R-universe search: sampler

mbaldassaro

sampler:Sample Design, Drawing & Data Analysis Using Data Frames

Determine sample sizes, draw samples, and conduct data analysis using data frames. It specifically enables you to determine simple random sample sizes, stratified sample sizes, and complex stratified sample sizes using a secondary variable such as population; draw simple random samples and stratified random samples from sampling data frames; determine which observations are missing from a random sample, missing by strata, duplicated within a dataset; and perform data analysis, including proportions, margins of error and upper and lower bounds for simple, stratified and cluster sample designs.

Maintained by Michael Baldassaro. Last updated 4 years ago.

55.0 match 7 stars 4.91 score 117 scripts

florianhartig

BayesianTools:General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

General-purpose MCMC and SMC samplers, as well as plots and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.

Maintained by Florian Hartig. Last updated 1 years ago.

bayes ecological-models mcmc optimization smc systems-biology cpp

21.1 match 122 stars 10.17 score 580 scripts 5 dependents

cran

bayesm:Bayesian Inference for Marketing/Micro-Econometrics

Covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009) For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch (Wiley first edition 2005 and second forthcoming) and Bayesian Non- and Semi-Parametric Methods and Applications (Princeton U Press 2014).

Maintained by Peter Rossi. Last updated 1 years ago.

openblas cpp

23.4 match 20 stars 8.20 score 322 scripts 43 dependents

vdorie

dbarts:Discrete Bayesian Additive Regression Trees Sampler

Fits Bayesian additive regression trees (BART; Chipman, George, and McCulloch (2010) <doi:10.1214/09-AOAS285>) while allowing the updating of predictors or response so that BART can be incorporated as a conditional model in a Gibbs/Metropolis-Hastings sampler. Also serves as a drop-in replacement for package 'BayesTree'.

Maintained by Vincent Dorie. Last updated 13 days ago.

cpp

16.3 match 56 stars 10.96 score 418 scripts 14 dependents

bsvars

bsvars:Bayesian Estimation of Structural Vector Autoregressive Models

Provides fast and efficient procedures for Bayesian analysis of Structural Vector Autoregressions. This package estimates a wide range of models, including homo-, heteroskedastic, and non-normal specifications. Structural models can be identified by adjustable exclusion restrictions, time-varying volatility, or non-normality. They all include a flexible three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses such as impulse responses, forecast error variance and historical decompositions, forecasting, verification of heteroskedasticity, non-normality, and hypotheses on autoregressive parameters, as well as analyses of structural shocks, volatilities, and fitted values. Beautiful plots, informative summary functions, and extensive documentation including the vignette by Woźniak (2024) <doi:10.48550/arXiv.2410.15090> complement all this. The implemented techniques align closely with those presented in Lütkepohl, Shang, Uzeda, & Woźniak (2024) <doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>. The 'bsvars' package is aligned regarding objects, workflows, and code structure with the R package 'bsvarSIGNs' by Wang & Woźniak (2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they constitute an integrated toolset.

Maintained by Tomasz Woźniak. Last updated 1 months ago.

bayesian-inference econometrics vector-autoregression openblas cpp openmp

18.0 match 46 stars 7.67 score 32 scripts 1 dependents

mlr-org

paradox:Define and Work with Parameter Spaces for Complex Algorithms

Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. Also includes statistical designs and random samplers. Objects are implemented as 'R6' classes.

Maintained by Martin Binder. Last updated 8 months ago.

experimental-design hyperparameters mlr3 transformations

11.8 match 29 stars 11.56 score 316 scripts 38 dependents

mrc-ide

monty:Monte Carlo Models

Experimental sources for the next generation of mcstate, now called 'monty', which will support much of the old mcstate functionality but new things like better parameter interfaces, Hamiltonian Monte Carlo, and other features.

Maintained by Rich FitzJohn. Last updated 1 months ago.

cpp

16.1 match 3 stars 7.52 score 29 scripts 3 dependents

mlverse

torch:Tensors and Neural Networks with 'GPU' Acceleration

Provides functionality to define and train neural networks similar to 'PyTorch' by Paszke et al (2019) <doi:10.48550/arXiv.1912.01703> but written entirely in R using the 'libtorch' library. Also supports low-level tensor operations and 'GPU' acceleration.

Maintained by Daniel Falbel. Last updated 7 days ago.

autograd deep-learning torch cpp

6.9 match 520 stars 16.52 score 1.4k scripts 38 dependents

mmi-codex

Xcertainty:Estimating Lengths and Uncertainty from Photogrammetric Imagery

Implementation of Bayesian models for estimating object lengths and morphological relationships between object lengths using photographic data collected from drones. The Bayesian model is described in "Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from drones" (Bierlich et al., 2021, <doi:10.3354/meps13814>).

Maintained by K.C. Bierlich. Last updated 5 months ago.

18.5 match 3 stars 5.95 score 10 scripts

eagerai

tfaddons:Interface to 'TensorFlow SIG Addons'

'TensorFlow SIG Addons' <https://www.tensorflow.org/addons> is a repository of community contributions that conform to well-established API patterns, but implement new functionality not available in core 'TensorFlow'. 'TensorFlow' natively supports a large number of operators, layers, metrics, losses, optimizers, and more. However, in a fast moving field like Machine Learning, there are many interesting new developments that cannot be integrated into core 'TensorFlow' (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community).

Maintained by Turgut Abdullayev. Last updated 3 years ago.

deep-learning keras neural-networks tensorflow tensorflow-addons tfa

18.5 match 20 stars 5.20 score 16 scripts

tdaverse

tdaunif:Uniform Manifold Samplers for Topological Data Analysis

Uniform random samples from simple manifolds, sometimes with noise, are commonly used to test topological data analytic (TDA) tools. This package includes samplers powered by two techniques: analytic volume-preserving parameterizations, as employed by Arvo (1995) <doi:10.1145/218380.218500>, and rejection sampling, as employed by Diaconis, Holmes, and Shahshahani (2013) <doi:10.1214/12-IMSCOLL1006>.

Maintained by Jason Cory Brunson. Last updated 9 months ago.

manifolds sampler tda topological-data-analysis topological-statistics

19.4 match 3 stars 4.95 score 8 scripts

lucas-castillo

samplr:Compare Human Performance to Sampling Algorithms

Understand human performance from the perspective of sampling, both looking at how people generate samples and how people use the samples they have generated. A longer overview and other resources can be found at <https://sampling.warwick.ac.uk>.

Maintained by Lucas Castillo. Last updated 3 days ago.

openblas cpp

15.9 match 2 stars 6.02 score 25 scripts

stla

matrixsampling:Simulations of Matrix Variate Distributions

Provides samplers for various matrix variate distributions: Wishart, inverse-Wishart, normal, t, inverted-t, Beta type I, Beta type II, Gamma, confluent hypergeometric. Allows to simulate the noncentral Wishart distribution without the integer restriction on the degrees of freedom.

Maintained by Stéphane Laurent. Last updated 6 years ago.

simulation

21.5 match 3 stars 4.22 score 37 scripts 1 dependents

grosssbm

missSBM:Handling Missing Data in Stochastic Block Models

When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0 due to missing information between node pairs), it is possible to account for the underlying process that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>, adjusts the popular stochastic block model from network data sampled under various missing data conditions, as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.

Maintained by Julien Chiquet. Last updated 4 days ago.

missing-data nas network-analysis network-dataset stochastic-block-model cpp

16.4 match 12 stars 5.53 score 19 scripts

stan-dev

cmdstanr:R Interface to 'CmdStan'

A lightweight interface to 'Stan' <https://mc-stan.org>. The 'CmdStanR' interface is an alternative to 'RStan' that calls the command line interface for compilation and running algorithms instead of interfacing with C++ via 'Rcpp'. This has many benefits including always being compatible with the latest version of Stan, fewer installation errors, fewer unexpected crashes in RStudio, and a more permissive license.

Maintained by Andrew Johnson. Last updated 9 months ago.

bayes bayesian markov-chain-monte-carlo maximum-likelihood mcmc stan variational-inference

6.5 match 145 stars 12.27 score 5.2k scripts 9 dependents

cole-monnahan-noaa

adnuts:No-U-Turn MCMC Sampling for 'ADMB' Models

Bayesian inference using the no-U-turn (NUTS) algorithm by Hoffman and Gelman (2014) <https://www.jmlr.org/papers/v15/hoffman14a.html>. Designed for 'AD Model Builder' ('ADMB') models, or when R functions for log-density and log-density gradient are available, such as 'Template Model Builder' models and other special cases. Functionality is similar to 'Stan', and the 'rstan' and 'shinystan' packages are used for diagnostics and inference.

Maintained by Cole Monnahan. Last updated 1 years ago.

11.3 match 24 stars 6.33 score 59 scripts

refunders

refund:Regression with Functional Data

Methods for regression for functional data, including function-on-scalar, scalar-on-function, and function-on-function regression. Some of the functions are applicable to image data.

Maintained by Julia Wrobel. Last updated 6 months ago.

6.9 match 41 stars 10.25 score 472 scripts 16 dependents

gertvv

hitandrun:"Hit and Run" and "Shake and Bake" for Sampling Uniformly from Convex Shapes

The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012) <doi:10.1016/j.ejor.2012.08.026>. van Valkenhoef, G., Tervonen, T., and Postmus, D. (2014) <doi:10.1016/j.ejor.2014.06.036>.

Maintained by Gert van Valkenhoef. Last updated 3 years ago.

openblas

9.9 match 16 stars 6.92 score 121 scripts 9 dependents

brubinstein

diffpriv:Easy Differential Privacy

An implementation of major general-purpose mechanisms for privatizing statistics, models, and machine learners, within the framework of differential privacy of Dwork et al. (2006) <doi:10.1007/11681878_14>. Example mechanisms include the Laplace mechanism for releasing numeric aggregates, and the exponential mechanism for releasing set elements. A sensitivity sampler (Rubinstein & Alda, 2017) <arXiv:1706.02562> permits sampling target non-private function sensitivity; combined with the generic mechanisms, it permits turn-key privatization of arbitrary programs.

Maintained by Benjamin Rubinstein. Last updated 3 years ago.

data-science differential-privacy diffpriv machine-learning statistics

9.9 match 66 stars 6.54 score 52 scripts

ludkinm

SBMSplitMerge:Inference for a Generalised SBM with a Split Merge Sampler

Inference in a Bayesian framework for a generalised stochastic block model. The generalised stochastic block model (SBM) can capture group structure in network data without requiring conjugate priors on the edge-states. Two sampling methods are provided to perform inference on edge parameters and block structure: a split-merge Markov chain Monte Carlo algorithm and a Dirichlet process sampler. Green, Richardson (2001) <doi:10.1111/1467-9469.00242>; Neal (2000) <doi:10.1080/10618600.2000.10474879>; Ludkin (2019) <arXiv:1909.09421>.

Maintained by Matthew Ludkin. Last updated 5 years ago.

23.3 match 2.70 score 3 scripts

bisaloo

mcmcensemble:Ensemble Sampler for Affine-Invariant MCMC

Provides ensemble samplers for affine-invariant Monte Carlo Markov Chain, which allow a faster convergence for badly scaled estimation problems. Two samplers are proposed: the 'differential.evolution' sampler from ter Braak and Vrugt (2008) <doi:10.1007/s11222-008-9104-9> and the 'stretch' sampler from Goodman and Weare (2010) <doi:10.2140/camcos.2010.5.65>.

Maintained by Hugo Gruson. Last updated 12 months ago.

mcmc mcmc-sampler

13.5 match 2 stars 4.60 score 8 scripts

spatstat

spatstat.random:Random Generation Functionality for the 'spatstat' Family

Functionality for random generation of spatial data in the 'spatstat' family of packages. Generates random spatial patterns of points according to many simple rules (complete spatial randomness, Poisson, binomial, random grid, systematic, cell), randomised alteration of patterns (thinning, random shift, jittering), simulated realisations of random point processes including simple sequential inhibition, Matern inhibition models, Neyman-Scott cluster processes (using direct, Brix-Kendall, or hybrid algorithms), log-Gaussian Cox processes, product shot noise cluster processes and Gibbs point processes (using Metropolis-Hastings birth-death-shift algorithm, alternating Gibbs sampler, or coupling-from-the-past perfect simulation). Also generates random spatial patterns of line segments, random tessellations, and random images (random noise, random mosaics). Excludes random generation on a linear network, which is covered by the separate package 'spatstat.linnet'.

Maintained by Adrian Baddeley. Last updated 2 days ago.

point-processes random-generation simulation spatial-sampling spatial-simulation cpp

5.7 match 5 stars 10.81 score 84 scripts 175 dependents

solivella

lda:Collapsed Gibbs Sampling Methods for Topic Models

Implements latent Dirichlet allocation (LDA) and related models. This includes (but is not limited to) sLDA, corrLDA, and the mixed-membership stochastic blockmodel. Inference for all of these models is implemented via a fast collapsed Gibbs sampler written in C. Utility functions for reading/writing data typically used in topic models, as well as tools for examining posterior distributions are also included.

Maintained by Santiago Olivella. Last updated 11 months ago.

8.0 match 7.62 score 548 scripts 11 dependents

bioc

matter:Out-of-core statistical computing and signal processing

Toolbox for larger-than-memory scientific computing and visualization, providing efficient out-of-core data structures using files or shared memory, for dense and sparse vectors, matrices, and arrays, with applications to nonuniformly sampled signals and images.

Maintained by Kylie A. Bemis. Last updated 3 months ago.

infrastructure datarepresentation dataimport dimensionreduction preprocessing cpp

6.3 match 57 stars 9.52 score 64 scripts 2 dependents

jcliff89

ContRespPP:Predictive Probability for a Continuous Response with an ANOVA Structure

A Bayesian approach to using predictive probability in an ANOVA construct with a continuous normal response, when threshold values must be obtained for the question of interest to be evaluated as successful (Sieck and Christensen (2021) <doi:10.1002/qre.2802>). The Bayesian Mission Mean (BMM) is used to evaluate a question of interest (that is, a mean that randomly selects combination of factor levels based on their probability of occurring instead of averaging over the factor levels, as in the grand mean). Under this construct, in contrast to a Gibbs sampler (or Metropolis-within-Gibbs sampler), a two-stage sampling method is required. The nested sampler determines the conditional posterior distribution of the model parameters, given Y, and the outside sampler determines the marginal posterior distribution of Y (also commonly called the predictive distribution for Y). This approach provides a sample from the joint posterior distribution of Y and the model parameters, while also accounting for the threshold value that must be obtained in order for the question of interest to be evaluated as successful.

Maintained by Victoria Sieck. Last updated 2 years ago.

15.3 match 3.70 score 4 scripts

nimble-dev

nimble:MCMC, Particle Filtering, and Programmable Hierarchical Modeling

A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, Laplace Approximation, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. 'NIMBLE' extends the 'BUGS'/'JAGS' language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the 'BUGS'/'JAGS' language for writing models, one can use 'NIMBLE' for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.

