Bayesian Linear Regression.

Static code analysis of box modules. The package enhances code quality by providing linters that check for common issues, enforce best practices, and ensure consistent coding standards.

Fits a Bayesian zero-inflated Bernoulli regression model handling (potentially) different covariates for the zero-inflated and non zero-inflated parts. See Moriña D, Puig P, Navarro A. (2021) <doi:10.1186/s12874-021-01427-2>.

Computes appropriate confidence intervals for the likelihood ratio tests commonly used in medicine/epidemiology, using the method of Marill et al. (2015) <doi:10.1177/0962280215592907>. It is particularly useful when the sensitivity or specificity in the sample is 100%. Note that this does not perform the test on nested models--for that, see epicalc::lrtest'.

This package provides a lightweight modelling syntax for defining likelihoods and priors and for computing Bayes factors for simple one parameter models. It includes functionality for computing and plotting priors, likelihoods, and model predictions. Additional functionality is included for computing and plotting posteriors.

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

Allows Bayesian borrowing from a historical dataset for time-to- event data. A flexible baseline hazard function is achieved via a piecewise exponential likelihood with time varying split points and smoothing prior on the historic baseline hazards. The method is described in Scott and Lewin (2024) <doi:10.48550/arXiv.2401.06082>, and the software paper is in Axillus et al. (2024) <doi:10.48550/arXiv.2408.04327>.

The backtest package provides facilities for exploring portfolio-based conjectures about financial instruments (stocks, bonds, swaps, options, et cetera).

Spike and slab regression with a variety of residual error distributions corresponding to Gaussian, Student T, probit, logit, SVM, and a few others. Spike and slab regression is Bayesian regression with prior distributions containing a point mass at zero. The posterior updates the amount of mass on this point, leading to a posterior distribution that is actually sparse, in the sense that if you sample from it many coefficients are actually zeros. Sampling from this posterior distribution is an elegant way to handle Bayesian variable selection and model averaging. See <DOI:10.1504/IJMMNO.2014.059942> for an explanation of the Gaussian case.

This package implements an S3 class based on data.table to store and process efficiently ethomics (high-throughput behavioural) data.

The Super Imposition by Translation and Rotation (SITAR) model is a shape-invariant nonlinear mixed effect model that fits a natural cubic spline mean curve to the growth data and aligns individual-specific growth curves to the underlying mean curve via a set of random effects (see Cole, 2010 <doi:10.1093/ije/dyq115> for details). The non-Bayesian version of the SITAR model can be fit by using the already available R package sitar'. While the sitar package allows modelling of a single outcome only, the bsitar package offers great flexibility in fitting models of varying complexities, including joint modelling of multiple outcomes such as height and weight (multivariate model). Additionally, the bsitar package allows for the simultaneous analysis of an outcome separately for subgroups defined by a factor variable such as gender. This is achieved by fitting separate models for each subgroup (for example males and females for gender variable). An advantage of this approach is that posterior draws for each subgroup are part of a single model object, making it possible to compare coefficients across subgroups and test hypotheses. Since the bsitar package is a front-end to the R package brms', it offers excellent support for post-processing of posterior draws via various functions that are directly available from the brms package. In addition, the bsitar package includes various customized functions that allow for the visualization of distance (increase in size with age) and velocity (change in growth rate as a function of age), as well as the estimation of growth spurt parameters such as age at peak growth velocity and peak growth velocity.

Assess the agreement in method comparison studies by tolerance intervals and errors-in-variables (EIV) regressions. The Ordinary Least Square regressions (OLSv and OLSh), the Deming Regression (DR), and the (Correlated)-Bivariate Least Square regressions (BLS and CBLS) can be used with unreplicated or replicated data. The BLS() and CBLS() are the two main functions to estimate a regression line, while XY.plot() and MD.plot() are the two main graphical functions to display, respectively an (X,Y) plot or (M,D) plot with the BLS or CBLS results. Four hyperbolic statistical intervals are provided: the Confidence Interval (CI), the Confidence Bands (CB), the Prediction Interval and the Generalized prediction Interval. Assuming no proportional bias, the (M,D) plot (Band-Altman plot) may be simplified by calculating univariate tolerance intervals (beta-expectation (type I) or beta-gamma content (type II)). Major updates from last version 1.0.0 are: title shortened, include the new functions BLS.fit() and CBLS.fit() as shortcut of the, respectively, functions BLS() and CBLS(). References: B.G. Francq, B. Govaerts (2016) <doi:10.1002/sim.6872>, B.G. Francq, B. Govaerts (2014) <doi:10.1016/j.chemolab.2014.03.006>, B.G. Francq, B. Govaerts (2014) <http://publications-sfds.fr/index.php/J-SFdS/article/view/262>, B.G. Francq (2013), PhD Thesis, UCLouvain, Errors-in-variables regressions to assess equivalence in method comparison studies, <https://dial.uclouvain.be/pr/boreal/object/boreal%3A135862/datastream/PDF_01/view>.

