Showing 154 of total 154 results (show query)
mrc-ide
EpiEstim:Estimate Time Varying Reproduction Numbers from Epidemic Curves
Tools to quantify transmissibility throughout an epidemic from the analysis of time series of incidence as described in Cori et al. (2013) <doi:10.1093/aje/kwt133> and Wallinga and Teunis (2004) <doi:10.1093/aje/kwh255>.
Maintained by Anne Cori. Last updated 7 months ago.
95 stars 12.06 score 1.0k scripts 7 dependents
hmsc-r
Hmsc:Hierarchical Model of Species Communities
Hierarchical Modelling of Species Communities (HMSC) is a model-based approach for analyzing community ecological data. This package implements it in the Bayesian framework with Gibbs Markov chain Monte Carlo (MCMC) sampling (Tikhonov et al. (2020) <doi:10.1111/2041-210X.13345>).
Maintained by Otso Ovaskainen. Last updated 10 days ago.
105 stars 10.36 score 476 scripts
pecanproject
PEcAn.assim.batch: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 Istem Fer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 9.96 score 20 scripts 2 dependentspecanproject
PEcAnRTM:PEcAn Functions Used for Radiative Transfer Modeling
Functions for performing forward runs and inversions of radiative transfer models (RTMs). Inversions can be performed using maximum likelihood, or more complex hierarchical Bayesian methods. Underlying numerical analyses are optimized for speed using Fortran code.
Maintained by Alexey Shiklomanov. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsfortranjagscpp
216 stars 9.70 score 132 scripts
donaldrwilliams
BGGM:Bayesian Gaussian Graphical Models
Fit Bayesian Gaussian graphical models. The methods are separated into two Bayesian approaches for inference: hypothesis testing and estimation. There are extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, and node wise predictability. These methods were recently introduced in the Gaussian graphical model literature, including Williams (2019) <doi:10.31234/osf.io/x8dpr>, Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>, Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.
Maintained by Philippe Rast. Last updated 3 months ago.
bayes-factorsbayesian-hypothesis-testinggaussian-graphical-modelsopenblascppopenmp
55 stars 9.61 score 102 scripts 1 dependents
jongheepark
MCMCpack:Markov Chain Monte Carlo (MCMC) Package
Contains functions to perform Bayesian inference using posterior simulation for a number of statistical models. Most simulation is done in compiled C++ written in the Scythe Statistical Library Version 1.0.3. All models return 'coda' mcmc objects that can then be summarized using the 'coda' package. Some useful utility functions such as density functions, pseudo-random number generators for statistical distributions, a general purpose Metropolis sampling algorithm, and tools for visualization are provided.
Maintained by Jong Hee Park. Last updated 7 months ago.
13 stars 9.47 score 2.6k scripts 149 dependents
brianstock
MixSIAR:Bayesian Mixing Models in R
Creates and runs Bayesian mixing models to analyze biological tracer data (i.e. stable isotopes, fatty acids), which estimate the proportions of source (prey) contributions to a mixture (consumer). 'MixSIAR' is not one model, but a framework that allows a user to create a mixing model based on their data structure and research questions, via options for fixed/ random effects, source data types, priors, and error terms. 'MixSIAR' incorporates several years of advances since 'MixSIR' and 'SIAR'.
Maintained by Brian Stock. Last updated 4 years ago.
98 stars 9.27 score 122 scriptspecanproject
PEcAn.allometry:PEcAn Allometry Functions
Synthesize allometric equations or fit allometries to data.
Maintained by Mike Dietze. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplants
216 stars 9.11 score 34 scriptspecanproject
PEcAn.all: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 David LeBauer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 9.00 score 266 scriptspecanproject
PEcAn.uncertainty:PEcAn Functions Used for Propagating and Partitioning Uncertainties in 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 David LeBauer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 8.93 score 15 scripts 5 dependentspecanproject
PEcAn.workflow: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. This package provides workhorse functions that can be used to run the major steps of a PEcAn analysis.
Maintained by David LeBauer. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 8.83 score 15 scripts 4 dependentspecanproject
PEcAn.emulator:Gausian Process Emulator
Implementation of a Gaussian Process model (both likelihood and bayesian approaches) for kriging and model emulation. Includes functions for sampling design and prediction.
Maintained by Mike Dietze. Last updated 3 days ago.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplants
216 stars 8.83 score 1 scripts 6 dependents
jonesor
Rage:Life History Metrics from Matrix Population Models
Functions for calculating life history metrics using matrix population models ('MPMs'). Described in Jones et al. (2021) <doi:10.1101/2021.04.26.441330>.
Maintained by Owen Jones. Last updated 3 months ago.
12 stars 8.18 score 62 scripts 1 dependentspecanproject
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.
bayesiancyberinfrastructuredata-assimilationdata-scienceecosystem-modelecosystem-scienceforecastingmeta-analysisnational-science-foundationpecanplantsjagscpp
216 stars 8.12 score 35 scripts
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.