Maintained by Christopher Paciorek. Last updated 5 days ago.

bayesian-inference bayesian-methods hierarchical-models mcmc probabilistic-programming openblas cpp

4.2 match 169 stars 12.97 score 2.6k scripts 19 dependents

stan-dev

bayesplot:Plotting for Bayesian Models

Plotting functions for posterior analysis, MCMC diagnostics, prior and posterior predictive checks, and other visualizations to support the applied Bayesian workflow advocated in Gabry, Simpson, Vehtari, Betancourt, and Gelman (2019) <doi:10.1111/rssa.12378>. The package is designed not only to provide convenient functionality for users, but also a common set of functions that can be easily used by developers working on a variety of R packages for Bayesian modeling, particularly (but not exclusively) packages interfacing with 'Stan'.

Maintained by Jonah Gabry. Last updated 1 months ago.

bayesian ggplot2 mcmc pandoc stan statistical-graphics visualization

3.0 match 436 stars 16.69 score 6.5k scripts 98 dependents

jmm34

bayess:Bayesian Essentials with R

Allows the reenactment of the R programs used in the book Bayesian Essentials with R without further programming. R code being available as well, they can be modified by the user to conduct one's own simulations. Marin J.-M. and Robert C. P. (2014) <doi:10.1007/978-1-4614-8687-9>.

Maintained by Jean-Michel Marin. Last updated 1 years ago.

12.2 match 3 stars 4.01 score 68 scripts

scollinselliott

eratosthenes:Archaeological Synchronism

Estimates unknown historical or archaeological dates subject to relationships with other dates and absolute constraints, derived as marginal densities from the full joint conditional distribution. Includes rule-based estimation of the production dates of artifact types. Collins-Elliott (2024) <https://volweb.utk.edu/~scolli46/sceGUTChronology.pdf>.

Maintained by Stephen A. Collins-Elliott. Last updated 6 months ago.

archaeology chronology gibbs-sampler cpp

9.4 match 6 stars 5.08 score 2 scripts

greta-dev

greta:Simple and Scalable Statistical Modelling in R

Write statistical models in R and fit them by MCMC and optimisation on CPUs and GPUs, using Google 'TensorFlow'. greta lets you write your own model like in BUGS, JAGS and Stan, except that you write models right in R, it scales well to massive datasets, and it’s easy to extend and build on. See the website for more information, including tutorials, examples, package documentation, and the greta forum.

Maintained by Nicholas Tierney. Last updated 6 days ago.

3.8 match 566 stars 12.53 score 396 scripts 6 dependents

alarm-redist

redist:Simulation Methods for Legislative Redistricting

Enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements. Tools for analysis such as computation of various summary statistics and plotting functionality are also included. The package implements the SMC algorithm of McCartan and Imai (2023) <doi:10.1214/23-AOAS1763>, the enumeration algorithm of Fifield, Imai, Kawahara, and Kenny (2020) <doi:10.1080/2330443X.2020.1791773>, the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) <doi:10.1080/10618600.2020.1739532>, the Merge-split/Recombination algorithms of Carter et al. (2019) <arXiv:1911.01503> and DeFord et al. (2021) <doi:10.1162/99608f92.eb30390f>, and the Short-burst optimization algorithm of Cannon et al. (2020) <arXiv:2011.02288>.

Maintained by Christopher T. Kenny. Last updated 2 months ago.

geospatial gerrymandering redistricting sampling openblas cpp openmp

5.1 match 68 stars 9.17 score 259 scripts

mqbssppe

fabMix:Overfitting Bayesian Mixtures of Factor Analyzers with Parsimonious Covariance and Unknown Number of Components

Model-based clustering of multivariate continuous data using Bayesian mixtures of factor analyzers (Papastamoulis (2019) <DOI:10.1007/s11222-019-09891-z> (2018) <DOI:10.1016/j.csda.2018.03.007>). The number of clusters is estimated using overfitting mixture models (Rousseau and Mengersen (2011) <DOI:10.1111/j.1467-9868.2011.00781.x>): suitable prior assumptions ensure that asymptotically the extra components will have zero posterior weight, therefore, the inference is based on the ``alive'' components. A Gibbs sampler is implemented in order to (approximately) sample from the posterior distribution of the overfitting mixture. A prior parallel tempering scheme is also available, which allows to run multiple parallel chains with different prior distributions on the mixture weights. These chains run in parallel and can swap states using a Metropolis-Hastings move. Eight different parameterizations give rise to parsimonious representations of the covariance per cluster (following Mc Nicholas and Murphy (2008) <DOI:10.1007/s11222-008-9056-0>). The model parameterization and number of factors is selected according to the Bayesian Information Criterion. Identifiability issues related to label switching are dealt by post-processing the simulated output with the Equivalence Classes Representatives algorithm (Papastamoulis and Iliopoulos (2010) <DOI:10.1198/jcgs.2010.09008>, Papastamoulis (2016) <DOI:10.18637/jss.v069.c01>).

Maintained by Panagiotis Papastamoulis. Last updated 1 years ago.

openblas cpp openmp

21.9 match 2.09 score 41 scripts 1 dependents

gertraudmalsinerwalli

telescope:Bayesian Mixtures with an Unknown Number of Components

Fits Bayesian finite mixtures with an unknown number of components using the telescoping sampler and different component distributions. For more details see Frühwirth-Schnatter et al. (2021) <doi:10.1214/21-BA1294>.

Maintained by Gertraud Malsiner-Walli. Last updated 2 months ago.

15.0 match 3.00 score 4 scripts

tkrisztin

estimateW:Estimation of Spatial Weight Matrices

Bayesian estimation of spatial weight matrices in spatial econometric panel models. Allows for estimation of spatial autoregressive (SAR), spatial Durbin (SDM), and spatially lagged explanatory variable (SLX) type specifications featuring an unknown spatial weight matrix. Methodological details are given in Krisztin and Piribauer (2022) <doi:10.1080/17421772.2022.2095426>.

Maintained by Tamas Krisztin. Last updated 2 years ago.

openblas cpp

16.1 match 2.70 score 2 scripts

kwb-r

kwb.monitoring:Functions Used Within Different Kwb Monitoring Projects

Functions used within different KWB projects dealing with monitoring data.

Maintained by Hauke Sonnenberg. Last updated 6 years ago.

monitoring

11.3 match 3.78 score 3 scripts 4 dependents

seunghyunmin

EAinference:Estimator Augmentation and Simulation-Based Inference

Estimator augmentation methods for statistical inference on high-dimensional data, as described in Zhou, Q. (2014) <arXiv:1401.4425v2> and Zhou, Q. and Min, S. (2017) <doi:10.1214/17-EJS1309>. It provides several simulation-based inference methods: (a) Gaussian and wild multiplier bootstrap for lasso, group lasso, scaled lasso, scaled group lasso and their de-biased estimators, (b) importance sampler for approximating p-values in these methods, (c) Markov chain Monte Carlo lasso sampler with applications in post-selection inference.

Maintained by Seunghyun Min. Last updated 6 years ago.

13.3 match 3.11 score 13 scripts

scheidan

adaptMCMC:Implementation of a Generic Adaptive Monte Carlo Markov Chain Sampler

Enables sampling from arbitrary distributions if the log density is known up to a constant; a common situation in the context of Bayesian inference. The implemented sampling algorithm was proposed by Vihola (2012) <DOI:10.1007/s11222-011-9269-5> and achieves often a high efficiency by tuning the proposal distributions to a user defined acceptance rate.

Maintained by Andreas Scheidegger. Last updated 1 years ago.

6.5 match 10 stars 6.31 score 133 scripts 10 dependents

trackage

tripEstimation:Metropolis Sampler and Supporting Functions for Estimating Animal Movement from Archival Tags and Satellite Fixes

Data handling and estimation functions for animal movement estimation from archival or satellite tags. Helper functions are included for making image summaries binned by time interval from Markov Chain Monte Carlo simulations.

Maintained by Michael D. Sumner. Last updated 2 years ago.

9.5 match 4 stars 4.19 score 13 scripts

limengbinggz

ddtlcm:Latent Class Analysis with Dirichlet Diffusion Tree Process Prior

Implements a Bayesian algorithm for overcoming weak separation in Bayesian latent class analysis. Reference: Li et al. (2023) <arXiv:2306.04700>.

Maintained by Mengbing Li. Last updated 8 months ago.

6.6 match 6 stars 5.80 score 8 scripts

richfitz

diversitree:Comparative 'Phylogenetic' Analyses of Diversification

Contains a number of comparative 'phylogenetic' methods, mostly focusing on analysing diversification and character evolution. Contains implementations of 'BiSSE' (Binary State 'Speciation' and Extinction) and its unresolved tree extensions, 'MuSSE' (Multiple State 'Speciation' and Extinction), 'QuaSSE', 'GeoSSE', and 'BiSSE-ness' Other included methods include Markov models of discrete and continuous trait evolution and constant rate 'speciation' and extinction.

Maintained by Richard G. FitzJohn. Last updated 6 months ago.

fftw3 gsl openblas cpp

4.5 match 33 stars 8.51 score 524 scripts 4 dependents

tmsalab

dina:Bayesian Estimation of DINA Model

Estimate the Deterministic Input, Noisy "And" Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) <doi:10.3102/1076998615595403>.

Maintained by James Joseph Balamuta. Last updated 5 years ago.

armadillo bayesian gibbs-sampler irt item-response-theory psychometrics rcpp rcpparmadillo openblas cpp

9.8 match 14 stars 3.85 score 3 scripts

university-of-newcastle-research

pmwg:Particle Metropolis Within Gibbs

Provides an R implementation of the Particle Metropolis within Gibbs sampler for model parameter, covariance matrix and random effect estimation. A more general implementation of the sampler based on the paper by Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D. (2020) <doi:10.1016/j.jmp.2020.102368>. An HTML tutorial document describing the package is available at <https://university-of-newcastle-research.github.io/samplerDoc/> and includes several detailed examples, some background and troubleshooting steps.

Maintained by Gavin Cooper. Last updated 1 years ago.

7.4 match 3 stars 4.94 score 29 scripts

shangzhi-hong

RfEmpImp:Multiple Imputation using Chained Random Forests

An R package for multiple imputation using chained random forests. Implemented methods can handle missing data in mixed types of variables by using prediction-based or node-based conditional distributions constructed using random forests. For prediction-based imputation, the method based on the empirical distribution of out-of-bag prediction errors of random forests and the method based on normality assumption for prediction errors of random forests are provided for imputing continuous variables. And the method based on predicted probabilities is provided for imputing categorical variables. For node-based imputation, the method based on the conditional distribution formed by the predicting nodes of random forests, and the method based on proximity measures of random forests are provided. More details of the statistical methods can be found in Hong et al. (2020) <arXiv:2004.14823>.

Maintained by Shangzhi Hong. Last updated 2 years ago.

imputation missing-data random-forest

8.1 match 5 stars 4.40 score 8 scripts

asmahani

sns:Stochastic Newton Sampler (SNS)

Stochastic Newton Sampler (SNS) is a Metropolis-Hastings-based, Markov Chain Monte Carlo sampler for twice differentiable, log-concave probability density functions (PDFs) where the proposal density function is a multivariate Gaussian resulting from a second-order Taylor-series expansion of log-density around the current point. The mean of the Gaussian proposal is the full Newton-Raphson step from the current point. A Boolean flag allows for switching from SNS to Newton-Raphson optimization (by choosing the mean of proposal function as next point). This can be used during burn-in to get close to the mode of the PDF (which is unique due to concavity). For high-dimensional densities, mixing can be improved via 'state space partitioning' strategy, in which SNS is applied to disjoint subsets of state space, wrapped in a Gibbs cycle. Numerical differentiation is available when analytical expressions for gradient and Hessian are not available. Facilities for validation and numerical differentiation of log-density are provided. Note: Formerly available versions of the MfUSampler can be obtained from the archive <https://cran.r-project.org/src/contrib/Archive/MfUSampler/>.

Maintained by Alireza Mahani. Last updated 2 years ago.

10.4 match 3.30 score 33 scripts 1 dependents

jarod-smithy

baygel:Bayesian Shrinkage Estimators for Precision Matrices in Gaussian Graphical Models

This R package offers block Gibbs samplers for the Bayesian (adaptive) graphical lasso, ridge, and naive elastic net priors. These samplers facilitate the simulation of the posterior distribution of precision matrices for Gaussian distributed data and were originally proposed by: Wang (2012) <doi:10.1214/12-BA729>; Smith et al. (2022) <doi:10.48550/arXiv.2210.16290> and Smith et al. (2023) <doi:10.48550/arXiv.2306.14199>, respectively.

Maintained by Jarod Smith. Last updated 1 years ago.

openblas cpp openmp

12.3 match 2.70 score 2 scripts

emmekeaarts

mHMMbayes:Multilevel Hidden Markov Models Using Bayesian Estimation

An implementation of the multilevel (also known as mixed or random effects) hidden Markov model using Bayesian estimation in R. The multilevel hidden Markov model (HMM) is a generalization of the well-known hidden Markov model, for the latter see Rabiner (1989) <doi:10.1109/5.18626>. The multilevel HMM is tailored to accommodate (intense) longitudinal data of multiple individuals simultaneously, see e.g., de Haan-Rietdijk et al. <doi:10.1080/00273171.2017.1370364>. Using a multilevel framework, we allow for heterogeneity in the model parameters (transition probability matrix and conditional distribution), while estimating one overall HMM. The model can be fitted on multivariate data with either a categorical, normal, or Poisson distribution, and include individual level covariates (allowing for e.g., group comparisons on model parameters). Parameters are estimated using Bayesian estimation utilizing the forward-backward recursion within a hybrid Metropolis within Gibbs sampler. Missing data (NA) in the dependent variables is accommodated assuming MAR. The package also includes various visualization options, a function to simulate data, and a function to obtain the most likely hidden state sequence for each individual using the Viterbi algorithm.

Maintained by Emmeke Aarts. Last updated 12 months ago.

cpp

5.4 match 17 stars 6.08 score 35 scripts

km4ivi

whSample:Utilities for Sampling

Interactive tools for generating random samples. Users select an .xlsx, .csv, or delimited .txt file with population data and are walked through selecting the sample type (Simple Random Sample or Stratified), the number of backups desired, and a "stratify_on" value (if desired). The sample size is determined using a normal approximation to the hypergeometric distribution based on Nicholson (1956) <doi:10.1214/aoms/1177728270>. An .xlsx file is created with the sample and key metadata for reference. It is menu-driven and lets users pick an output directory. See vignettes for a detailed walk-through.

Maintained by Paul West. Last updated 4 years ago.

8.1 match 4.00 score 5 scripts

joliencremers

bpnreg:Bayesian Projected Normal Regression Models for Circular Data

Fitting Bayesian multiple and mixed-effect regression models for circular data based on the projected normal distribution. Both continuous and categorical predictors can be included. Sampling from the posterior is performed via an MCMC algorithm. Posterior descriptives of all parameters, model fit statistics and Bayes factors for hypothesis tests for inequality constrained hypotheses are provided. See Cremers, Mulder & Klugkist (2018) <doi:10.1111/bmsp.12108> and Nuñez-Antonio & Guttiérez-Peña (2014) <doi:10.1016/j.csda.2012.07.025>.

Maintained by Jolien Cremers. Last updated 1 years ago.

openblas cpp openmp

5.3 match 14 stars 6.15 score 101 scripts

marshalllab

MGDrivE2:Mosquito Gene Drive Explorer 2

A simulation modeling framework which significantly extends capabilities from the 'MGDrivE' simulation package via a new mathematical and computational framework based on stochastic Petri nets. For more information about 'MGDrivE', see our publication: <https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13318>. Some of the notable capabilities of 'MGDrivE2' include: incorporation of human populations, epidemiological dynamics, time-varying parameters, and a continuous-time simulation framework with various sampling algorithms for both deterministic and stochastic interpretations. 'MGDrivE2' relies on the genetic inheritance structures provided in package 'MGDrivE', so we suggest installing that package initially.

