that is, dropping missing individuals that have missing observations in any time period), converting id numbers to row numbers, and to treat repeated cross sections as panel data under the assumption of rank invariance. For quantiles, there are functions to make distribution functions from a set of data points (this is particularly useful when a distribution function is created in several steps), to combine distribution functions based on some external weights, and to invert distribution functions. Finally, there are several other miscellaneous functions for obtaining weighted means, weighted distribution functions, and weighted quantiles; to generate summary statistics and their differences for two groups; and to add or drop covariates from formulas. Additional utilities support staggered treatment adoption settings, including functions for identifying treatment groups, recovering pre-treatment outcomes and covariate averages, and computing lagged outcomes and first differences in panel data.

Is used to simulate and fit biological geometries. biogeom incorporates several novel universal parametric equations that can generate the profiles of bird eggs, flowers, linear and lanceolate leaves, seeds, starfish, and tree-rings (Gielis (2003) <doi:10.3732/ajb.90.3.333>; Shi et al. (2020) <doi:10.3390/sym12040645>), three growth-rate curves representing the ontogenetic growth trajectories of animals and plants against time, and the axially symmetrical and integral forms of all these functions (Shi et al. (2017) <doi:10.1016/j.ecolmodel.2017.01.012>; Shi et al. (2021) <doi:10.3390/sym13081524>). The optimization method proposed by Nelder and Mead (1965) <doi:10.1093/comjnl/7.4.308> was used to estimate model parameters. biogeom includes several real data sets of the boundary coordinates of natural shapes, including avian eggs, fruit, lanceolate and ovate leaves, tree rings, seeds, and sea stars,and can be potentially applied to other natural shapes. biogeom can quantify the conspecific or interspecific similarity of natural outlines, and provides information with important ecological and evolutionary implications for the growth and form of living organisms. Please see Shi et al. (2022) <doi:10.1111/nyas.14862> for details.

We provide a framework for testing the probability of ruin in the classical (compound Poisson) risk process. It also includes some procedures for assessing and comparing the performance between the bootstrap test and the test using asymptotic normality.

Bayesian estimation of multilevel Vector Autoregression (VAR) models using Stan. Supports Gaussian, Binary, and Ordinal (adjacent category) outcome variables with random effects and customizable priors.

Bland-Altman Plots using either base graphics or ggplot2, augmented with confidence intervals, with detailed return values and a sunflowerplot option for data with ties.

This package provides functions for training extreme gradient boosting model using propensity score A-learning and weight-learning methods. For further details, see Liu et al. (2024) <doi:10.1093/bioinformatics/btae592>.

Battery reduction is a method used in data reduction. It uses Gram-Schmidt orthogonal rotations to find out a subset of variables best representing the original set of variables.