Simulation and parameter estimation of multitype Bienayme - Galton - Watson processes.

Reads several formats of 13C data (IRIS/Wagner, BreathID) and CSV. Creates artificial sample data for testing. Fits Maes/Ghoos, Bluck-Coward self-correcting formula using nls', nlme'. Methods to fit breath test curves with Bayesian Stan methods are refactored to package breathteststan'. For a Shiny GUI, see package dmenne/breathtestshiny on github.

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>.

Subgroup analyses are routinely performed in clinical trial analyses. From a methodological perspective, two key issues of subgroup analyses are multiplicity (even if only predefined subgroups are investigated) and the low sample sizes of subgroups which lead to highly variable estimates, see e.g. Yusuf et al (1991) <doi:10.1001/jama.1991.03470010097038>. This package implements subgroup estimates based on Bayesian shrinkage priors, see Carvalho et al (2019) <https://proceedings.mlr.press/v5/carvalho09a.html>. In addition, estimates based on penalized likelihood inference are available, based on Simon et al (2011) <doi:10.18637/jss.v039.i05>. The corresponding shrinkage based forest plots address the aforementioned issues and can complement standard forest plots in practical clinical trial analyses.

This package contains specialised analyses and visualisation tools for behavior change science. These facilitate conducting determinant studies (for example, using confidence interval-based estimation of relevance, CIBER, or CIBERlite plots, see Crutzen, Noijen & Peters (2017) <doi:10/ghtfz9>), systematically developing, reporting, and analysing interventions (for example, using Acyclic Behavior Change Diagrams), and reporting about intervention effectiveness (for example, using the Numbers Needed for Change, see Gruijters & Peters (2017) <doi:10/jzkt>), and computing the required sample size (using the Meaningful Change Definition, see Gruijters & Peters (2020) <doi:10/ghpnx8>). This package is especially useful for researchers in the field of behavior change or health psychology and to behavior change professionals such as intervention developers and prevention workers.

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 contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.

This package provides nested sequential Monte Carlo algorithms for performing sequential inference in the Bayesian Mallows model, which is a widely used probability model for rank and preference data. The package implements the SMC2 (Sequential Monte Carlo Squared) algorithm for handling sequentially arriving rankings and pairwise preferences, including support for complete rankings, partial rankings, and pairwise comparisons. The methods are based on Sorensen (2025) <doi:10.1214/25-BA1564>.

This package implements regression models for binary data on the absolute risk scale. These models are applicable to cohort and population-based case-control data.

This package provides branding, theme application, and navigation utilities for applications built with bs4Dash and shiny'. Supports configurable sidebar brand display modes, hover-expand behavior, and theme customization using CSS variables. Includes standardized navigation components such as refresh and help controls, along with helpers for common navigation bar and footer layouts.

This package provides functions for performing the Bayesian bootstrap as introduced by Rubin (1981) <doi:10.1214/aos/1176345338> and for summarizing the result. The implementation can handle both summary statistics that works on a weighted version of the data and summary statistics that works on a resampled data set.

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.

Get a current financial year, start of current month, End of current month, start of financial year and end of it. Allow for offset from the date.

Multilevel ecological data series (MEDS) are sequences of observations ordered according to temporal/spatial hierarchies that are defined by sample designs, with sample variability confined to ecological factors. Dendroclimatic MEDS of tree rings and climate are modeled into normalized fluctuations of tree growth and aridity. Modeled fluctuations (model frames) are compared with Mantel correlograms on multiple levels defined by sample design. Package implementation can be understood by running examples in modelFrame(), and muleMan() functions.

Fits novel models for the conditional relative risk, risk difference and odds ratio <doi:10.1080/01621459.2016.1192546>.

Understanding the drivers of microbial diversity is an important frontier of microbial ecology, and investigating the diversity of samples from microbial ecosystems is a common step in any microbiome analysis. breakaway is the premier package for statistical analysis of microbial diversity. breakaway implements the latest and greatest estimates of species richness, described in Willis and Bunge (2015) <doi:10.1111/biom.12332>, Willis et al. (2017) <doi:10.1111/rssc.12206>, and Willis (2016) <arXiv:1604.02598>, as well as the most commonly used estimates, including the objective Bayes approach described in Barger and Bunge (2010) <doi:10.1214/10-BA527>.

This package provides a backward-pipe operator for magrittr (%<%) or pipeR (%<<%) that allows for a performing operations from right-to-left. This allows writing more legible code where right-to-left ordering is natural. This is common with hierarchies and nested structures such as trees, directories or markup languages (e.g. HTML and XML). The package also includes a R-Studio add-in that can be bound to a keyboard shortcut.

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.

Nonparametric detection of nonuniformity and dependence with Binary Expansion Testing (BET). See Kai Zhang (2019) BET on Independence, Journal of the American Statistical Association, 114:528, 1620-1637, <DOI:10.1080/01621459.2018.1537921>, Kai Zhang, Wan Zhang, Zhigen Zhao, Wen Zhou. (2023). BEAUTY Powered BEAST, <doi:10.48550/arXiv.2103.00674> and Wan Zhang, Zhigen Zhao, Michael Baiocchi, Yao Li, Kai Zhang. (2023) SorBET: A Fast and Powerful Algorithm to Test Dependence of Variables, Techinical report.

Anonymised Bay Area bike share trip data for the year 2014. Also contains additional metadata on stations and weather.

Makes it easy to download financial data from Yahoo Finance <https://finance.yahoo.com/>.

Included here are babel routines for identifying unusual ribosome protected fragment counts given mRNA counts.

The binomialRF is a new feature selection technique for decision trees that aims at providing an alternative approach to identify significant feature subsets using binomial distributional assumptions (Rachid Zaim, S., et al. (2019)) <doi:10.1101/681973>. Treating each splitting variable selection as a set of exchangeable correlated Bernoulli trials, binomialRF then tests whether a feature is selected more often than by random chance.

Application of genome prediction for a continuous variable, focused on genotype by environment (GE) genomic selection models (GS). It consists a group of functions that help to create regression kernels for some GE genomic models proposed by Jarquà n et al. (2014) <doi:10.1007/s00122-013-2243-1> and Lopez-Cruz et al. (2015) <doi:10.1534/g3.114.016097>. Also, it computes genomic predictions based on Bayesian approaches. The prediction function uses an orthogonal transformation of the data and specific priors present by Cuevas et al. (2014) <doi:10.1534/g3.114.013094>.