Maintained by Sean L. Wu. Last updated 4 years ago.

5.1 match 6 stars 6.33 score 30 scripts

danheck

TreeBUGS:Hierarchical Multinomial Processing Tree Modeling

User-friendly analysis of hierarchical multinomial processing tree (MPT) models that are often used in cognitive psychology. Implements the latent-trait MPT approach (Klauer, 2010) <DOI:10.1007/s11336-009-9141-0> and the beta-MPT approach (Smith & Batchelder, 2010) <DOI:10.1016/j.jmp.2009.06.007> to model heterogeneity of participants. MPT models are conveniently specified by an .eqn-file as used by other MPT software and data are provided by a .csv-file or directly in R. Models are either fitted by calling JAGS or by an MPT-tailored Gibbs sampler in C++ (only for nonhierarchical and beta MPT models). Provides tests of heterogeneity and MPT-tailored summaries and plotting functions. A detailed documentation is available in Heck, Arnold, & Arnold (2018) <DOI:10.3758/s13428-017-0869-7> and a tutorial on MPT modeling can be found in Schmidt, Erdfelder, & Heck (2023) <DOI:10.1037/met0000561>.

Maintained by Daniel W. Heck. Last updated 4 days ago.

openblas cpp jags

4.0 match 12 stars 8.01 score 53 scripts 1 dependents

paul-buerkner

brms:Bayesian Regression Models using 'Stan'

Fit Bayesian generalized (non-)linear multivariate multilevel models using 'Stan' for full Bayesian inference. A wide range of distributions and link functions are supported, allowing users to fit -- among others -- linear, robust linear, count data, survival, response times, ordinal, zero-inflated, hurdle, and even self-defined mixture models all in a multilevel context. Further modeling options include both theory-driven and data-driven non-linear terms, auto-correlation structures, censoring and truncation, meta-analytic standard errors, and quite a few more. In addition, all parameters of the response distribution can be predicted in order to perform distributional regression. Prior specifications are flexible and explicitly encourage users to apply prior distributions that actually reflect their prior knowledge. Models can easily be evaluated and compared using several methods assessing posterior or prior predictions. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Bürkner (2018) <doi:10.32614/RJ-2018-017>; Bürkner (2021) <doi:10.18637/jss.v100.i05>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>.

Maintained by Paul-Christian Bürkner. Last updated 3 days ago.

bayesian-inference brms multilevel-models stan statistical-models

1.8 match 1.3k stars 16.61 score 13k scripts 34 dependents

rstudio

tfprobability:Interface to 'TensorFlow Probability'

Interface to 'TensorFlow Probability', a 'Python' library built on 'TensorFlow' that makes it easy to combine probabilistic models and deep learning on modern hardware ('TPU', 'GPU'). 'TensorFlow Probability' includes a wide selection of probability distributions and bijectors, probabilistic layers, variational inference, Markov chain Monte Carlo, and optimizers such as Nelder-Mead, BFGS, and SGLD.

Maintained by Tomasz Kalinowski. Last updated 3 years ago.

3.4 match 54 stars 8.63 score 221 scripts 3 dependents

iembry

ie2miscdata:Irucka Embry's Miscellaneous USGS Data Collection

A collection of Irucka Embry's miscellaneous USGS data sets (USGS Parameter codes with fixed values, USGS global time zone codes, and US Air Force Global Engineering Weather Data). Irucka created these data sets while a Cherokee Nation Technology Solutions (CNTS) United States Geological Survey (USGS) Contractor and/or USGS employee.

Maintained by Irucka Embry. Last updated 2 years ago.

7.3 match 4.00 score

stan-dev

rstan:R Interface to Stan

User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.

Maintained by Ben Goodrich. Last updated 2 days ago.

bayesian-data-analysis bayesian-inference bayesian-statistics mcmc stan cpp

1.5 match 1.1k stars 18.67 score 14k scripts 279 dependents

stefanwilhelm

tmvtnorm:Truncated Multivariate Normal and Student t Distribution

Random number generation for the truncated multivariate normal and Student t distribution. Computes probabilities, quantiles and densities, including one-dimensional and bivariate marginal densities. Computes first and second moments (i.e. mean and covariance matrix) for the double-truncated multinormal case.

Maintained by Stefan Wilhelm. Last updated 1 years ago.

fortran openblas

3.1 match 1 stars 8.84 score 338 scripts 59 dependents

bioc

BiRewire:High-performing routines for the randomization of a bipartite graph (or a binary event matrix), undirected and directed signed graph preserving degree distribution (or marginal totals)

Fast functions for bipartite network rewiring through N consecutive switching steps (See References) and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. Includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks and directed signed networks is also provided. Starting from version 1.9.7 a more precise bound (especially for small network) has been implemented. Starting from version 2.2.0 the analysis routine is more complete and a visual montioring of the underlying Markov Chain has been implemented. Starting from 3.6.0 the library can handle also matrices with NA (not for the directed signed graphs). Since version 3.27.1 it is possible to add a constraint for dsg generation: usually positive and negative arc between two nodes could be not accepted.

Maintained by Andrea Gobbi. Last updated 5 months ago.

network

6.0 match 4.54 score 35 scripts

danielturek

nimbleSCR:Spatial Capture-Recapture (SCR) Methods Using 'nimble'

Provides utility functions, distributions, and fitting methods for Bayesian Spatial Capture-Recapture (SCR) and Open Population Spatial Capture-Recapture (OPSCR) modelling using the nimble package (de Valpine et al. 2017 <doi:10.1080/10618600.2016.1172487 >). Development of the package was motivated primarily by the need for flexible and efficient analysis of large-scale SCR data (Bischof et al. 2020 <doi:10.1073/pnas.2011383117 >). Computational methods and techniques implemented in nimbleSCR include those discussed in Turek et al. 2021 <doi:10.1002/ecs2.3385>; among others. For a recent application of nimbleSCR, see Milleret et al. (2021) <doi:10.1098/rsbl.2021.0128>.

Maintained by Daniel Turek. Last updated 2 years ago.

6.2 match 4.29 score 388 scripts

cleanzr

blink:Record Linkage for Empirically Motivated Priors

An implementation of the model in Steorts (2015) <DOI:10.1214/15-BA965SI>, which performs Bayesian entity resolution for categorical and text data, for any distance function defined by the user. In addition, the precision and recall are in the package to allow one to compare to any other comparable method such as logistic regression, Bayesian additive regression trees (BART), or random forests. The experiments are reproducible and illustrated using a simple vignette. LICENSE: GPL-3 + file license.

Maintained by Rebecca Steorts. Last updated 1 years ago.

4.7 match 5 stars 5.72 score 70 scripts 1 dependents

genentech

psborrow2:Bayesian Dynamic Borrowing Analysis and Simulation

Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from <https://stan-dev.r-universe.dev>.

Maintained by Matt Secrest. Last updated 1 months ago.

bayesian-dynamic-borrowing psborrow2 simulation-study

3.4 match 18 stars 7.87 score 16 scripts

r-lib

cpp11:A C++11 Interface for R's C Interface

Provides a header only, C++11 interface to R's C interface. Compared to other approaches 'cpp11' strives to be safe against long jumps from the C API as well as C++ exceptions, conform to normal R function semantics and supports interaction with 'ALTREP' vectors.

Maintained by Davis Vaughan. Last updated 13 days ago.

cpp cpp11

1.5 match 212 stars 17.69 score 104 scripts 8.6k dependents

asmahani

MfUSampler:Multivariate-from-Univariate (MfU) MCMC Sampler

Convenience functions for multivariate MCMC using univariate samplers including: slice sampler with stepout and shrinkage (Neal (2003) <DOI:10.1214/aos/1056562461>), adaptive rejection sampler (Gilks and Wild (1992) <DOI:10.2307/2347565>), adaptive rejection Metropolis (Gilks et al (1995) <DOI:10.2307/2986138>), and univariate Metropolis with Gaussian proposal.

Maintained by Alireza S. Mahani. Last updated 2 years ago.

8.5 match 3.08 score 20 scripts 2 dependents

drkowal

SeBR:Semiparametric Bayesian Regression Analysis

Monte Carlo sampling algorithms for semiparametric Bayesian regression analysis. These models feature a nonparametric (unknown) transformation of the data paired with widely-used regression models including linear regression, spline regression, quantile regression, and Gaussian processes. The transformation enables broader applicability of these key models, including for real-valued, positive, and compactly-supported data with challenging distributional features. The samplers prioritize computational scalability and, for most cases, Monte Carlo (not MCMC) sampling for greater efficiency. Details of the methods and algorithms are provided in Kowal and Wu (2024) <doi:10.1080/01621459.2024.2395586>.

Maintained by Dan Kowal. Last updated 6 days ago.

5.9 match 1 stars 4.30 score 3 scripts

cran

GiRaF:Gibbs Random Fields Analysis

Allows calculation on, and sampling from Gibbs Random Fields, and more precisely general homogeneous Potts model. The primary tool is the exact computation of the intractable normalising constant for small rectangular lattices. Beside the latter function, it contains method that give exact sample from the likelihood for small enough rectangular lattices or approximate sample from the likelihood using MCMC samplers for large lattices.

Maintained by Julien Stoehr. Last updated 4 years ago.

cpp

6.4 match 3.95 score 3 dependents

finlaycampbell

outbreaker2:Bayesian Reconstruction of Disease Outbreaks by Combining Epidemiologic and Genomic Data

Bayesian reconstruction of disease outbreaks using epidemiological and genetic information. Jombart T, Cori A, Didelot X, Cauchemez S, Fraser C and Ferguson N. 2014. <doi:10.1371/journal.pcbi.1003457>. Campbell, F, Cori A, Ferguson N, Jombart T. 2019. <doi:10.1371/journal.pcbi.1006930>.

Maintained by Finlay Campbell. Last updated 6 months ago.

cpp

3.3 match 7.67 score 101 scripts 1 dependents

ghislainv

jSDM:Joint Species Distribution Models

Fits joint species distribution models ('jSDM') in a hierarchical Bayesian framework (Warton and al. 2015 <doi:10.1016/j.tree.2015.09.007>). The Gibbs sampler is written in 'C++'. It uses 'Rcpp', 'Armadillo' and 'GSL' to maximize computation efficiency.

Maintained by Ghislain Vieilledent. Last updated 2 years ago.

gsl openblas cpp

4.2 match 11 stars 5.87 score 68 scripts

dcgerard

hwep:Hardy-Weinberg Equilibrium in Polyploids

Inference concerning equilibrium and random mating in autopolyploids. Methods are available to test for equilibrium and random mating at any even ploidy level (>2) in the presence of double reduction at biallelic loci. For autopolyploid populations in equilibrium, methods are available to estimate the degree of double reduction. We also provide functions to calculate genotype frequencies at equilibrium, or after one or several rounds of random mating, given rates of double reduction. The main function is hwefit(). This material is based upon work supported by the National Science Foundation under Grant No. 2132247. The opinions, findings, and conclusions or recommendations expressed are those of the author and do not necessarily reflect the views of the National Science Foundation. For details of these methods, see Gerard (2023a) <doi:10.1111/biom.13722> and Gerard (2023b) <doi:10.1111/1755-0998.13856>.

Maintained by David Gerard. Last updated 2 years ago.

cpp

5.1 match 3 stars 4.72 score 35 scripts

ingaschwabe

BayesTwin:Bayesian Analysis of Item-Level Twin Data

Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) <doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014) <doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016) <doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016) <doi:10.1016/j.lindif.2017.01.018>. Caution! The subroutines of this package rely on the program JAGS, which can be freely obtained from http://mcmc-jags.sourceforge.net.

Maintained by Inga Schwabe. Last updated 6 years ago.

bayesian genetics heritability item-response-theory mcmc-sampler psychometrics jags cpp

7.5 match 3.04 score 11 scripts

cran

runMCMCbtadjust:Runs Monte Carlo Markov Chain - With Either 'JAGS', 'nimble' or 'greta' - While Adjusting Burn-in and Thinning Parameters

The function runMCMC_btadjust() returns a mcmc.list object which is the output of a Markov Chain Monte Carlo obtained - from either 'JAGS', 'nimble' or 'greta' - after adjusting burn-in and thinning parameters to meet pre-specified criteria in terms of convergence & effective sample size. Used with 'nimble', runMCMC_btadjust() allows extra calculations (e.g. information criteria for model comparison and goodness-of-fit p-values for model diagnosis).

Maintained by Frédéric Gosselin. Last updated 7 months ago.

7.1 match 3.21 score 18 scripts

gavinsimpson

gratia:Graceful 'ggplot'-Based Graphics and Other Functions for GAMs Fitted Using 'mgcv'

Graceful 'ggplot'-based graphics and utility functions for working with generalized additive models (GAMs) fitted using the 'mgcv' package. Provides a reimplementation of the plot() method for GAMs that 'mgcv' provides, as well as 'tidyverse' compatible representations of estimated smooths.

Maintained by Gavin L. Simpson. Last updated 21 hours ago.

distributional-regression gam gamm generalized-additive-mixed-models generalized-additive-models ggplot2 glm lm mgcv penalized-spline random-effects smoothing splines

1.7 match 217 stars 12.99 score 1.6k scripts 2 dependents

hjboonstra

mcmcsae:Markov Chain Monte Carlo Small Area Estimation

Fit multi-level models with possibly correlated random effects using Markov Chain Monte Carlo simulation. Such models allow smoothing over space and time and are useful in, for example, small area estimation.

Maintained by Harm Jan Boonstra. Last updated 3 months ago.

cpp

8.7 match 2.48 score 8 scripts

martynplummer

rjags:Bayesian Graphical Models using MCMC

Interface to the JAGS MCMC library.

Maintained by Martyn Plummer. Last updated 7 months ago.

jags cpp

2.3 match 7 stars 9.60 score 4.0k scripts 165 dependents

benrenard

HydroPortailStats:'HydroPortail' Statistical Functions

Statistical functions used in the French 'HydroPortail' <https://hydro.eaufrance.fr/>. This includes functions to estimate distributions, quantile curves and uncertainties, along with various other utilities. Technical details are available (in French) in Renard (2016) <https://hal.inrae.fr/hal-02605318>.

Maintained by Benjamin Renard. Last updated 4 months ago.

hydrology statistical-distributions statistics

5.5 match 3 stars 3.78 score 1 scripts

danielturek

nimbleHMC:Hamiltonian Monte Carlo and Other Gradient-Based MCMC Sampling Algorithms for 'nimble'

Provides gradient-based MCMC sampling algorithms for use with the MCMC engine provided by the 'nimble' package. This includes two versions of Hamiltonian Monte Carlo (HMC) No-U-Turn (NUTS) sampling, and (under development) Langevin samplers. The `NUTS_classic` sampler implements the original HMC-NUTS algorithm as described in Hoffman and Gelman (2014) <doi:10.48550/arXiv.1111.4246>. The `NUTS` sampler is a modern version of HMC-NUTS sampling matching the HMC sampler available in version 2.32.2 of Stan (Stan Development Team, 2023). In addition, convenience functions are provided for generating and modifying MCMC configuration objects which employ HMC sampling.