9 stars 8.06 score 37 scripts 8 dependents
jonesor
Rcompadre:Utilities for using the 'COM(P)ADRE' Matrix Model Database
Utility functions for interacting with the 'COMPADRE' and 'COMADRE' databases of matrix population models. Described in Jones et al. (2021) <doi:10.1101/2021.04.26.441330>.
Maintained by Owen Jones. Last updated 6 months ago.
11 stars 7.74 score 55 scripts 2 dependents
psoerensen
qgg:Statistical Tools for Quantitative Genetic Analyses
Provides an infrastructure for efficient processing of large-scale genetic and phenotypic data including core functions for: 1) fitting linear mixed models, 2) constructing marker-based genomic relationship matrices, 3) estimating genetic parameters (heritability and correlation), 4) performing genomic prediction and genetic risk profiling, and 5) single or multi-marker association analyses. Rohde et al. (2019) <doi:10.1101/503631>.
Maintained by Peter Soerensen. Last updated 11 days ago.
36 stars 7.01 score 47 scripts
reconhub
earlyR:Estimation of Transmissibility in the Early Stages of a Disease Outbreak
Implements a simple, likelihood-based estimation of the reproduction number (R0) using a branching process with a Poisson likelihood. This model requires knowledge of the serial interval distribution, and dates of symptom onsets. Infectiousness is determined by weighting R0 by the probability mass function of the serial interval on the corresponding day. It is a simplified version of the model introduced by Cori et al. (2013) <doi:10.1093/aje/kwt133>.
Maintained by Thibaut Jombart. Last updated 4 years ago.
9 stars 6.59 score 96 scripts
acaimo
Bergm:Bayesian Exponential Random Graph Models
Bayesian analysis for exponential random graph models using advanced computational algorithms. More information can be found at: <https://acaimo.github.io/Bergm/>.
Maintained by Alberto Caimo. Last updated 2 months ago.
16 stars 6.37 score 31 scripts 4 dependents
thevaachandereng
bayesCT:Simulation and Analysis of Adaptive Bayesian Clinical Trials
Simulation and analysis of Bayesian adaptive clinical trials for binomial, continuous, and time-to-event data types, incorporates historical data and allows early stopping for futility or early success. The package uses novel and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data into the analysis via the power prior or non-informative priors.
Maintained by Thevaa Chandereng. Last updated 5 years ago.
adaptivebayesian-methodsbayesian-trialclinical-trialsstatistical-power
14 stars 6.30 score 36 scripts
fabian-s
spikeSlabGAM:Bayesian Variable Selection and Model Choice for Generalized Additive Mixed Models
Bayesian variable selection, model choice, and regularized estimation for (spatial) generalized additive mixed regression models via stochastic search variable selection with spike-and-slab priors.
Maintained by Fabian Scheipl. Last updated 5 months ago.
14 stars 6.28 score 15 scripts 1 dependents
lem-usp
evolqg:Evolutionary Quantitative Genetics
Provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools. Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) <doi:10.12688/f1000research.7082.3>.
Maintained by Diogo Melo. Last updated 11 months ago.
10 stars 6.26 score 114 scripts
jsakaluk
dySEM:Dyadic Structural Equation Modeling
Scripting of structural equation models via 'lavaan' for Dyadic Data Analysis, and helper functions for supplemental calculations, tabling, and model visualization. Current models supported include Dyadic Confirmatory Factor Analysis, the ActorโPartner Interdependence Model (observed and latent), the Common Fate Model (observed and latent), Mutual Influence Model (latent), and the Bifactor Dyadic Model (latent).
Maintained by John Sakaluk. Last updated 3 days ago.
6 stars 6.12 score 10 scripts
jonesor
mpmsim:Simulation of Matrix Population Models with Defined Life History Characteristics
Allows users to simulate matrix population models with particular characteristics based on aspects of life history such as mortality trajectories and fertility trajectories. Also allows the exploration of sampling error due to small sample size.
Maintained by Owen Jones. Last updated 23 days ago.
5 stars 6.03 score 16 scripts
bioc
faers:R interface for FDA Adverse Event Reporting System
The FDA Adverse Event Reporting System (FAERS) is a database used for the spontaneous reporting of adverse events and medication errors related to human drugs and therapeutic biological products. faers pacakge serves as the interface between the FAERS database and R. Furthermore, faers pacakge offers a standardized approach for performing pharmacovigilance analysis.
Maintained by Yun Peng. Last updated 5 months ago.
softwaredataimportbiomedicalinformaticspharmacogenomicsadverse-eventsdrug-safetyfaersfaers-procedurepharmacovigilancesignal-detection
20 stars 5.90 score 5 scripts
graemeleehickey
bayesDP:Implementation of the Bayesian Discount Prior Approach for Clinical Trials
Functions for data augmentation using the Bayesian discount prior method for single arm and two-arm clinical trials, as described in Haddad et al. (2017) <doi:10.1080/10543406.2017.1300907>. The discount power prior methodology was developed in collaboration with the The Medical Device Innovation Consortium (MDIC) Computer Modeling & Simulation Working Group.