Bayesian Mixture Survival Models using Additive Mixture-of-Weibull Hazards, with Lasso Shrinkage and Stratification. As a Bayesian dynamic survival model, it relaxes the proportional-hazard assumption. Lasso shrinkage controls overfitting, given the increase in the number of free parameters in the model due to presence of two Weibull components in the hazard function.

Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.

This package provides probability computation, data generation, and model estimation for fully-visible Boltzmann machines. It follows the methods described in Nguyen and Wood (2016a) <doi:10.1162/NECO_a_00813> and Nguyen and Wood (2016b) <doi:10.1109/TNNLS.2015.2425898>.

This package provides a set of functions to allow analysis of count data (such as faecal egg count data) using Bayesian MCMC methods. Returns information on the possible values for mean count, coefficient of variation and zero inflation (true prevalence) present in the data. A complete faecal egg count reduction test (FECRT) model is implemented, which returns inference on the true efficacy of the drug from the pre- and post-treatment data provided, using non-parametric bootstrapping as well as using Bayesian MCMC. Functions to perform power analyses for faecal egg counts (including FECRT) are also provided.

The goal of the package is to provide an easy-to-use method for estimating degrees of relatedness (up to the second degree) for extreme low-coverage data. The package also allows users to quantify and visualise the level of confidence in the estimated degrees of relatedness.

Package for Breed Wheat Genomic Selection Pipeline. The R package BWGS is developed by Louis Gautier Tran <louis.gautier.tran@gmail.com> and Gilles Charmet <gilles.charmet@inra.fr>. This repository is forked from original repository <https://forgemia.inra.fr/umr-gdec/bwgs> and modified as a R package.

This package performs parametric mediation analysis using the Bayesian g-formula approach for binary and continuous outcomes. The methodology is based on Comment (2018) <doi:10.5281/zenodo.1285275> and a demonstration of its application can be found at Yimer et al. (2022) <doi:10.48550/arXiv.2210.08499>.

Derived from the work of Kruschke (2015, <ISBN:9780124058880>), the present package aims to provide a framework for conducting Bayesian analysis using Markov chain Monte Carlo (MCMC) sampling utilizing the Just Another Gibbs Sampler ('JAGS', Plummer, 2003, <https://mcmc-jags.sourceforge.io>). The initial version includes several modules for conducting Bayesian equivalents of chi-squared tests, analysis of variance (ANOVA), multiple (hierarchical) regression, softmax regression, and for fitting data (e.g., structural equation modeling).

Smoothed lexis diagrams with Bayesian method specifically tailored to cancer incidence data. Providing to calculating slope and constructing credible interval. LC Chien et al. (2015) <doi:10.1080/01621459.2015.1042106>. LH Chien et al. (2017) <doi:10.1002/cam4.1102>.

Unsupervised clustering protocol for large scale structured data, based on a low dimensional representation of the data. Dimensionality reduction is performed using a parallelized implementation of the t-Stochastic Neighboring Embedding algorithm (Garriga J. and Bartumeus F. (2018), <arXiv:1812.09869>).

This package provides a chemical speciation and toxicity prediction model for the toxicity of metals to aquatic organisms. The Biotic Ligand Model (BLM) engine was originally programmed in PowerBasic by Robert Santore and others. The main way the BLM can be used is to predict the toxicity of a metal to an organism with a known sensitivity (i.e., it is known how much of that metal must accumulate on that organism's biotic ligand to cause a physiological effect in a certain percentage of the population, such as a 20% loss in reproduction or a 50% mortality rate). The second way the BLM can be used is to estimate the chemical speciation of the metal and other constituents in water, including estimating the amount of metal accumulated to an organism's biotic ligand during a toxicity test. In the first application of the BLM, the amount of metal associated with a toxicity endpoint, or regulatory limit will be predicted, while in the second application, the amount of metal is known and the portions of that metal that exist in various forms will be determined. This version of the engine has been re-structured to perform the calculations in a different way that will make it more efficient in R, while also making it more flexible and easier to maintain in the future. Because of this, it does not currently match the desktop model exactly, but we hope to improve this comparability in the future.

This package provides a way to reduce model objects to necessary parts, making them easier to work with, store, share and simulate multiple values for new responses while allowing for parameter uncertainty.