Maintained by Daniel Turek. Last updated 3 months ago.

11.6 match 1.79 score 31 scripts

epimodel

EpiModel:Mathematical Modeling of Infectious Disease Dynamics

Tools for simulating mathematical models of infectious disease dynamics. Epidemic model classes include deterministic compartmental models, stochastic individual-contact models, and stochastic network models. Network models use the robust statistical methods of exponential-family random graph models (ERGMs) from the Statnet suite of software packages in R. Standard templates for epidemic modeling include SI, SIR, and SIS disease types. EpiModel features an API for extending these templates to address novel scientific research aims. Full methods for EpiModel are detailed in Jenness et al. (2018, <doi:10.18637/jss.v084.i08>).

Maintained by Samuel Jenness. Last updated 2 months ago.

agent-based-modeling epidemics epidemiology infectious-diseases network-graph cpp

1.8 match 250 stars 11.57 score 315 scripts

bioc

BASiCS:Bayesian Analysis of Single-Cell Sequencing data

Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend.

Maintained by Catalina Vallejos. Last updated 5 months ago.

immunooncology normalization sequencing rnaseq software geneexpression transcriptomics singlecell differentialexpression bayesian cellbiology bioconductor-package gene-expression rcpp rcpparmadillo scrna-seq single-cell openblas cpp openmp

2.0 match 83 stars 10.26 score 368 scripts 1 dependents

bioc

bacon:Controlling bias and inflation in association studies using the empirical null distribution

Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores.

Maintained by Maarten van Iterson. Last updated 5 months ago.

immunooncology statisticalmethod bayesian regression genomewideassociation transcriptomics rnaseq methylationarray batcheffect multiplecomparison

3.9 match 5.19 score 97 scripts

tmsalab

fourPNO:Bayesian 4 Parameter Item Response Model

Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.

Maintained by Steven Andrew Culpepper. Last updated 5 years ago.

armadillo cognitive-diagnostic-models gibbs-sampler item-response-theory rcpp rcpparmadillo openblas cpp openmp

7.5 match 1 stars 2.70 score 5 scripts

cran

BayesFluxR:Implementation of Bayesian Neural Networks

Implementation of 'BayesFlux.jl' for R; It extends the famous 'Flux.jl' machine learning library to Bayesian Neural Networks. The goal is not to have the fastest production ready library, but rather to allow more people to be able to use and research on Bayesian Neural Networks.

Maintained by Enrico Wegner. Last updated 1 years ago.

11.3 match 1.70 score

agandy

mcunit:Unit Tests for MC Methods

Unit testing for Monte Carlo methods, particularly Markov Chain Monte Carlo (MCMC) methods, are implemented as extensions of the 'testthat' package. The MCMC methods check whether the MCMC chain has the correct invariant distribution. They do not check other properties of successful samplers such as whether the chain can reach all points, i.e. whether is recurrent. The tests require the ability to sample from the prior and to run steps of the MCMC chain. The methodology is described in Gandy and Scott (2020) <arXiv:2001.06465>.

Maintained by Axel Gandy. Last updated 3 years ago.

4.8 match 4.00 score 1 scripts

growthcharts

brokenstick:Broken Stick Model for Irregular Longitudinal Data

Data on multiple individuals through time are often sampled at times that differ between persons. Irregular observation times can severely complicate the statistical analysis of the data. The broken stick model approximates each subject’s trajectory by one or more connected line segments. The times at which segments connect (breakpoints) are identical for all subjects and under control of the user. A well-fitting broken stick model effectively transforms individual measurements made at irregular times into regular trajectories with common observation times. Specification of the model requires three variables: time, measurement and subject. The model is a special case of the linear mixed model, with time as a linear B-spline and subject as the grouping factor. The main assumptions are: subjects are exchangeable, trajectories between consecutive breakpoints are straight, random effects follow a multivariate normal distribution, and unobserved data are missing at random. The package contains functions for fitting the broken stick model to data, for predicting curves in new data and for plotting broken stick estimates. The package supports two optimization methods, and includes options to structure the variance-covariance matrix of the random effects. The analyst may use the software to smooth growth curves by a series of connected straight lines, to align irregularly observed curves to a common time grid, to create synthetic curves at a user-specified set of breakpoints, to estimate the time-to-time correlation matrix and to predict future observations. See <doi:10.18637/jss.v106.i07> for additional documentation on background, methodology and applications.

Maintained by Stef van Buuren. Last updated 2 years ago.

b-spline growth-curves linear-mixed-models longitudinal-data

3.5 match 9 stars 5.33 score 12 scripts

pecanproject

PEcAnAssimSequential:PEcAn Functions Used for Ecological Forecasts and Reanalysis

The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific workflow management tool that is designed to simplify the management of model parameterization, execution, and analysis. The goal of PECAn is to streamline the interaction between data and models, and to improve the efficacy of scientific investigation.

Maintained by Mike Dietze. Last updated 3 days ago.

bayesian cyberinfrastructure data-assimilation data-science ecosystem-model ecosystem-science forecasting meta-analysis national-science-foundation pecan plants jags cpp

2.3 match 216 stars 8.11 score 35 scripts

tjheaton

carbondate:Calibration and Summarisation of Radiocarbon Dates

Performs Bayesian non-parametric calibration of multiple related radiocarbon determinations, and summarises the calendar age information to plot their joint calendar age density (see Heaton (2022) <doi:10.1111/rssc.12599>). Also models the occurrence of radiocarbon samples as a variable-rate (inhomogeneous) Poisson process, plotting the posterior estimate for the occurrence rate of the samples over calendar time, and providing information about potential change points.

Maintained by Timothy J Heaton. Last updated 2 months ago.

cpp

3.1 match 5 stars 5.78 score 20 scripts

coatless-rpkg

rgen:Random Sampling Distribution C++ Routines for Armadillo

Provides popular sampling distributions C++ routines based in armadillo through a header file approach.

Maintained by James Joseph Balamuta. Last updated 1 years ago.

armadillo random-distributions rcpp rcpparmadillo

3.3 match 4 stars 5.38 score 1 scripts 4 dependents

jongheepark

NetworkChange:Bayesian Package for Network Changepoint Analysis

Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.

Maintained by Jong Hee Park. Last updated 3 years ago.

bayesian changepoint latent-space network

3.9 match 5 stars 4.60 score 16 scripts

cotima

CoTiMA:Continuous Time Meta-Analysis ('CoTiMA')

The 'CoTiMA' package performs meta-analyses of correlation matrices of repeatedly measured variables taken from studies that used different time intervals. Different time intervals between measurement occasions impose problems for meta-analyses because the effects (e.g. cross-lagged effects) cannot be simply aggregated, for example, by means of common fixed or random effects analysis. However, continuous time math, which is applied in 'CoTiMA', can be used to extrapolate or intrapolate the results from all studies to any desired time lag. By this, effects obtained in studies that used different time intervals can be meta-analyzed. 'CoTiMA' fits models to empirical data using the structural equation model (SEM) package 'ctsem', the effects specified in a SEM are related to parameters that are not directly included in the model (i.e., continuous time parameters; together, they represent the continuous time structural equation model, CTSEM). Statistical model comparisons and significance tests are then performed on the continuous time parameter estimates. 'CoTiMA' also allows analysis of publication bias (Egger's test, PET-PEESE estimates, zcurve analysis etc.) and analysis of statistical power (post hoc power, required sample sizes). See Dormann, C., Guthier, C., & Cortina, J. M. (2019) <doi:10.1177/1094428119847277>. and Guthier, C., Dormann, C., & Voelkle, M. C. (2020) <doi:10.1037/bul0000304>.

Maintained by Markus Homberg. Last updated 2 months ago.

3.3 match 4 stars 5.28 score

richardgeveritt

ggsmc:Visualising Output from Sequential Monte Carlo and Ensemble-Based Methods

Functions for plotting, and animating, the output of importance samplers, sequential Monte Carlo samplers (SMC) and ensemble-based methods. The package can be used to plot and animate histograms, densities, scatter plots and time series, and to plot the genealogy of an SMC or ensemble-based algorithm. These functions all rely on algorithm output to be supplied in tidy format. A function is provided to transform algorithm output from matrix format (one Monte Carlo point per row) to the tidy format required by the plotting and animating functions.

Maintained by Richard G Everitt. Last updated 2 months ago.

3.9 match 4.48 score 6 scripts

hughparsonage

hutils:Miscellaneous R Functions and Aliases

Provides utility functions for, and drawing on, the 'data.table' package. The package also collates useful miscellaneous functions extending base R not available elsewhere. The name is a portmanteau of 'utils' and the author.

Maintained by Hugh Parsonage. Last updated 2 years ago.

2.3 match 12 stars 7.76 score 219 scripts 8 dependents

tlverse

sl3:Pipelines for Machine Learning and Super Learning

A modern implementation of the Super Learner prediction algorithm, coupled with a general purpose framework for composing arbitrary pipelines for machine learning tasks.

Maintained by Jeremy Coyle. Last updated 4 months ago.

data-science ensemble-learning ensemble-model machine-learning model-selection regression stacking statistics

1.8 match 100 stars 9.94 score 748 scripts 7 dependents

mcol

hsstan:Hierarchical Shrinkage Stan Models for Biomarker Selection

Linear and logistic regression models penalized with hierarchical shrinkage priors for selection of biomarkers (or more general variable selection), which can be fitted using Stan (Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>). It implements the horseshoe and regularized horseshoe priors (Piironen and Vehtari (2017) <doi:10.1214/17-EJS1337SI>), as well as the projection predictive selection approach to recover a sparse set of predictive biomarkers (Piironen, Paasiniemi and Vehtari (2020) <doi:10.1214/20-EJS1711>).

Maintained by Marco Colombo. Last updated 1 years ago.

bayesian feature-selection mcmc cpp

4.5 match 7 stars 3.66 score 13 scripts

stephenslab

ebnm:Solve the Empirical Bayes Normal Means Problem

Provides simple, fast, and stable functions to fit the normal means model using empirical Bayes. For available models and details, see function ebnm(). A detailed introduction to the package is provided by Willwerscheid and Stephens (2023) <arXiv:2110.00152>.

Maintained by Peter Carbonetto. Last updated 9 months ago.

2.0 match 12 stars 8.22 score 145 scripts 1 dependents

lbelzile

mev:Modelling of Extreme Values

Various tools for the analysis of univariate, multivariate and functional extremes. Exact simulation from max-stable processes [Dombry, Engelke and Oesting (2016) <doi:10.1093/biomet/asw008>, R-Pareto processes for various parametric models, including Brown-Resnick (Wadsworth and Tawn, 2014, <doi:10.1093/biomet/ast042>) and Extremal Student (Thibaud and Opitz, 2015, <doi:10.1093/biomet/asv045>). Threshold selection methods, including Wadsworth (2016) <doi:10.1080/00401706.2014.998345>, and Northrop and Coleman (2014) <doi:10.1007/s10687-014-0183-z>. Multivariate extreme diagnostics. Estimation and likelihoods for univariate extremes, e.g., Coles (2001) <doi:10.1007/978-1-4471-3675-0>.

Maintained by Leo Belzile. Last updated 5 months ago.

extreme-value-statistics likelihood-functions max-stable simulation threshold-selection openblas cpp openmp

1.9 match 13 stars 8.23 score 94 scripts 4 dependents

bioc

scoup:Simulate Codons with Darwinian Selection Modelled as an OU Process

An elaborate molecular evolutionary framework that facilitates straightforward simulation of codon genetic sequences subjected to different degrees and/or patterns of Darwinian selection. The model is built upon the fitness landscape paradigm of Sewall Wright, as popularised by the mutation-selection model of Halpern and Bruno. This enables realistic evolutionary process of living organisms to be reproducible seamlessly. For example, an Ornstein-Uhlenbeck fitness update algorithm is incorporated herein. Consequently, otherwise complex biological processes, such as the effect of the interplay between genetic drift and fitness landscape fluctuations on the inference of diversifying selection, may now be investigated with minimal effort. Frequency-dependent and stochastic fitness landscape update techniques are available.

Maintained by Hassan Sadiq. Last updated 2 months ago.

alignment classification comparativegenomics dataimport genetics mathematicalbiology researchfield sequencing sequencematching software statisticalmethod workflowstep

3.3 match 4.60 score 8 scripts

lucapresicce

spBPS:Bayesian Predictive Stacking for Scalable Geospatial Transfer Learning

Provides functions for Bayesian Predictive Stacking within the Bayesian transfer learning framework for geospatial artificial systems, as introduced in "Bayesian Transfer Learning for Artificially Intelligent Geospatial Systems: A Predictive Stacking Approach" (Presicce and Banerjee, 2024) <doi:10.48550/arXiv.2410.09504>. This methodology enables efficient Bayesian geostatistical modeling, utilizing predictive stacking to improve inference across spatial datasets. The core functions leverage 'C++' for high-performance computation, making the framework well-suited for large-scale spatial data analysis in parallel and distributed computing environments. Designed for scalability, it allows seamless application in computationally demanding scenarios.

Maintained by Luca Presicce. Last updated 5 months ago.

openblas cpp openmp

3.4 match 4.40 score 10 scripts

lynettecaitlin

oHMMed:HMMs with Ordered Hidden States and Emission Densities

Inference using a class of Hidden Markov models (HMMs) called 'oHMMed'(ordered HMM with emission densities <doi:10.1186/s12859-024-05751-4>): The 'oHMMed' algorithms identify the number of comparably homogeneous regions within observed sequences with autocorrelation patterns. These are modelled as discrete hidden states; the observed data points are then realisations of continuous probability distributions with state-specific means that enable ordering of these distributions. The observed sequence is labelled according to the hidden states, permitting only neighbouring states that are also neighbours within the ordering of their associated distributions. The parameters that characterise these state-specific distributions are then inferred. Relevant for application to genomic sequences, time series, or any other sequence data with serial autocorrelation.

Maintained by Michal Majka. Last updated 1 months ago.

4.4 match 2 stars 3.30 score 4 scripts

cran

beyondWhittle:Bayesian Spectral Inference for Time Series

Implementations of Bayesian parametric, nonparametric and semiparametric procedures for univariate and multivariate time series. The package is based on the methods presented in C. Kirch et al (2018) <doi:10.1214/18-BA1126>, A. Meier (2018) <https://opendata.uni-halle.de//handle/1981185920/13470> and Y. Tang et al (2023) <doi:10.48550/arXiv.2303.11561>. It was supported by DFG grants KI 1443/3-1 and KI 1443/3-2.

Maintained by Renate Meyer. Last updated 4 months ago.

openblas cpp

8.6 match 2 stars 1.68 score 12 scripts

vabar

vibass:Valencia International Bayesian Summer School

Materials for the introductory course on Bayesian inference. Practicals, data and interactive apps.

Maintained by Facundo Muñoz. Last updated 8 months ago.

bayesian-inference teaching

2.7 match 7 stars 5.40 score 2 scripts

alun-thomas

rviewgraph:Animated Graph Layout Viewer

Provides 'Java' graphical user interfaces for viewing, manipulating and plotting graphs. Graphs may be directed or undirected.