Maintained by Graeme L. Hickey. Last updated 3 months ago.
bayesianbayesian-inferencebayesian-statisticsclinical-trialsmdicposterior-predictiveposterior-probabilityprior-distributionopenblascpp
5.56 score 20 scripts 1 dependents
iainmstott
popdemo:Demographic Modelling Using Projection Matrices
Tools for modelling populations and demography using matrix projection models, with deterministic and stochastic model implementations. Includes population projection, indices of short- and long-term population size and growth, perturbation analysis, convergence to stability or stationarity, and diagnostic and manipulation tools.
Maintained by Iain Stott. Last updated 3 years ago.
5.16 score 172 scripts 7 dependents
lanl
ezECM:Event Categorization Matrix Classification for Nuclear Detonations
Implementation of an Event Categorization Matrix (ECM) detonation detection model and a Bayesian variant. Functions are provided for importing and exporting data, fitting models, and applying decision criteria for categorizing new events. This package implements methods described in the paper "Bayesian Event Categorization Matrix Approach for Nuclear Detonations" Koermer, Carmichael, and Williams (2024) available on arXiv at <doi:10.48550/arXiv.2409.18227>.
Maintained by Scott Koermer. Last updated 5 months ago.
5.08 score 4 scripts
sujit-sahu
bmstdr:Bayesian Modeling of Spatio-Temporal Data with R
Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer', 'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) <doi:10.1201/9780429318443> describes the methods in detail.
Maintained by Sujit K. Sahu. Last updated 1 days ago.
bayesianmodellingspatio-temporal-datacpp
16 stars 4.98 score 12 scriptsbioc
Melissa:Bayesian clustering and imputationa of single cell methylomes
Melissa is a Baysian probabilistic model for jointly clustering and imputing single cell methylomes. This is done by taking into account local correlations via a Generalised Linear Model approach and global similarities using a mixture modelling approach.
Maintained by C. A. Kapourani. Last updated 5 months ago.
immunooncologydnamethylationgeneexpressiongeneregulationepigeneticsgeneticsclusteringfeatureextractionregressionrnaseqbayesiankeggsequencingcoveragesinglecell
4.90 score 7 scriptsbioc
mfa:Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations
MFA models genomic bifurcations using a Bayesian hierarchical mixture of factor analysers.
Maintained by Kieran Campbell. Last updated 5 months ago.
immunooncologyrnaseqgeneexpressionbayesiansinglecellcpp
4.85 score 35 scripts
phargarten2
miWQS:Multiple Imputation Using Weighted Quantile Sum Regression
The miWQS package handles the uncertainty due to below the detection limit in a correlated component mixture problem. Researchers want to determine if a set/mixture of continuous and correlated components/chemicals is associated with an outcome and if so, which components are important in that mixture. These components share a common outcome but are interval-censored between zero and low thresholds, or detection limits, that may be different across the components. This package applies the multiple imputation (MI) procedure to the weighted quantile sum regression (WQS) methodology for continuous, binary, or count outcomes (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>). The imputation models are: bootstrapping imputation (Lubin et al (2004) <doi:10.1289/ehp.7199>), univariate Bayesian imputation (Hargarten & Wheeler (2020) <doi:10.1016/j.envres.2020.109466>), and multivariate Bayesian regression imputation.
Maintained by Paul M. Hargarten. Last updated 1 years ago.
2 stars 4.78 score 20 scripts 1 dependents
diogoferrari
hdpGLM:Hierarchical Dirichlet Process Generalized Linear Models
Implementation of MCMC algorithms to estimate the Hierarchical Dirichlet Process Generalized Linear Model (hdpGLM) presented in the paper Ferrari (2020) Modeling Context-Dependent Latent Heterogeneity, Political Analysis <DOI:10.1017/pan.2019.13> and <doi:10.18637/jss.v107.i10>.
Maintained by Diogo Ferrari. Last updated 1 years ago.
dirichlet-process-mixtureshierarchical-clusteringnonparametricnonparametricbayesnpbsemi-parametricopenblascpp
12 stars 4.78 score 5 scripts
abdalkarima
iClusterVB:Fast Integrative Clustering and Feature Selection for High Dimensional Data
A variational Bayesian approach for fast integrative clustering and feature selection, facilitating the analysis of multi-view, mixed type, high-dimensional datasets with applications in fields like cancer research, genomics, and more.
Maintained by Abdalkarim Alnajjar. Last updated 4 months ago.
1 stars 4.74 score 6 scripts
epiforecasts
EpiNow:Estimate Realtime Case Counts and Time-varying Epidemiological Parameters
To identify changes in the reproduction number, rate of spread, and doubling time during the course of outbreaks whilst accounting for potential biases due to delays in case reporting.
Maintained by Sam Abbott. Last updated 5 years ago.
33 stars 4.74 score 111 scripts
bioc
mist:Differential Methylation Analysis for scDNAm Data
mist (Methylation Inference for Single-cell along Trajectory) is a hierarchical Bayesian framework for modeling DNA methylation trajectories and performing differential methylation (DM) analysis in single-cell DNA methylation (scDNAm) data. It estimates developmental-stage-specific variations, identifies genomic features with drastic changes along pseudotime, and, for two phenotypic groups, detects features with distinct temporal methylation patterns. mist uses Gibbs sampling to estimate parameters for temporal changes and stage-specific variations.