Maintained by Alun Thomas. Last updated 2 years ago.

openjdk

6.3 match 2.30 score 3 scripts

mrc-ide

mcstate:Monte Carlo Methods for State Space Models

Implements Monte Carlo methods for state-space models such as 'SIR' models in epidemiology. Particle MCMC (pmcmc) and SMC2 methods are planned. This package is particularly designed to work with odin/dust models, but we will see how general it becomes.

Maintained by Rich FitzJohn. Last updated 9 months ago.

2.0 match 19 stars 7.08 score 87 scripts

globalecologylab

poems:Pattern-Oriented Ensemble Modeling System

A framework of interoperable R6 classes (Chang, 2020, <https://CRAN.R-project.org/package=R6>) for building ensembles of viable models via the pattern-oriented modeling (POM) approach (Grimm et al.,2005, <doi:10.1126/science.1116681>). The package includes classes for encapsulating and generating model parameters, and managing the POM workflow. The workflow includes: model setup; generating model parameters via Latin hyper-cube sampling (Iman & Conover, 1980, <doi:10.1080/03610928008827996>); running multiple sampled model simulations; collating summary results; and validating and selecting an ensemble of models that best match known patterns. By default, model validation and selection utilizes an approximate Bayesian computation (ABC) approach (Beaumont et al., 2002, <doi:10.1093/genetics/162.4.2025>), although alternative user-defined functionality could be employed. The package includes a spatially explicit demographic population model simulation engine, which incorporates default functionality for density dependence, correlated environmental stochasticity, stage-based transitions, and distance-based dispersal. The user may customize the simulator by defining functionality for translocations, harvesting, mortality, and other processes, as well as defining the sequence order for the simulator processes. The framework could also be adapted for use with other model simulators by utilizing its extendable (inheritable) base classes.

Maintained by July Pilowsky. Last updated 21 days ago.

biogeography population-model process-based

1.8 match 10 stars 8.05 score 59 scripts 2 dependents

eami91

BGPhazard:Markov Beta and Gamma Processes for Modeling Hazard Rates

Computes the hazard rate estimate as described by Nieto-Barajas & Walker (2002), Nieto-Barajas (2003), Nieto-Barajas & Walker (2007) and Nieto-Barajas & Yin (2008).

Maintained by Emilio Akira Morones Ishikawa. Last updated 2 years ago.

3.3 match 1 stars 4.32 score 21 scripts

cran

BayesFBHborrow:Bayesian Dynamic Borrowing with Flexible Baseline Hazard Function

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>.

Maintained by Darren Scott. Last updated 6 months ago.

10.7 match 1.30 score

nickreich

coarseDataTools:Analysis of Coarsely Observed Data

Functions to analyze coarse data. Specifically, it contains functions to (1) fit parametric accelerated failure time models to interval-censored survival time data, and (2) estimate the case-fatality ratio in scenarios with under-reporting. This package's development was motivated by applications to infectious disease: in particular, problems with estimating the incubation period and the case fatality ratio of a given disease. Sample data files are included in the package. See Reich et al. (2009) <doi:10.1002/sim.3659>, Reich et al. (2012) <doi:10.1111/j.1541-0420.2011.01709.x>, and Lessler et al. (2009) <doi:10.1016/S1473-3099(09)70069-6>.

Maintained by Nicholas G. Reich. Last updated 2 years ago.

1.7 match 9 stars 8.07 score 37 scripts 8 dependents

lbelzile

BMAmevt:Multivariate Extremes: Bayesian Estimation of the Spectral Measure

Toolkit for Bayesian estimation of the dependence structure in multivariate extreme value parametric models, following Sabourin and Naveau (2014) <doi:10.1016/j.csda.2013.04.021> and Sabourin, Naveau and Fougeres (2013) <doi:10.1007/s10687-012-0163-0>.

Maintained by Leo Belzile. Last updated 2 years ago.

3.5 match 3.90 score 16 scripts

hyu-ub

BayesNetBP:Bayesian Network Belief Propagation

Belief propagation methods in Bayesian Networks to propagate evidence through the network. The implementation of these methods are based on the article: Cowell, RG (2005). Local Propagation in Conditional Gaussian Bayesian Networks <https://www.jmlr.org/papers/v6/cowell05a.html>. For details please see Yu et. al. (2020) BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks <doi:10.18637/jss.v094.i03>. The optional 'cyjShiny' package for running the Shiny app is available at <https://github.com/cytoscape/cyjShiny>. Please see the example in the documentation of 'runBayesNetApp' function for installing 'cyjShiny' package from GitHub.

Maintained by Han Yu. Last updated 2 years ago.

bayesian-networks conditional-gaussian network-inference probabilistic-graphical-models

3.3 match 19 stars 3.98 score 3 scripts

gertvv

gemtc:Network Meta-Analysis Using Bayesian Methods

Network meta-analyses (mixed treatment comparisons) in the Bayesian framework using JAGS. Includes methods to assess heterogeneity and inconsistency, and a number of standard visualizations.

Maintained by Gert van Valkenhoef. Last updated 5 years ago.

jags cpp

1.8 match 44 stars 7.48 score 71 scripts 1 dependents

mlysy

msde:Bayesian Inference for Multivariate Stochastic Differential Equations

Implements an MCMC sampler for the posterior distribution of arbitrary time-homogeneous multivariate stochastic differential equation (SDE) models with possibly latent components. The package provides a simple entry point to integrate user-defined models directly with the sampler's C++ code, and parallelizes large portions of the calculations when compiled with 'OpenMP'.

Maintained by Martin Lysy. Last updated 3 years ago.

cpp

2.7 match 4.91 score 27 scripts

pmair78

eRm:Extended Rasch Modeling

Fits Rasch models (RM), linear logistic test models (LLTM), rating scale model (RSM), linear rating scale models (LRSM), partial credit models (PCM), and linear partial credit models (LPCM). Missing values are allowed in the data matrix. Additional features are the ML estimation of the person parameters, Andersen's LR-test, item-specific Wald test, Martin-Loef-Test, nonparametric Monte-Carlo Tests, itemfit and personfit statistics including infit and outfit measures, ICC and other plots, automated stepwise item elimination, simulation module for various binary data matrices.

Maintained by Patrick Mair. Last updated 1 years ago.

fortran

2.0 match 4 stars 6.42 score 182 scripts 5 dependents

muriteams

ergmito:Exponential Random Graph Models for Small Networks

Simulation and estimation of Exponential Random Graph Models (ERGMs) for small networks using exact statistics as shown in Vega Yon et al. (2020) <DOI:10.1016/j.socnet.2020.07.005>. As a difference from the 'ergm' package, 'ergmito' circumvents using Markov-Chain Maximum Likelihood Estimator (MC-MLE) and instead uses Maximum Likelihood Estimator (MLE) to fit ERGMs for small networks. As exhaustive enumeration is computationally feasible for small networks, this R package takes advantage of this and provides tools for calculating likelihood functions, and other relevant functions, directly, meaning that in many cases both estimation and simulation of ERGMs for small networks can be faster and more accurate than simulation-based algorithms.

Maintained by George Vega Yon. Last updated 2 years ago.

ergm exponential-random-graph-models statistics openblas cpp openmp

2.3 match 9 stars 5.49 score 34 scripts

bioc

plasmut:Stratifying mutations observed in cell-free DNA and white blood cells as germline, hematopoietic, or somatic

A Bayesian method for quantifying the liklihood that a given plasma mutation arises from clonal hematopoesis or the underlying tumor. It requires sequencing data of the mutation in plasma and white blood cells with the number of distinct and mutant reads in both tissues. We implement a Monte Carlo importance sampling method to assess the likelihood that a mutation arises from the tumor relative to non-tumor origin.

Maintained by Adith Arun. Last updated 5 months ago.

bayesian somaticmutation germlinemutation sequencing

3.0 match 4.00 score 2 scripts

duckmayr

bggum:Bayesian Estimation of Generalized Graded Unfolding Model Parameters

Provides a Metropolis-coupled Markov chain Monte Carlo sampler, post-processing and parameter estimation functions, and plotting utilities for the generalized graded unfolding model of Roberts, Donoghue, and Laughlin (2000) <doi:10.1177/01466216000241001>.

Maintained by JBrandon Duck-Mayr. Last updated 5 years ago.

cpp

2.5 match 4 stars 4.78 score 6 scripts

ropensci

stantargets:Targets for Stan Workflows

Bayesian data analysis usually incurs long runtimes and cumbersome custom code. A pipeline toolkit tailored to Bayesian statisticians, the 'stantargets' R package leverages 'targets' and 'cmdstanr' to ease these burdens. 'stantargets' makes it super easy to set up scalable Stan pipelines that automatically parallelize the computation and skip expensive steps when the results are already up to date. Minimal custom code is required, and there is no need to manually configure branching, so usage is much easier than 'targets' alone. 'stantargets' can access all of 'cmdstanr''s major algorithms (MCMC, variational Bayes, and optimization) and it supports both single-fit workflows and multi-rep simulation studies. For the statistical methodology, please refer to 'Stan' documentation (Stan Development Team 2020) <https://mc-stan.org/>.

Maintained by William Michael Landau. Last updated 1 months ago.

bayesian high-performance-computing make r-targetopia reproducibility stan statistics targets

1.8 match 49 stars 6.85 score 180 scripts

epiforecasts

epinowcast:Flexible Hierarchical Nowcasting

Tools to enable flexible and efficient hierarchical nowcasting of right-truncated epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes. Nowcasting, in this context, is gaining situational awareness using currently available observations and the reporting patterns of historical observations. This can be useful when tracking the spread of infectious disease in real-time: without nowcasting, changes in trends can be obfuscated by partial reporting or their detection may be delayed due to the use of simpler methods like truncation. While the package has been designed with epidemiological applications in mind, it could be applied to any set of right-truncated time-series count data.

Maintained by Sam Abbott. Last updated 11 months ago.

cmdstanr effective-reproduction-number-estimation epidemiology infectious-disease-surveillance nowcasting outbreak-analysis pandemic-preparedness real-time-infectious-disease-modelling stan

1.5 match 61 stars 7.88 score 65 scripts

dcgerard

tensr:Covariance Inference and Decompositions for Tensor Datasets

A collection of functions for Kronecker structured covariance estimation and testing under the array normal model. For estimation, maximum likelihood and Bayesian equivariant estimation procedures are implemented. For testing, a likelihood ratio testing procedure is available. This package also contains additional functions for manipulating and decomposing tensor data sets. This work was partially supported by NSF grant DMS-1505136. Details of the methods are described in Gerard and Hoff (2015) <doi:10.1016/j.jmva.2015.01.020> and Gerard and Hoff (2016) <doi:10.1016/j.laa.2016.04.033>.

Maintained by David Gerard. Last updated 2 years ago.

1.8 match 5 stars 6.53 score 56 scripts 4 dependents

epinowcast

epinowcast:Flexible Hierarchical Nowcasting

Tools to enable flexible and efficient hierarchical nowcasting of right-truncated epidemiological time-series using a semi-mechanistic Bayesian model with support for a range of reporting and generative processes. Nowcasting, in this context, is gaining situational awareness using currently available observations and the reporting patterns of historical observations. This can be useful when tracking the spread of infectious disease in real-time: without nowcasting, changes in trends can be obfuscated by partial reporting or their detection may be delayed due to the use of simpler methods like truncation. While the package has been designed with epidemiological applications in mind, it could be applied to any set of right-truncated time-series count data.

Maintained by Sam Abbott. Last updated 11 months ago.

cmdstanr effective-reproduction-number-estimation epidemiology infectious-disease-surveillance nowcasting outbreak-analysis pandemic-preparedness real-time-infectious-disease-modelling stan

1.5 match 61 stars 7.79 score 71 scripts

unuran

Runuran:R Interface to the 'UNU.RAN' Random Variate Generators

Interface to the 'UNU.RAN' library for Universal Non-Uniform RANdom variate generators. Thus it allows to build non-uniform random number generators from quite arbitrary distributions. In particular, it provides an algorithm for fast numerical inversion for distribution with given density function. In addition, the package contains densities, distribution functions and quantiles from a couple of distributions.

Maintained by Josef Leydold. Last updated 6 months ago.

1.7 match 6.87 score 180 scripts 8 dependents

mango-empire

dapper:Data Augmentation for Private Posterior Estimation

A data augmentation based sampler for conducting privacy-aware Bayesian inference. The dapper_sample() function takes an existing sampler as input and automatically constructs a privacy-aware sampler. The process of constructing a sampler is simplified through the specification of four independent modules, allowing for easy comparison between different privacy mechanisms by only swapping out the relevant modules. Probability mass functions for the discrete Gaussian and discrete Laplacian are provided to facilitate analyses dealing with privatized count data. The output of dapper_sample() can be analyzed using many of the same tools from the 'rstan' ecosystem. For methodological details on the sampler see Ju et al. (2022) <doi:10.48550/arXiv.2206.00710>, and for details on the discrete Gaussian and discrete Laplacian distributions see Canonne et al. (2020) <doi:10.48550/arXiv.2004.00010>.

Maintained by Kevin Eng. Last updated 5 months ago.

3.0 match 2 stars 3.78 score 4 scripts

distancedevelopment

dsm:Density Surface Modelling of Distance Sampling Data

Density surface modelling of line transect data. A Generalized Additive Model-based approach is used to calculate spatially-explicit estimates of animal abundance from distance sampling (also presence/absence and strip transect) data. Several utility functions are provided for model checking, plotting and variance estimation.

Maintained by Laura Marshall. Last updated 2 years ago.

1.9 match 8 stars 6.09 score 146 scripts

schoonees

vdg:Variance Dispersion Graphs and Fraction of Design Space Plots

Facilities for constructing variance dispersion graphs, fraction- of-design-space plots and similar graphics for exploring the properties of experimental designs. The design region is explored via random sampling, which allows for more flexibility than traditional variance dispersion graphs. A formula interface is leveraged to provide access to complex model formulae. Graphics can be constructed simultaneously for multiple experimental designs and/or multiple model formulae. Instead of using pointwise optimization to find the minimum and maximum scaled prediction variance curves, which can be inaccurate and time consuming, this package uses quantile regression as an alternative.

Maintained by Pieter Schoonees. Last updated 11 months ago.

fortran

5.6 match 2.00 score 10 scripts

vivianalobo

lnmixsurv:Bayesian Mixture Log-Normal Survival Model

Bayesian Survival models via the mixture of Log-Normal distribution extends the well-known survival models and accommodates different behaviour over time and considers higher censored survival times. The proposal combines mixture distributions Fruhwirth-Schnatter(2006) <doi:10.1007/s11336-009-9121-4>, and data augmentation techniques Tanner and Wong (1987) <doi:10.1080/01621459.1987.10478458>.

Maintained by Victor Hugo Soares Ney. Last updated 12 days ago.

gsl openblas cpp

1.8 match 2 stars 6.16 score 18 scripts

rh8liuqy

MSIMST:Bayesian Monotonic Single-Index Regression Model with the Skew-T Likelihood

Incorporates a Bayesian monotonic single-index mixed-effect model with a multivariate skew-t likelihood, specifically designed to handle survey weights adjustments. Features include a simulation program and an associated Gibbs sampler for model estimation. The single-index function is constrained to be monotonic increasing, utilizing a customized Gaussian process prior for precise estimation. The model assumes random effects follow a canonical skew-t distribution, while residuals are represented by a multivariate Student-t distribution. Offers robust Bayesian adjustments to integrate survey weight information effectively.

Maintained by Qingyang Liu. Last updated 6 months ago.

openblas cpp

2.5 match 2 stars 4.30 score

tmsalab

hmcdm:Hidden Markov Cognitive Diagnosis Models for Learning

Fitting hidden Markov models of learning under the cognitive diagnosis framework. The estimation of the hidden Markov diagnostic classification model, the first order hidden Markov model, the reduced-reparameterized unified learning model, and the joint learning model for responses and response times.

Maintained by Sunbeom Kwon. Last updated 2 years ago.

cognitive-diagnostic-models psychometrics rcpp rcpparmadillo openblas cpp openmp

1.9 match 7 stars 5.70 score 12 scripts

ash0204

GHS:Graphical Horseshoe MCMC Sampler Using Data Augmented Block Gibbs Sampler

Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.

Maintained by Ashutosh Srivastava. Last updated 6 years ago.

7.2 match 1.48 score 2 scripts

tbrown122387

cPseudoMaRg:Constructs a Correlated Pseudo-Marginal Sampler

The primary function makeCPMSampler() generates a sampler function which performs the correlated pseudo-marginal method of Deligiannidis, Doucet and Pitt (2017) <arXiv:1511.04992>. If the 'rho=' argument of makeCPMSampler() is set to 0, then the generated sampler function performs the original pseudo-marginal method of Andrieu and Roberts (2009) <DOI:10.1214/07-AOS574>. The sampler function is constructed with the user's choice of prior, parameter proposal distribution, and the likelihood approximation scheme. Note that this algorithm is not automatically tuned--each one of these arguments must be carefully chosen.

Maintained by Taylor Brown. Last updated 4 years ago.

3.9 match 2.70 score 2 scripts

kurtis-s

overture:Tools for Writing MCMC

Simplifies MCMC setup by automatically looping through sampling functions and saving the results. Reduces the memory footprint of running MCMC and saves samples to disk as the chain runs. Allows samples from the chain to be analyzed while the MCMC is still running. Provides functions for commonly performed operations such as calculating Metropolis acceptance ratios and creating adaptive Metropolis samplers. References: Roberts and Rosenthal (2009) <doi:10.1198/jcgs.2009.06134>.

Maintained by Kurtis Shuler. Last updated 6 years ago.

3.3 match 3 stars 3.18 score 7 scripts

ohdsi

CohortGenerator:Cohort Generation for the OMOP Common Data Model

Generate cohorts and subsets using an Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) Database. Cohorts are defined using 'CIRCE' (<https://github.com/ohdsi/circe-be>) or SQL compatible with 'SqlRender' (<https://github.com/OHDSI/SqlRender>).

Maintained by Anthony Sena. Last updated 6 months ago.

hades openjdk

1.3 match 13 stars 7.91 score 165 scripts

cran

BayesGOF:Bayesian Modeling via Frequentist Goodness-of-Fit

A Bayesian data modeling scheme that performs four interconnected tasks: (i) characterizes the uncertainty of the elicited parametric prior; (ii) provides exploratory diagnostic for checking prior-data conflict; (iii) computes the final statistical prior density estimate; and (iv) executes macro- and micro-inference. Primary reference is Mukhopadhyay, S. and Fletcher, D. 2018 paper "Generalized Empirical Bayes via Frequentist Goodness of Fit" (<https://www.nature.com/articles/s41598-018-28130-5 >).

Maintained by Doug Fletcher. Last updated 6 years ago.

4.3 match 2.48 score 1 dependents

emilmip

LTFHPlus:Implementation of LT-FH++

Implementation of LT-FH++, an extension of the liability threshold family history (LT-FH) model. LT-FH++ uses a Gibbs sampler for sampling from the truncated multivariate normal distribution and allows for flexible family structures. LT-FH++ was first described in Pedersen, Emil M., et al. (2022) <https://pure.au.dk/ws/portalfiles/portal/353346245/> as an extension to LT-FH with more flexible family structures, and again as the age-dependent liability threshold (ADuLT) model Pedersen, Emil M., et al. (2023) <https://www.nature.com/articles/s41467-023-41210-z> as an alternative to traditional time-to-event genome-wide association studies, where family history was not considered.

Maintained by Emil Michael Pedersen. Last updated 9 months ago.

cpp

2.3 match 10 stars 4.66 score 23 scripts

ropensci

predictNMB:Evaluate Clinical Prediction Models by Net Monetary Benefit

Estimates when and where a model-guided treatment strategy may outperform a treat-all or treat-none approach by Monte Carlo simulation and evaluation of the Net Monetary Benefit. Details can be viewed in Parsons et al. (2023) <doi:10.21105/joss.05328>.

Maintained by Rex Parsons. Last updated 8 months ago.

1.7 match 10 stars 6.23 score 17 scripts

svdpas

BayesSurvival:Bayesian Survival Analysis for Right Censored Data

Performs unadjusted Bayesian survival analysis for right censored time-to-event data. The main function, BayesSurv(), computes the posterior mean and a credible band for the survival function and for the cumulative hazard, as well as the posterior mean for the hazard, starting from a piecewise exponential (histogram) prior with Gamma distributed heights that are either independent, or have a Markovian dependence structure. A function, PlotBayesSurv(), is provided to easily create plots of the posterior means of the hazard, cumulative hazard and survival function, with a credible band accompanying the latter two. The priors and samplers are described in more detail in Castillo and Van der Pas (2020) "Multiscale Bayesian survival analysis" <arXiv:2005.02889>. In that paper it is also shown that the credible bands for the survival function and the cumulative hazard can be considered confidence bands (under mild conditions) and thus offer reliable uncertainty quantification.

Maintained by Stephanie van der Pas. Last updated 4 years ago.

3.8 match 2.70 score 4 scripts

cubiczebra

MVNBayesian:Bayesian Analysis Framework for MVN (Mixture) Distribution

Tools of Bayesian analysis framework using the method suggested by Berger (1985) <doi:10.1007/978-1-4757-4286-2> for multivariate normal (MVN) distribution and multivariate normal mixture (MixMVN) distribution: a) calculating Bayesian posteriori of (Mix)MVN distribution; b) generating random vectors of (Mix)MVN distribution; c) Markov chain Monte Carlo (MCMC) for (Mix)MVN distribution.

Maintained by ZHANG Chen. Last updated 6 years ago.

3.7 match 2.81 score 13 scripts

matthieu-bruneaux

isotracer:Isotopic Tracer Analysis Using MCMC

Implements Bayesian models to analyze data from tracer addition experiments. The implemented method was originally described in the article "A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from Isotope Tracer Addition Experiments" by López-Sepulcre et al. (2020) <doi:10.1086/708546>.

Maintained by Matthieu Bruneaux. Last updated 4 months ago.

cpp

1.7 match 5.92 score 60 scripts

bsvars

bsvarSIGNs:Bayesian SVARs with Sign, Zero, and Narrative Restrictions

Implements state-of-the-art algorithms for the Bayesian analysis of Structural Vector Autoregressions (SVARs) identified by sign, zero, and narrative restrictions. The core model is based on a flexible Vector Autoregression with estimated hyper-parameters of the Minnesota prior and the dummy observation priors as in Giannone, Lenza, Primiceri (2015) <doi:10.1162/REST_a_00483>. The sign restrictions are implemented employing the methods proposed by Rubio-Ramírez, Waggoner & Zha (2010) <doi:10.1111/j.1467-937X.2009.00578.x>, while identification through sign and zero restrictions follows the approach developed by Arias, Rubio-Ramírez, & Waggoner (2018) <doi:10.3982/ECTA14468>. Furthermore, our tool provides algorithms for identification via sign and narrative restrictions, in line with the methods introduced by Antolín-Díaz and Rubio-Ramírez (2018) <doi:10.1257/aer.20161852>. Users can also estimate a model with sign, zero, and narrative restrictions imposed at once. The package facilitates predictive and structural analyses using impulse responses, forecast error variance and historical decompositions, forecasting and conditional forecasting, as well as analyses of structural shocks and fitted values. All this is complemented by colourful plots, user-friendly summary functions, and comprehensive documentation including the vignette by Wang & Woźniak (2024) <doi:10.48550/arXiv.2501.16711>. The 'bsvarSIGNs' package is aligned regarding objects, workflows, and code structure with the R package 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, and they constitute an integrated toolset. It was granted the Di Cook Open-Source Statistical Software Award by the Statistical Society of Australia in 2024.

Maintained by Xiaolei Wang. Last updated 2 months ago.

bayesian-inference econometrics vector-autoregression openblas cpp openmp

1.6 match 13 stars 6.21 score 10 scripts

cran

chkptstanr:Checkpoint MCMC Sampling with 'Stan'

Fit Bayesian models in Stan <doi: 10.18637/jss.v076.i01> with checkpointing, that is, the ability to stop the MCMC sampler at will, and then pick right back up where the MCMC sampler left off. Custom 'Stan' models can be fitted, or the popular package 'brms' <doi: 10.18637/jss.v080.i01> can be used to generate the 'Stan' code. This package is fully compatible with the R packages 'brms', 'posterior', 'cmdstanr', and 'bayesplot'.

Maintained by Donald Williams. Last updated 3 years ago.

2.7 match 2 stars 3.72 score 26 scripts

mqbssppe

BayesBinMix:Bayesian Estimation of Mixtures of Multivariate Bernoulli Distributions

Fully Bayesian inference for estimating the number of clusters and related parameters to heterogeneous binary data.

Maintained by Panagiotis Papastamoulis. Last updated 8 years ago.

7.7 match 1.28 score 19 scripts

tmsalab

pg:Polya Gamma Distribution Sampler

Provides access to a series of highly performant random distribution samplers for the Polya Gamma Distribution as described by Polson, Scott, and Windle (2013) <arXiv:1205.0310> using either 'C++' headers for 'Rcpp' or 'RcppArmadillo' and 'R'. The 'C++' header approach was developed to enable computations in Balamuta (2021) <https://www.ideals.illinois.edu/items/121209>.

Maintained by James Balamuta. Last updated 2 years ago.

cpp

3.6 match 1 stars 2.70 score 5 scripts

alxsrobert

o2geosocial:Reconstruction of Transmission Chains from Surveillance Data

Bayesian reconstruction of who infected whom during past outbreaks using routinely-collected surveillance data. Inference of transmission trees using genotype, age specific social contacts, distance between cases and onset dates of the reported cases. (Robert A, Kucharski AJ, Gastanaduy PA, Paul P, Funk S. (2020) <doi:10.1098/rsif.2020.0084>).

Maintained by Alexis Robert. Last updated 9 months ago.

baysian-inference markov-chain-monte-carlo transmission-chain-reconstruction cpp

2.0 match 8 stars 4.90 score 5 scripts

darrenjw

smfsb:Stochastic Modelling for Systems Biology

Code and data for modelling and simulation of stochastic kinetic biochemical network models. It contains the code and data associated with the second and third editions of the book Stochastic Modelling for Systems Biology, published by Chapman & Hall/CRC Press.

Maintained by Darren Wilkinson. Last updated 1 years ago.

3.3 match 2.94 score 88 scripts

cran

RobPer:Robust Periodogram and Periodicity Detection Methods

Calculates periodograms based on (robustly) fitting periodic functions to light curves (irregularly observed time series, possibly with measurement accuracies, occurring in astroparticle physics). Three main functions are included: RobPer() calculates the periodogram. Outlying periodogram bars (indicating a period) can be detected with betaCvMfit(). Artificial light curves can be generated using the function tsgen(). For more details see the corresponding article: Thieler, Fried and Rathjens (2016), Journal of Statistical Software 69(9), 1-36, <doi:10.18637/jss.v069.i09>.

Maintained by Jonathan Rathjens. Last updated 3 years ago.

3.3 match 3 stars 2.95 score 1 dependents

jingyuhe

bayeslm:Efficient Sampling for Gaussian Linear Regression with Arbitrary Priors

Efficient sampling for Gaussian linear regression with arbitrary priors, Hahn, He and Lopes (2018) <arXiv:1806.05738>.

Maintained by Jingyu He. Last updated 3 years ago.

openblas cpp

1.9 match 9 stars 5.03 score 24 scripts

martenthompson

agfh:Agnostic Fay-Herriot Model for Small Area Statistics

Implements the Agnostic Fay-Herriot model, an extension of the traditional small area model. In place of normal sampling errors, the sampling error distribution is estimated with a Gaussian process to accommodate a broader class of distributions. This flexibility is most useful in the presence of bounded, multi-modal, or heavily skewed sampling errors.

Maintained by Marten Thompson. Last updated 2 years ago.

3.5 match 2.70 score 2 scripts

hanwengutierrez

TAR:Bayesian Modeling of Autoregressive Threshold Time Series Models

Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.

Maintained by Hanwen Zhang. Last updated 8 years ago.

3.3 match 5 stars 2.74 score 11 scripts

franzmohr

bvartools:Bayesian Inference of Vector Autoregressive and Error Correction Models

Assists in the set-up of algorithms for Bayesian inference of vector autoregressive (VAR) and error correction (VEC) models. Functions for posterior simulation, forecasting, impulse response analysis and forecast error variance decomposition are largely based on the introductory texts of Chan, Koop, Poirier and Tobias (2019, ISBN: 9781108437493), Koop and Korobilis (2010) <doi:10.1561/0800000013> and Luetkepohl (2006, ISBN: 9783540262398).

Maintained by Franz X. Mohr. Last updated 1 years ago.

bayesian bayesian-inference bayesian-var bvar bvecm gibbs-sampling mcmc vector-autoregression vector-error-correction-model openblas cpp

1.3 match 31 stars 6.80 score 34 scripts 1 dependents

danielmork

dlmtree:Bayesian Treed Distributed Lag Models

Estimation of distributed lag models (DLMs) based on a Bayesian additive regression trees framework. Includes several extensions of DLMs: treed DLMs and distributed lag mixture models (Mork and Wilson, 2023) <doi:10.1111/biom.13568>; treed distributed lag nonlinear models (Mork and Wilson, 2022) <doi:10.1093/biostatistics/kxaa051>; heterogeneous DLMs (Mork, et. al., 2024) <doi:10.1080/01621459.2023.2258595>; monotone DLMs (Mork and Wilson, 2024) <doi:10.1214/23-BA1412>. The package also includes visualization tools and a 'shiny' interface to help interpret results.

Maintained by Daniel Mork. Last updated 1 months ago.

fortran cpp openmp

1.6 match 21 stars 5.40 score 17 scripts

agandy

systemicrisk:Systemic Risk and Network Reconstruction

Analysis of risk through liability matrices. Contains a Gibbs sampler for network reconstruction, where only row and column sums of the liabilities matrix as well as some other fixed entries are observed, following the methodology of Gandy&Veraart (2016) <doi:10.1287/mnsc.2016.2546>. It also incorporates models that use a power law distribution on the degree distribution.

Maintained by Axel Gandy. Last updated 11 months ago.

cpp

2.3 match 5 stars 3.88 score 51 scripts

msadinle

BRL:Beta Record Linkage

Implementation of the record linkage methodology proposed by Sadinle (2017) <doi:10.1080/01621459.2016.1148612>. It handles the bipartite record linkage problem, where two duplicate-free datafiles are to be merged.

Maintained by Mauricio Sadinle. Last updated 5 years ago.