Maintained by Daoyu Duan. Last updated 2 months ago.
epigeneticsdifferentialmethylationdnamethylationsinglecellsoftware
4.73 score 12 scripts
ejosymart
sizeMat:Estimate Size at Sexual Maturity
Estimate morphometric and gonadal size at sexual maturity for organisms, usually fish and invertebrates. It includes methods for classification based on relative growth (using principal components analysis, hierarchical clustering, discriminant analysis), logistic regression (Frequentist or Bayes), parameters estimation and some basic plots.
Maintained by Josymar Torrejon-Magallanes. Last updated 1 years ago.
allometric-variablesgonad-maturitymorphometric-maturity
4 stars 4.72 score 26 scripts
sumanm47
BSTZINB:Association Among Disease Counts and Socio-Environmental Factors
Estimation of association between disease or death counts (e.g. COVID-19) and socio-environmental risk factors using a zero-inflated Bayesian spatiotemporal model. Non-spatiotemporal models and/or models without zero-inflation are also included for comparison. Functions to produce corresponding maps are also included. See Chakraborty et al. (2022) <doi:10.1007/s13253-022-00487-1> for more details on the method.
Maintained by Suman Majumder. Last updated 2 months ago.
4.70 score 2 scripts
philippallmann
ANOM:Analysis of Means
Analysis of means (ANOM) as used in technometrical computing. The package takes results from multiple comparisons with the grand mean (obtained with 'multcomp', 'SimComp', 'nparcomp', or 'MCPAN') or corresponding simultaneous confidence intervals as input and produces ANOM decision charts that illustrate which group means deviate significantly from the grand mean.
Maintained by Philip Pallmann. Last updated 8 years ago.
4.70 score 10 scriptsbioc
CARDspa:Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics
CARD is a reference-based deconvolution method that estimates cell type composition in spatial transcriptomics based on cell type specific expression information obtained from a reference scRNA-seq data. A key feature of CARD is its ability to accommodate spatial correlation in the cell type composition across tissue locations, enabling accurate and spatially informed cell type deconvolution as well as refined spatial map construction. CARD relies on an efficient optimization algorithm for constrained maximum likelihood estimation and is scalable to spatial transcriptomics with tens of thousands of spatial locations and tens of thousands of genes.
Maintained by Jing Fu. Last updated 2 days ago.
spatialsinglecelltranscriptomicsvisualizationopenblascppopenmp
4.60 score 3 scriptsjongheepark
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.
bayesianchangepointlatent-spacenetwork
5 stars 4.60 score 16 scripts
lukasklima
quid:Bayesian Mixed Models for Qualitative Individual Differences
Test whether equality and order constraints hold for all individuals simultaneously by comparing Bayesian mixed models through Bayes factors. A tutorial style vignette and a quickstart guide are available, via vignette("manual", "quid"), and vignette("quickstart", "quid") respectively. See Haaf and Rouder (2017) <doi:10.1037/met0000156>; Haaf, Klaassen and Rouder (2019) <doi:10.31234/osf.io/a4xu9>; and Rouder & Haaf (2021) <doi:10.5334/joc.131>.
Maintained by Lukas Klima. Last updated 3 years ago.
3 stars 4.59 score 13 scripts
falkcarl
multilevelmediation:Utility Functions for Multilevel Mediation Analysis
The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for multilevel mediation, the option of a moderating variable for either the a, b, or both paths, and covariates. Currently the 1-1-1 model is supported and several options of random effects; the initial code for bootstrapping was evaluated in simulations by Falk, Vogel, Hammami, and Mioฤeviฤ (2024) <doi:10.3758/s13428-023-02079-4>. Support for Bayesian estimation using 'brms' comprises ongoing work. Currently only continuous mediators and outcomes are supported. Factors for any predictors must be numerically represented.
Maintained by Carl F. Falk. Last updated 3 months ago.
6 stars 4.56 score 2 scriptsbioc
DCATS:Differential Composition Analysis Transformed by a Similarity matrix
Methods to detect the differential composition abundances between conditions in singel-cell RNA-seq experiments, with or without replicates. It aims to correct bias introduced by missclaisification and enable controlling of confounding covariates. To avoid the influence of proportion change from big cell types, DCATS can use either total cell number or specific reference group as normalization term.
Maintained by Xinyi Lin. Last updated 5 months ago.
4.53 score 34 scripts
karolinehuth
easybgm:Extracting and Visualizing Bayesian Graphical Models
Fit and visualize the results of a Bayesian analysis of networks commonly found in psychology. The package supports fitting cross-sectional network models fitted using the packages 'BDgraph', 'bgms' and 'BGGM'. The package provides the parameter estimates, posterior inclusion probabilities, inclusion Bayes factor, and the posterior density of the parameters. In addition, for 'BDgraph' and 'bgms' it allows to assess the posterior structure space. Furthermore, the package comes with an extensive suite for visualizing results.
Maintained by Karoline Huth. Last updated 5 months ago.
4.51 score 27 scripts
oswaldogressani
EpiLPS:A Fast and Flexible Bayesian Tool for Estimating Epidemiological Parameters
Estimation of epidemiological parameters with Laplacian-P-splines following the methodology of Gressani et al. (2022) <doi:10.1371/journal.pcbi.1010618>.
Maintained by Oswaldo Gressani. Last updated 5 months ago.
19 stars 4.51 score 17 scripts
ai4ci
ggoutbreak:Estimate Incidence, Proportions and Exponential Growth Rates
Simple statistical models and visualisations for calculating the incidence, proportion, exponential growth rate, and reproduction number of infectious disease case time series. This toolkit was largely developed during the COVID-19 pandemic.
Maintained by Robert Challen. Last updated 2 months ago.
1 stars 4.30 score
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.
9 stars 4.18 score 34 scripts
vsousa
poolHelper:Simulates Pooled Sequencing Genetic Data
Simulates pooled sequencing data under a variety of conditions. Also allows for the evaluation of the average absolute difference between allele frequencies computed from genotypes and those computed from pooled data. Carvalho et al., (2022) <doi:10.1101/2023.01.20.524733>.
Maintained by Joรฃo Carvalho. Last updated 2 years ago.
4.18 score 3 scripts 1 dependents
yooyh
bartcs:Bayesian Additive Regression Trees for Confounder Selection
Fit Bayesian Regression Additive Trees (BART) models to select true confounders from a large set of potential confounders and to estimate average treatment effect. For more information, see Kim et al. (2023) <doi:10.1111/biom.13833>.
Maintained by Yeonghoon Yoo. Last updated 11 months ago.
3 stars 4.18 score 3 scripts
quantmeth
Rnest:Next Eigenvalue Sufficiency Test
Determine the number of dimensions to retain in exploratory factor analysis. The main function, nest(), returns the solution and the plot(nest()) returns a plot.
Maintained by P.-O. Caron. Last updated 9 days ago.
exploratory-data-analysisfactor-analysis
2 stars 4.07 score 13 scriptsbioc
frenchFISH:Poisson Models for Quantifying DNA Copy-number from FISH Images of Tissue Sections
FrenchFISH comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting.
Maintained by Adam Berman. Last updated 5 months ago.
softwarebiomedicalinformaticscellbiologygeneticshiddenmarkovmodelpreprocessing
4.00 score 3 scripts
puigjos
ConsReg:Fitting Regression Models & Regression with Arma Errors, Subject to Constraints and Restrictions to the Coefficients
Fits or generalized linear models either a regression with arma errors (Time series) where the parameters could be subject to constraints or restrictions. The model is specified by an objective function (Gaussian, Binomial or Poisson) or an Arma order (p,q), a vector of bound constraints for the coefficients and the possibility to incorporate restrictions among coefficients (i.e b1 > b2).
Maintained by Josep Puig. Last updated 5 years ago.
2 stars 4.00 score 4 scripts
cribbie
negligible:A Collection of Functions for Negligible Effect/Equivalence Testing
Researchers often want to evaluate whether there is a negligible relationship among variables. The 'negligible' package provides functions that are useful for conducting negligible effect testing (also called equivalence testing). For example, there are functions for evaluating the equivalence of means or the presence of a negligible association (correlation or regression). Beribisky, N., Mara, C., & Cribbie, R. A. (2020) <doi:10.20982/tqmp.16.4.p424>. Beribisky, N., Davidson, H., Cribbie, R. A. (2019) <doi:10.7717/peerj.6853>. Shiskina, T., Farmus, L., & Cribbie, R. A. (2018) <doi:10.20982/tqmp.14.3.p167>. Mara, C. & Cribbie, R. A. (2017) <doi:10.1080/00220973.2017.1301356>. Counsell, A. & Cribbie, R. A. (2015) <doi:10.1111/bmsp.12045>. van Wieringen, K. & Cribbie, R. A. (2014) <doi:10.1111/bmsp.12015>. Goertzen, J. R. & Cribbie, R. A. (2010) <doi:10.1348/000711009x475853>. Cribbie, R. A., Gruman, J. & Arpin-Cribbie, C. (2004) <doi:10.1002/jclp.10217>.
Maintained by Robert Cribbie. Last updated 5 months ago.
equivalence-testingnegligible-effect-statistical-testingnegligible-effect-testingstatistics
4 stars 4.00 score 10 scripts
allengoebl
iopsych:Methods for Industrial/Organizational Psychology
Collection of functions for IO Psychologists.
Maintained by Allen Goebl. Last updated 7 years ago.