1.8 match 6 stars 4.96 score 17 scripts 1 dependents

sooahnshin

aihuman:Experimental Evaluation of Algorithm-Assisted Human Decision-Making

Provides statistical methods for analyzing experimental evaluation of the causal impacts of algorithmic recommendations on human decisions developed by Imai, Jiang, Greiner, Halen, and Shin (2023) <doi:10.1093/jrsssa/qnad010> and Ben-Michael, Greiner, Huang, Imai, Jiang, and Shin (2024) <doi:10.48550/arXiv.2403.12108>. The data used for this paper, and made available here, are interim, based on only half of the observations in the study and (for those observations) only half of the study follow-up period. We use them only to illustrate methods, not to draw substantive conclusions.

Maintained by Sooahn Shin. Last updated 3 months ago.

openblas cpp openmp

1.9 match 2 stars 4.60 score 8 scripts

keefe-murphy

IMIFA:Infinite Mixtures of Infinite Factor Analysers and Related Models

Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.

Maintained by Keefe Murphy. Last updated 1 years ago.

bayesian-nonparametrics dimension-reduction factor-analysis gaussian-mixture-model model-based-clustering

1.6 match 7 stars 5.25 score 51 scripts

cran

popReconstruct:Reconstruct Human Populations of the Recent Past

Implements the Bayesian hierarchical model described by Wheldon, Raftery, Clark and Gerland (see: <doi:10.1080/01621459.2012.737729>) for simultaneously estimating age-specific population counts, fertility rates, mortality rates and net international migration flows, at the national level.

Maintained by "Mark C. Wheldon". Last updated 5 years ago.

4.1 match 2.00 score

permaverse

nevada:Network-Valued Data Analysis

A flexible statistical framework for network-valued data analysis. It leverages the complexity of the space of distributions on graphs by using the permutation framework for inference as implemented in the 'flipr' package. Currently, only the two-sample testing problem is covered and generalization to k samples and regression will be added in the future as well. It is a 4-step procedure where the user chooses a suitable representation of the networks, a suitable metric to embed the representation into a metric space, one or more test statistics to target specific aspects of the distributions to be compared and a formula to compute the permutation p-value. Two types of inference are provided: a global test answering whether there is a difference between the distributions that generated the two samples and a local test for localizing differences on the network structure. The latter is assumed to be shared by all networks of both samples. References: Lovato, I., Pini, A., Stamm, A., Vantini, S. (2020) "Model-free two-sample test for network-valued data" <doi:10.1016/j.csda.2019.106896>; Lovato, I., Pini, A., Stamm, A., Taquet, M., Vantini, S. (2021) "Multiscale null hypothesis testing for network-valued data: Analysis of brain networks of patients with autism" <doi:10.1111/rssc.12463>.

Maintained by Aymeric Stamm. Last updated 3 months ago.

openblas cpp

1.8 match 6 stars 4.48 score 7 scripts

sistm

NPflow:Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry Data

Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions modeling oriented towards flow-cytometry data preprocessing applications. Method is detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.

Maintained by Boris P Hejblum. Last updated 1 years ago.

openblas cpp openmp

1.8 match 4 stars 4.45 score 47 scripts 1 dependents

franciscorichter

rgm:Advanced Inference with Random Graphical Models

Implements state-of-the-art Random Graphical Models (RGMs) for multivariate data analysis across multiple environments, offering tools for exploring network interactions and structural relationships. Capabilities include joint inference across environments, integration of external covariates, and a Bayesian framework for uncertainty quantification. Applicable in various fields, including microbiome analysis. Methods based on Vinciotti, V., Wit, E., & Richter, F. (2023). "Random Graphical Model of Microbiome Interactions in Related Environments." <arXiv:2304.01956>.

Maintained by Francisco Richter. Last updated 1 years ago.

cpp

2.0 match 4.00 score 5 scripts

bioc

BAGS:A Bayesian Approach for Geneset Selection

R package providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest.

Maintained by Alejandro Quiroz-Zarate. Last updated 5 months ago.

bayesian

1.7 match 4.38 score 40 scripts

cran

SMPracticals:Practicals for Use with Davison (2003) Statistical Models

Contains the datasets and a few functions for use with the practicals outlined in Appendix A of the book Statistical Models (Davison, 2003, Cambridge University Press), which can be found at <doi:10.1017/CBO9780511815850>.

Maintained by Alessandra R. Brazzale. Last updated 1 years ago.

5.1 match 1.48 score 1 dependents

giorgilancs

PrevMap:Geostatistical Modelling of Spatially Referenced Prevalence Data

Provides functions for both likelihood-based and Bayesian analysis of spatially referenced prevalence data. For a tutorial on the use of the R package, see Giorgi and Diggle (2017) <doi:10.18637/jss.v078.i08>.

Maintained by Emanuele Giorgi. Last updated 2 years ago.

1.7 match 4.36 score 46 scripts

laplacesdemonr

LaplacesDemon:Complete Environment for Bayesian Inference

Provides a complete environment for Bayesian inference using a variety of different samplers (see ?LaplacesDemon for an overview).

Maintained by Henrik Singmann. Last updated 12 months ago.

0.5 match 93 stars 13.45 score 1.8k scripts 60 dependents

cran

UPG:Efficient Bayesian Algorithms for Binary and Categorical Data Regression Models

Efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and marginal data augmentation algorithms described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association <doi:10.1080/01621459.2023.2259030>".

Maintained by Gregor Zens. Last updated 4 months ago.

2.2 match 3.31 score 41 scripts

martin-wiegand

Rosenbrock:Extended Rosenbrock-Type Densities for Markov Chain Monte Carlo (MCMC) Sampler Benchmarking

New Markov chain Monte Carlo (MCMC) samplers new to be thoroughly tested and their performance accurately assessed. This requires densities that offer challenging properties to the novel sampling algorithms. One such popular problem is the Rosenbrock function. However, while its shape lends itself well to a benchmark problem, no codified multivariate expansion of the density exists. We have developed an extension to this class of distributions and supplied densities and direct sampler functions to assess the performance of novel MCMC algorithms. The functions are introduced in "An n-dimensional Rosenbrock Distribution for MCMC Testing" by Pagani, Wiegand and Nadarajah (2019) <arXiv:1903.09556>.

Maintained by Martin Wiegand. Last updated 5 years ago.

7.1 match 1.00 score 1 scripts

drjp

nimbleAPT:Adaptive Parallel Tempering for 'NIMBLE'

Functions for adaptive parallel tempering (APT) with NIMBLE models. Adapted from 'Lacki' & 'Miasojedow' (2016) <DOI:10.1007/s11222-015-9579-0> and 'Miasojedow, Moulines and Vihola' (2013) <DOI:10.1080/10618600.2013.778779>.

Maintained by David Pleydell. Last updated 2 months ago.

mcmc-methods mcmc-sampling

1.7 match 1 stars 4.18 score 6 scripts

berchuck

womblR:Spatiotemporal Boundary Detection Model for Areal Unit Data

Implements a spatiotemporal boundary detection model with a dissimilarity metric for areal data with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and spatial correlation is introduced at each time point through a conditional autoregressive (CAR) prior. Temporal correlation is introduced through a hierarchical structure and can be specified as exponential or first-order autoregressive. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in "Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method", by Berchuck et al (2018), <arXiv:1805.11636>. The paper is in press at the Journal of the American Statistical Association.

Maintained by Samuel I. Berchuck. Last updated 3 years ago.

openblas cpp

1.7 match 1 stars 4.10 score 25 scripts

joshcullen

bayesmove:Non-Parametric Bayesian Analyses of Animal Movement

Methods for assessing animal movement from telemetry and biologging data using non-parametric Bayesian methods. This includes features for pre- processing and analysis of data, as well as the visualization of results from the models. This framework does not rely on standard parametric density functions, which provides flexibility during model fitting. Further details regarding part of this framework can be found in Cullen et al. (2022) <doi:10.1111/2041-210X.13745>.

Maintained by Joshua Cullen. Last updated 1 years ago.

cpp

1.7 match 9 stars 4.18 score 34 scripts

kenkellner

IPMbook:Functions and Data for the Book 'Integrated Population Models'

Provides functions and data sets to accompany the book 'Integrated Population Models: Theory and Ecological Applications with R and JAGS' by Michael Schaub and Marc Kéry (ISBN: 9780128205648).

Maintained by Ken Kellner. Last updated 1 months ago.

1.8 match 1 stars 3.95 score 177 scripts

aleshing

multilink:Multifile Record Linkage and Duplicate Detection

Implementation of the methodology of Aleshin-Guendel & Sadinle (2022) <doi:10.1080/01621459.2021.2013242>. It handles the general problem of multifile record linkage and duplicate detection, where any number of files are to be linked, and any of the files may have duplicates.

Maintained by Serge Aleshin-Guendel. Last updated 2 years ago.

openblas cpp openmp

1.9 match 9 stars 3.65 score 4 scripts

r-forge

TopKLists:Inference, Aggregation and Visualization for Top-K Ranked Lists

For multiple ranked input lists (full or partial) representing the same set of N objects, the package TopKLists offers (1) statistical inference on the lengths of informative top-k lists, (2) stochastic aggregation of full or partial lists, and (3) graphical tools for the statistical exploration of input lists, and for the visualization of aggregation results.

Maintained by Michael G. Schimek. Last updated 9 years ago.

1.7 match 4.05 score 37 scripts 1 dependents

blakemoya

copre:Tools for Nonparametric Martingale Posterior Sampling

Performs Bayesian nonparametric density estimation using Martingale posterior distributions including the Copula Resampling (CopRe) algorithm. Also included are a Gibbs sampler for the marginal Gibbs-type mixture model and an extension to include full uncertainty quantification via a predictive sequence resampling (SeqRe) algorithm. The CopRe and SeqRe samplers generate random nonparametric distributions as output, leading to complete nonparametric inference on posterior summaries. Routines for calculating arbitrary functionals from the sampled distributions are included as well as an important algorithm for finding the number and location of modes, which can then be used to estimate the clusters in the data using, for example, k-means. Implements work developed in Moya B., Walker S. G. (2022). <doi:10.48550/arxiv.2206.08418>, Fong, E., Holmes, C., Walker, S. G. (2021) <doi:10.48550/arxiv.2103.15671>, and Escobar M. D., West, M. (1995) <doi:10.1080/01621459.1995.10476550>.

Maintained by Blake Moya. Last updated 10 months ago.

bayesian dirichlet-process nonparametric cpp openmp

2.5 match 1 stars 2.70 score 2 scripts

rodrigorsdc

mrfse:Markov Random Field Structure Estimator

Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. These algorithm are described in Frondana (2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.The third one is a greedy algorithm, described in Bresler (2015) <doi:10.1145/2746539.2746631).

Maintained by Rodrigo Carvalho. Last updated 5 months ago.

cpp openmp

3.8 match 1.78 score 12 scripts

bioc

BUScorrect:Batch Effects Correction with Unknown Subtypes

High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity.

Maintained by Xiangyu Luo. Last updated 5 months ago.

geneexpression statisticalmethod bayesian clustering featureextraction batcheffect

1.7 match 4.00 score 2 scripts

egarpor

polykde:Polyspherical Kernel Density Estimation

Kernel density estimation on the polysphere, hypersphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests. Companion package to García-Portugués and Meilán-Vila (2024) <doi:10.48550/arXiv.2411.04166> and García-Portugués and Meilán-Vila (2023) <doi:10.1007/978-3-031-32729-2_4>.

Maintained by Eduardo García-Portugués. Last updated 1 months ago.

circular-statistics directional-statistics kernel-smoothing openblas cpp

2.2 match 3.00 score 5 scripts

bioc

HiLDA:Conducting statistical inference on comparing the mutational exposures of mutational signatures by using hierarchical latent Dirichlet allocation

A package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation. It statistically tests whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. The package also provides inference and visualization.

Maintained by Zhi Yang. Last updated 5 months ago.

software somaticmutation sequencing statisticalmethod bayesian mutational-signatures rjags somatic-mutations cpp jags

1.2 match 3 stars 5.56 score 7 scripts 1 dependents

cran

valection:Sampler for Verification Studies

A binding for the 'valection' program which offers various ways to sample the outputs of competing algorithms or parameterizations, and fairly assess their performance against each other. The 'valection' C library is required to use this package and can be downloaded from: <http://labs.oicr.on.ca/boutros-lab/software/valection>. Cooper CI, et al; Valection: Design Optimization for Validation and Verification Studies; Biorxiv 2018; <doi:10.1101/254839>.

Maintained by Paul C. Boutros. Last updated 7 years ago.

3.3 match 2.00 score

cran

BayesCPclust:A Bayesian Approach for Clustering Constant-Wise Change-Point Data

A Gibbs sampler algorithm was developed to estimate change points in constant-wise data sequences while performing clustering simultaneously. The algorithm is described in da Cruz, A. C. and de Souza, C. P. E "A Bayesian Approach for Clustering Constant-wise Change-point Data" <doi:10.48550/arXiv.2305.17631>.

Maintained by Ana Carolina da Cruz. Last updated 2 months ago.

3.9 match 1.70 score

barnhilldave

TML:Tropical Geometry Tools for Machine Learning

Suite of tropical geometric tools for use in machine learning applications. These methods may be summarized in the following references: Yoshida, et al. (2022) <arxiv:2209.15045>, Barnhill et al. (2023) <arxiv:2303.02539>, Barnhill and Yoshida (2023) <doi:10.3390/math11153433>, Aliatimis et al. (2023) <arXiv:2306.08796>, Yoshida et al. (2022) <arXiv:2206.04206>, and Yoshida et al. (2019) <doi:10.1007/s11538-018-0493-4>.

Maintained by David Barnhill. Last updated 8 months ago.

1.8 match 3 stars 3.65 score 1 scripts

berchuck

spCP:Spatially Varying Change Points

Implements a spatially varying change point model with unique intercepts, slopes, variance intercepts and slopes, and change points at each location. Inference is within the Bayesian setting using Markov chain Monte Carlo (MCMC). The response variable can be modeled as Gaussian (no nugget), probit or Tobit link and the five spatially varying parameter are modeled jointly using a multivariate conditional autoregressive (MCAR) prior. The MCAR is a unique process that allows for a dissimilarity metric to dictate the local spatial dependencies. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in the corresponding paper on arXiv by Berchuck et al (2018): "A spatially varying change points model for monitoring glaucoma progression using visual field data", <arXiv:1811.11038>.

Maintained by Samuel I. Berchuck. Last updated 3 years ago.

openblas cpp

1.7 match 3.70 score 4 scripts

mauroflorez

MultRegCMP:Bayesian Multivariate Conway-Maxwell-Poisson Regression Model for Correlated Count Data

Fits a Bayesian Regression Model for multivariate count data. This model assumes that the data is distributed according to the Conway-Maxwell-Poisson distribution, and for each response variable it is associate different covariates. This model allows to account for correlations between the counts by using latent effects based on the Chib and Winkelmann (2001) <http://www.jstor.org/stable/1392277> proposal.

Maintained by Mauro Florez. Last updated 9 months ago.

1.9 match 3.30 score 4 scripts

kwb-r

kwb.kuras:Interface to KURAS database

Interface to KURAS database.

Maintained by Hauke Sonnenberg. Last updated 3 years ago.

data-import project-kuras

3.6 match 1.70 score

pmair78

RaschSampler:Rasch Sampler

MCMC based sampling of binary matrices with fixed margins as used in exact Rasch model tests.