3 stars 4.00 score 66 scripts
kasselhingee
scorematchingad:Score Matching Estimation by Automatic Differentiation
Hyvรคrinen's score matching (Hyvรคrinen, 2005) <https://jmlr.org/papers/v6/hyvarinen05a.html> is a useful estimation technique when the normalising constant for a probability distribution is difficult to compute. This package implements score matching estimators using automatic differentiation in the 'CppAD' library <https://github.com/coin-or/CppAD> and is designed for quickly implementing score matching estimators for new models. Also available is general robustification (Windham, 1995) <https://www.jstor.org/stable/2346159>. Already in the package are estimators for directional distributions (Mardia, Kent and Laha, 2016) <doi:10.48550/arXiv.1604.08470> and the flexible Polynomially-Tilted Pairwise Interaction model for compositional data. The latter estimators perform well when there are zeros in the compositions (Scealy and Wood, 2023) <doi:10.1080/01621459.2021.2016422>, even many zeros (Scealy, Hingee, Kent, and Wood, 2024) <doi:10.1007/s11222-024-10412-w>. A partial interface to CppAD's ADFun objects is also available.
Maintained by Kassel Liam Hingee. Last updated 3 months ago.
automatic-differentiationscore-matchingstatistical-inferencecpp
3.98 score 1 scripts
bips-hb
pvm:PharmacoVigilance Methods
A collection of methods used in the field of pharmacovigilance for the dectection of 'interesting' drug-adverse event pairs from spontaneous reporting data.
Maintained by Louis Dijkstra. Last updated 2 months ago.
27 stars 3.94 score 16 scripts
jlepird
prefeR:R Package for Pairwise Preference Elicitation
Allows users to derive multi-objective weights from pairwise comparisons, which research shows is more repeatable, transparent, and intuitive other techniques. These weights can be rank existing alternatives or to define a multi-objective utility function for optimization.
Maintained by John Lepird. Last updated 3 years ago.
bayesian-inferencemultiobjective-optimizationpreference-elicitation
1 stars 3.90 score 16 scripts
alexchristensen
latentFactoR:Data Simulation Based on Latent Factors
Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.
Maintained by Alexander Christensen. Last updated 8 months ago.
3 stars 3.88 score 2 scriptsvsousa
poolABC:Approximate Bayesian Computation with Pooled Sequencing Data
Provides functions to simulate Pool-seq data under models of demographic formation and to import Pool-seq data from real populations. Implements two ABC algorithms for performing parameter estimation and model selection using Pool-seq data. Cross-validation can also be performed to assess the accuracy of ABC estimates and model choice. Carvalho et al., (2022) <doi:10.1111/1755-0998.13834>.
Maintained by Joรฃo Carvalho. Last updated 2 years ago.
1 stars 3.70 score 3 scripts
wzhang17
lchemix:A Bayesian Multi-Dimensional Couple-Based Latent Risk Model
A joint latent class model where a hierarchical structure exists, with an interaction between female and male partners of a couple. A Bayesian perspective to inference and Markov chain Monte Carlo algorithms to obtain posterior estimates of model parameters. The reference paper is: Beom Seuk Hwang, Zhen Chen, Germaine M.Buck Louis, Paul S. Albert, (2018) "A Bayesian multi-dimensional couple-based latent risk model with an application to infertility". Biometrics, 75, 315-325. <doi:10.1111/biom.12972>.
Maintained by Weimin Zhang. Last updated 5 years ago.
3.70 score 2 scripts
miguel-sorrel
cdcatR:Cognitive Diagnostic Computerized Adaptive Testing
Provides a set of functions for conducting cognitive diagnostic computerized adaptive testing applications (Chen, 2009) <DOI:10.1007/s11336-009-9123-2>). It includes different item selection rules such us the global discrimination index (Kaplan, de la Torre, and Barrada (2015) <DOI:10.1177/0146621614554650>) and the nonparametric selection method (Chang, Chiu, and Tsai (2019) <DOI:10.1177/0146621618813113>), as well as several stopping rules. Functions for generating item banks and responses are also provided. To guide item bank calibration, model comparison at the item level can be conducted using the two-step likelihood ratio test statistic by Sorrel, de la Torre, Abad and Olea (2017) <DOI:10.1027/1614-2241/a000131>.
Maintained by Miguel A. Sorrel. Last updated 3 years ago.
5 stars 3.63 score 17 scripts
louisaslett
ReliabilityTheory:Structural Reliability Analysis
Perform structural reliability analysis, including computation and simulation with system signatures, Samaniego (2007) <doi:10.1007/978-0-387-71797-5>, and survival signatures, Coolen and Coolen-Maturi (2013) <doi:10.1007/978-3-642-30662-4_8>. Additionally supports parametric and topological inference given system lifetime data, Aslett (2012) <https://www.louisaslett.com/PhD_Thesis.pdf>.
Maintained by Louis Aslett. Last updated 6 months ago.
7 stars 3.62 score 12 scripts
keblu
nse:Numerical Standard Errors Computation in R
Collection of functions designed to calculate numerical standard error (NSE) of univariate time series as described in Ardia et al. (2018) <doi:10.1515/jtse-2017-0011> and Ardia and Bluteau (2017) <doi:10.21105/joss.00172>.
Maintained by Keven Bluteau. Last updated 2 years ago.
3.56 score 12 scripts 1 dependents
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.
3.30 score 3 scriptsbioc
epigenomix:Epigenetic and gene transcription data normalization and integration with mixture models
A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types.
Maintained by Hans-Ulrich Klein. Last updated 5 months ago.
chipseqgeneexpressiondifferentialexpressionclassification
3.30 score 1 scripts
common2016
FAVAR:Bayesian Analysis of a FAVAR Model
Estimate a FAVAR model by a Bayesian method, based on Bernanke et al. (2005) <DOI:10.1162/0033553053327452>.
Maintained by Pu Chen. Last updated 2 years ago.
3 stars 3.18 score 7 scripts
nicolas-schmidt
BayesMFSurv:Bayesian Misclassified-Failure Survival Model
Contains a split population survival estimator that models the misclassification probability of failure versus right-censored events. The split population survival estimator is described in Bagozzi et al. (2019) <doi:10.1017/pan.2019.6>.
Maintained by Nicolas Schmidt. Last updated 5 years ago.
misclassified-failure-estimatessurvivalcpp
1 stars 3.00 score
pcbrom
bgumbel:Bimodal Gumbel Distribution
Bimodal Gumbel distribution. General functions for performing extreme value analysis.
Maintained by Pedro C. Brom. Last updated 4 years ago.
bgbromciragumbel-distributionmcmcpereiraroberto-vila
2 stars 3.00 score 1 scripts
lbenitesanchez
ssmsn:Scale-Shape Mixtures of Skew-Normal Distributions
It provides the density and random number generator for the Scale-Shape Mixtures of Skew-Normal Distributions proposed by Jamalizadeh and Lin (2016) <doi:10.1007/s00180-016-0691-1>.
Maintained by Luis Benites. Last updated 8 years ago.
2 stars 3.00 score 2 scripts
xilustat
Bayenet:Bayesian Quantile Elastic Net for Genetic Study
As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty for quantile regression in genetic analysis. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in 'C++' and R.
Maintained by Xi Lu. Last updated 8 days ago.
3.00 score
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 Frรผhwirth-Schnatter et al. (2021) <doi:10.1214/21-BA1294>.
Maintained by Gertraud Malsiner-Walli. Last updated 2 months ago.
3.00 score 4 scriptswzhang17
sorocs:A Bayesian Semiparametric Approach to Correlated ROC Surfaces
A Bayesian semiparametric Dirichlet process mixtures to estimate correlated receiver operating characteristic (ROC) surfaces and the associated volume under the surface (VUS) with stochastic order constraints. The reference paper is:Zhen Chen, Beom Seuk Hwang, (2018) "A Bayesian semiparametric approach to correlated ROC surfaces with stochastic order constraints". Biometrics, 75, 539-550. <doi:10.1111/biom.12997>.
Maintained by Weimin Zhang. Last updated 5 years ago.
3.00 score 2 scripts
vroys
geommc:Geometric Markov Chain Sampling
Simulates from discrete and continuous target distributions using geometric Metropolis-Hastings (MH) algorithms. Users specify the target distribution by an R function that evaluates the log un-normalized pdf or pmf. The package also contains a function implementing a specific geometric MH algorithm for performing high dimensional Bayesian variable selection.
Maintained by Vivekananda Roy. Last updated 5 months ago.
2.95 score 1 scripts
fvidoli
Compind:Composite Indicators Functions
A collection of functions to calculate Composite Indicators methods, focusing, in particular, on the normalisation and weighting-aggregation steps, as described in OECD Handbook on constructing composite indicators: methodology and user guide, 2008, 'Vidoli' and 'Fusco' and 'Mazziotta' <doi:10.1007/s11205-014-0710-y>, 'Mazziotta' and 'Pareto' (2016) <doi:10.1007/s11205-015-0998-2>, 'Van Puyenbroeck and 'Rogge' <doi:10.1016/j.ejor.2016.07.038> and other authors.
Maintained by Francesco Vidoli. Last updated 3 months ago.
1 stars 2.90 score 40 scripts
maxsal
covid19india:Pulling Clean Data from Covid19india.org
Pull raw and pre-cleaned versions of national and state-level COVID-19 time-series data from covid19india.org <https://www.covid19india.org>. Easily obtain and merge case count data, testing data, and vaccine data. Also assists in calculating the time-varying effective reproduction number with sensible parameters for COVID-19.
Maintained by Max Salvatore. Last updated 3 years ago.
covid-19covid-19-datacovid-19-india
2.85 score 14 scripts
tpourmohamad
bmet:Bayesian Multigroup Equivalence Testing
Calculates the necessary quantities to perform Bayesian multigroup equivalence testing. Currently the package includes the Bayesian models and equivalence criteria outlined in Pourmohamad and Lee (2023) <doi:10.1002/sta4.645>, but more models and equivalence testing features may be added over time.
Maintained by Tony Pourmohamad. Last updated 1 years ago.
2.70 score
yunshandys
SurrogateBMA:Flexible Evaluation of Surrogate Markers with Bayesian Model Averaging
Provides functions to estimate the proportion of treatment effect explained by the surrogate marker using a Bayesian Model Averaging approach. Duan and Parast (2023) <doi:10.1002/sim.9986>.
Maintained by Yunshan Duan. Last updated 1 years ago.