Maintained by Patrick Mair. Last updated 1 years ago.

fortran

5.8 match 1.04 score 11 scripts

jmhewitt

telefit:Estimation and Prediction for Remote Effects Spatial Process Models

Implementation of the remote effects spatial process (RESP) model for teleconnection. The RESP model is a geostatistical model that allows a spatially-referenced variable (like average precipitation) to be influenced by covariates defined on a remote domain (like sea surface temperatures). The RESP model is introduced in Hewitt et al. (2018) <doi:10.1002/env.2523>. Sample code for working with the RESP model is available at <https://jmhewitt.github.io/research/resp_example>. This material is based upon work supported by the National Science Foundation under grant number AGS 1419558. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Maintained by Joshua Hewitt. Last updated 5 years ago.

openblas cpp

1.9 match 1 stars 3.19 score 31 scripts

jimclarkatduke

gjam:Generalized Joint Attribute Modeling

Analyzes joint attribute data (e.g., species abundance) that are combinations of continuous and discrete data with Gibbs sampling. Full model and computation details are described in Clark et al. (2018) <doi:10.1002/ecm.1241>.

Maintained by James S. Clark. Last updated 3 years ago.

openblas cpp openmp

1.9 match 3.18 score 150 scripts

holans

stcos:Space-Time Change of Support

Spatio-temporal change of support (STCOS) methods are designed for statistical inference on geographic and time domains which differ from those on which the data were observed. In particular, a parsimonious class of STCOS models supporting Gaussian outcomes was introduced by Bradley, Wikle, and Holan <doi:10.1002/sta4.94>. The 'stcos' package contains tools which facilitate use of STCOS models.

Maintained by Andrew M. Raim. Last updated 2 years ago.

openblas cpp

1.9 match 3 stars 3.18 score 4 scripts

cran

ctmcd:Estimating the Parameters of a Continuous-Time Markov Chain from Discrete-Time Data

Estimation of Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal and weighted adjustment of matrix logarithm based candidate solutions as in Israel (2001) <doi:10.1111/1467-9965.00114> as well as a quasi-optimization approach. Moreover, the expectation-maximization algorithm and the Gibbs sampling approach of Bladt and Sorensen (2005) <doi:10.1111/j.1467-9868.2005.00508.x> are included.

Maintained by Marius Pfeuffer. Last updated 1 years ago.

openblas cpp openmp

2.3 match 2 stars 2.60 score

lbelzile

lcopula:Liouville Copulas

Collections of functions allowing random number generations and estimation of 'Liouville' copulas, as described in Belzile and Neslehova (2017) <doi:10.1016/j.jmva.2017.05.008>.

Maintained by Leo Belzile. Last updated 1 years ago.

copula extremes cpp

2.0 match 1 stars 2.85 score 14 scripts

cran

BMS:Bayesian Model Averaging Library

Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Built-in priors include coefficient priors (fixed, hyper-g and empirical priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Post-processing functions allow for inferring posterior inclusion and model probabilities, various moments, coefficient and predictive densities. Plotting functions available for posterior model size, MCMC convergence, predictive and coefficient densities, best models representation, BMA comparison. Also includes Bayesian normal-conjugate linear model with Zellner's g prior, and assorted methods.

Maintained by Stefan Zeugner. Last updated 3 years ago.

1.9 match 1 stars 3.00 score

ayushmclaren

qbld:Quantile Regression for Binary Longitudinal Data

Implements the Bayesian quantile regression model for binary longitudinal data (QBLD) developed in Rahman and Vossmeyer (2019) <DOI:10.1108/S0731-90532019000040B009>. The model handles both fixed and random effects and implements both a blocked and an unblocked Gibbs sampler for posterior inference.

Maintained by Ayush Agarwal. Last updated 3 years ago.

openblas cpp

2.8 match 2.00 score 4 scripts

jcai-1122

DGP4LCF:Dependent Gaussian Processes for Longitudinal Correlated Factors

Model high-dimensional gene expression trajectories using dynamic factor analysis with dependent Gaussian processes

Maintained by Jiachen Cai. Last updated 10 months ago.

openblas cpp

1.7 match 3.30 score 3 scripts

newmi1988

seeds:Estimate Hidden Inputs using the Dynamic Elastic Net

Algorithms to calculate the hidden inputs of systems of differential equations. These hidden inputs can be interpreted as a control that tries to minimize the discrepancies between a given model and taken measurements. The idea is also called the Dynamic Elastic Net, as proposed in the paper "Learning (from) the errors of a systems biology model" (Engelhardt, Froelich, Kschischo 2016) <doi:10.1038/srep20772>. To use the experimental SBML import function, the 'rsbml' package is required. For installation I refer to the official 'rsbml' page: <https://bioconductor.org/packages/release/bioc/html/rsbml.html>.

Maintained by Tobias Newmiwaka. Last updated 4 years ago.

1.8 match 3.00 score 2 scripts

fndemarqui

peppm:Piecewise Exponential Distribution with Random Time Grids

Fits the Piecewise Exponential distribution with random time grids using the clustering structure of the Product Partition Models. Details of the implemented model can be found in Demarqui et al. (2008) <doi:10.1007/s10985-008-9086-0>.

Maintained by Fabio Demarqui. Last updated 5 years ago.

cpp

2.0 match 2.70 score 5 scripts

usepa

httk:High-Throughput Toxicokinetics

Pre-made models that can be rapidly tailored to various chemicals and species using chemical-specific in vitro data and physiological information. These tools allow incorporation of chemical toxicokinetics ("TK") and in vitro-in vivo extrapolation ("IVIVE") into bioinformatics, as described by Pearce et al. (2017) (<doi:10.18637/jss.v079.i04>). Chemical-specific in vitro data characterizing toxicokinetics have been obtained from relatively high-throughput experiments. The chemical-independent ("generic") physiologically-based ("PBTK") and empirical (for example, one compartment) "TK" models included here can be parameterized with in vitro data or in silico predictions which are provided for thousands of chemicals, multiple exposure routes, and various species. High throughput toxicokinetics ("HTTK") is the combination of in vitro data and generic models. We establish the expected accuracy of HTTK for chemicals without in vivo data through statistical evaluation of HTTK predictions for chemicals where in vivo data do exist. The models are systems of ordinary differential equations that are developed in MCSim and solved using compiled (C-based) code for speed. A Monte Carlo sampler is included for simulating human biological variability (Ring et al., 2017 <doi:10.1016/j.envint.2017.06.004>) and propagating parameter uncertainty (Wambaugh et al., 2019 <doi:10.1093/toxsci/kfz205>). Empirically calibrated methods are included for predicting tissue:plasma partition coefficients and volume of distribution (Pearce et al., 2017 <doi:10.1007/s10928-017-9548-7>). These functions and data provide a set of tools for using IVIVE to convert concentrations from high-throughput screening experiments (for example, Tox21, ToxCast) to real-world exposures via reverse dosimetry (also known as "RTK") (Wetmore et al., 2015 <doi:10.1093/toxsci/kfv171>).

Maintained by John Wambaugh. Last updated 1 months ago.

comptox ord

0.5 match 27 stars 10.22 score 307 scripts 1 dependents

cran

infinitefactor:Bayesian Infinite Factor Models

Sampler and post-processing functions for semi-parametric Bayesian infinite factor models, motivated by the Multiplicative Gamma Shrinkage Prior of Bhattacharya and Dunson (2011) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3419391/>. Contains component C++ functions for building samplers for linear and 2-way interaction factor models using the multiplicative gamma and Dirichlet-Laplace shrinkage priors. The package also contains post processing functions to return matrices that display rotational ambiguity to identifiability through successive application of orthogonalization procedures and resolution of column label and sign switching. This package was developed with the support of the National Institute of Environmental Health Sciences grant 1R01ES028804-01.

Maintained by Evan Poworoznek. Last updated 5 years ago.

openblas cpp openmp

3.0 match 1.70 score 7 scripts

kenkellner

jagsUI:A Wrapper Around 'rjags' to Streamline 'JAGS' Analyses

A set of wrappers around 'rjags' functions to run Bayesian analyses in 'JAGS' (specifically, via 'libjags'). A single function call can control adaptive, burn-in, and sampling MCMC phases, with MCMC chains run in sequence or in parallel. Posterior distributions are automatically summarized (with the ability to exclude some monitored nodes if desired) and functions are available to generate figures based on the posteriors (e.g., predictive check plots, traceplots). Function inputs, argument syntax, and output format are nearly identical to the 'R2WinBUGS'/'R2OpenBUGS' packages to allow easy switching between MCMC samplers.

Maintained by Ken Kellner. Last updated 1 months ago.

jags cpp

0.5 match 35 stars 10.02 score 1.4k scripts 7 dependents

tmsalab

rrum:Bayesian Estimation of the Reduced Reparameterized Unified Model with Gibbs Sampling

Implementation of Gibbs sampling algorithm for Bayesian Estimation of the Reduced Reparameterized Unified Model ('rrum'), described by Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.

Maintained by James Joseph Balamuta. Last updated 1 years ago.

armadillo cdm cognitive-diagnostic-models gibbs-sampling-algorithm psychometrics rcpparmadillo rrum openblas cpp openmp

1.9 match 2.70 score 3 scripts

knudson1

stableGR:A Stable Gelman-Rubin Diagnostic for Markov Chain Monte Carlo

Practitioners of Bayesian statistics often use Markov chain Monte Carlo (MCMC) samplers to sample from a posterior distribution. This package determines whether the MCMC sample is large enough to yield reliable estimates of the target distribution. In particular, this calculates a Gelman-Rubin convergence diagnostic using stable and consistent estimators of Monte Carlo variance. Additionally, this uses the connection between an MCMC sample's effective sample size and the Gelman-Rubin diagnostic to produce a threshold for terminating MCMC simulation. Finally, this informs the user whether enough samples have been collected and (if necessary) estimates the number of samples needed for a desired level of accuracy. The theory underlying these methods can be found in "Revisiting the Gelman-Rubin Diagnostic" by Vats and Knudson (2018) <arXiv:1812:09384>.

Maintained by Christina Knudson. Last updated 2 years ago.

2.2 match 2.23 score 57 scripts 1 dependents

kwb-r

kwb.logger:Functions to read measurement data from logger files

Functions to read measurement data from logger files.

Maintained by Hauke Sonnenberg. Last updated 3 years ago.

data-import data-logger

1.8 match 1 stars 2.78 score 1 scripts 4 dependents

cran

RGE:Response from Genotype to Environment

Compute yield-stability index based on Bayesian methodology, which is useful for analyze multi-environment trials in plant breeding programs. References: Cotes Torres JM, Gonzalez Jaimes EP, and Cotes Torres A (2016) <https://revistas.unimilitar.edu.co/index.php/rfcb/article/view/2037> Seleccion de Genotipos con Alta Respuesta y Estabilidad Fenotipica en Pruebas Regionales: Recuperando el Concepto Biologico.

Maintained by Jose Miguel Cotes Torres. Last updated 3 years ago.

3.6 match 1.30 score 6 scripts

jarod-smithy

abglasso:Adaptive Bayesian Graphical Lasso

Implements a Bayesian adaptive graphical lasso data-augmented block Gibbs sampler. The sampler simulates the posterior distribution of precision matrices of a Gaussian Graphical Model. This sampler was adapted from the original MATLAB routine proposed in Wang (2012) <doi:10.1214/12-BA729>.

Maintained by Jarod Smith. Last updated 4 years ago.

2.7 match 1.70 score 1 scripts

mqbssppe

beast:Bayesian Estimation of Change-Points in the Slope of Multivariate Time-Series

Assume that a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. The package infers the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters per time-series by MCMC sampling. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence, but also available is the Poisson distribution. See Papastamoulis et al (2017) <arXiv:1709.06111> for a detailed presentation of the method.

Maintained by Panagiotis Papastamoulis. Last updated 7 years ago.

2.3 match 2.00 score 2 scripts

stochastictree

stochtree:Stochastic Tree Ensembles (XBART and BART) for Supervised Learning and Causal Inference

Flexible stochastic tree ensemble software. Robust implementations of Bayesian Additive Regression Trees (BART) Chipman, George, McCulloch (2010) <doi:10.1214/09-AOAS285> for supervised learning and Bayesian Causal Forests (BCF) Hahn, Murray, Carvalho (2020) <doi:10.1214/19-BA1195> for causal inference. Enables model serialization and parallel sampling and provides a low-level interface for custom stochastic forest samplers.

Maintained by Drew Herren. Last updated 7 hours ago.

bart bayesian-machine-learning bayesian-methods decision-trees gradient-boosted-trees machine-learning probabilistic-models tree-ensembles cpp

0.5 match 22 stars 8.57 score 40 scripts

poissonconsulting

extras:Helper Functions for Bayesian Analyses

Functions to 'numericise' 'R' objects (coerce to numeric objects), summarise 'MCMC' (Monte Carlo Markov Chain) samples and calculate deviance residuals as well as 'R' translations of some 'BUGS' (Bayesian Using Gibbs Sampling), 'JAGS' (Just Another Gibbs Sampler), 'STAN' and 'TMB' (Template Model Builder) functions.

Maintained by Nicole Hill. Last updated 2 months ago.

0.5 match 9 stars 8.49 score 15 scripts 16 dependents

mcthedwards

bsplinePsd:Bayesian Nonparametric Spectral Density Estimation Using B-Spline Priors

Implementation of a Metropolis-within-Gibbs MCMC algorithm to flexibly estimate the spectral density of a stationary time series. The algorithm updates a nonparametric B-spline prior using the Whittle likelihood to produce pseudo-posterior samples and is based on the work presented in Edwards, M.C., Meyer, R. and Christensen, N., Statistics and Computing (2018). <doi.org/10.1007/s11222-017-9796-9>.

Maintained by Matthew C. Edwards. Last updated 6 years ago.

cpp

1.6 match 1 stars 2.70 score 3 scripts

framverse

pssp:Package to interface with the WDFW Puget Sound sampling database

The pssp package serves as an interface between R and the Puget Sound sampling database. It"s goal is to ease the burden of retrieving and analyzing sport creel information in Puget Sound.

Maintained by Ty Garber. Last updated 4 months ago.

1.8 match 2.40 score

rabilon

merror:Accuracy and Precision of Measurements

N>=3 methods are used to measure each of n items. The data are used to estimate simultaneously systematic error (bias) and random error (imprecision). Observed measurements for each method or device are assumed to be linear functions of the unknown true values and the errors are assumed normally distributed. Pairwise calibration curves and plots can be easily generated. Unlike the 'ncb.od' function, the 'omx' function builds a one-factor measurement error model using 'OpenMx' and allows missing values, uses full information maximum likelihood to estimate parameters, and provides both likelihood-based and bootstrapped confidence intervals for all parameters, in addition to Wald-type intervals.

Maintained by Richard A. Bilonick. Last updated 2 years ago.

3.4 match 1.23 score 17 scripts

mezarafael

Bhat:General Likelihood Exploration

Provides functions for Maximum Likelihood Estimation, Markov Chain Monte Carlo, finding confidence intervals. The implementation is heavily based on the original Fortran source code translated to R.

Maintained by Rafael Meza. Last updated 3 years ago.

1.9 match 2.16 score 36 scripts