2.70 score
adrincont
BMTAR:Bayesian Approach for MTAR Models with Missing Data
Implements parameter estimation using a Bayesian approach for Multivariate Threshold Autoregressive (MTAR) models with missing data using Markov Chain Monte Carlo methods. Performs the simulation of MTAR processes (mtarsim()), estimation of matrix parameters and the threshold values (mtarns()), identification of the autoregressive orders using Bayesian variable selection (mtarstr()), identification of the number of regimes using Metropolised Carlin and Chib (mtarnumreg()) and estimate missing data, coefficients and covariance matrices conditional on the autoregressive orders, the threshold values and the number of regimes (mtarmissing()). Calderon and Nieto (2017) <doi:10.1080/03610926.2014.990758>.
Maintained by Andrey Duvan Rincon Torres. Last updated 3 years ago.
1 stars 2.70 score 2 scripts
geobosh
mixAR:Mixture Autoregressive Models
Model time series using mixture autoregressive (MAR) models. Implemented are frequentist (EM) and Bayesian methods for estimation, prediction and model evaluation. See Wong and Li (2002) <doi:10.1111/1467-9868.00222>, Boshnakov (2009) <doi:10.1016/j.spl.2009.04.009>), and the extensive references in the documentation.
Maintained by Georgi N. Boshnakov. Last updated 5 months ago.
assymetricheteroskedasticitymixture-autoregressivestudent-ttime-series
1 stars 2.70 score 6 scripts
dbasu-umass
clptheory:Compute Price of Production and Labor Values
Computes the uniform rate of profit, the vector of price of production and the vector of labor values; and also compute measures of deviation between relative prices of production and relative values. <https://scholarworks.umass.edu/econ_workingpaper/347/>. You provide the input-output data and 'clptheory' does the calculations for you.
Maintained by Deepankar Basu. Last updated 2 years ago.
2.70 score 1 scriptstpourmohamad
BayesDissolution:Bayesian Models for Dissolution Testing
Fits Bayesian models (amongst others) to dissolution data sets that can be used for dissolution testing. The package was originally constructed to include only the Bayesian models outlined in Pourmohamad et al. (2022) <doi:10.1111/rssc.12535>. However, additional Bayesian and non-Bayesian models (based on bootstrapping and generalized pivotal quanties) have also been added. More models may be added over time.
Maintained by Tony Pourmohamad. Last updated 1 years ago.
2.70 score
nathansam
BASSLINE:Bayesian Survival Analysis Using Shape Mixtures of Log-Normal Distributions
Mixtures of life distributions provide a convenient framework for survival analysis: particularly when standard models such as the Weibull are unable to capture some features from the data. These mixtures can also account for unobserved heterogeneity or outlying observations. BASSLINE uses shape mixtures of log-normal distributions and has particular applicability to data with fat tails.
Maintained by Nathan Constantine-Cooke. Last updated 3 years ago.
2.70 score
sullivan0147
midas2:An Information Borrowing Drug-Combination Bayesian Platform Design(MIDAS-2)
An Information borrowing drug-combination Bayesian platform design with subgroup exploration and hierarchical constrain.
Maintained by Su Liwen. Last updated 3 years ago.
2.70 score
kwkim89
SizeEstimation:Estimating the Sizes of Populations at Risk of HIV Infection from Multiple Data Sources Using a Bayesian Hierarchical Model
This function develops an algorithm for presenting a Bayesian hierarchical model for estimating the sizes of local and national drug injected populations in Bangladesh. The model incorporates multiple commonly used data sources including mapping data, surveys, interventions, capture-recapture data, estimates or guesstimates from organizations, and expert opinion.
Maintained by Kyongwon Kim. Last updated 6 years ago.
2.70 score 1 scripts
itsarthurwhite
BayesLCA:Bayesian Latent Class Analysis
Bayesian Latent Class Analysis using several different methods.
Maintained by Arthur White. Last updated 5 years ago.
2 stars 2.48 score 38 scripts
mbojan
NSUM:Network Scale Up Method
A Bayesian framework for population group size estimation using the Network Scale Up Method (NSUM). Size estimates are based on a random degree model and include options to adjust for barrier and transmission effects.
Maintained by Aaron J. Baraff. Last updated 3 years ago.
2.48 score 8 scripts
coalesce-lab
NSUM:Network Scale Up Method
A Bayesian framework for population group size estimation using the Network Scale Up Method (NSUM). Size estimates are based on a random degree model and include options to adjust for barrier and transmission effects.
Maintained by Aaron J. Baraff. Last updated 2 years ago.
2.48 score 8 scripts
davidchampredon
ern:Effective Reproduction Number Estimation
Estimate the effective reproduction number from wastewater and clinical data sources.
Maintained by David Champredon. Last updated 2 months ago.
2.45 score 14 scripts
daifengstat
agRee:Various Methods for Measuring Agreement
Bland-Altman plot and scatter plot with identity line for visualization and point and interval estimates for different metrics related to reproducibility/repeatability/agreement including the concordance correlation coefficient, intraclass correlation coefficient, within-subject coefficient of variation, smallest detectable difference, and mean normalized smallest detectable difference.
Maintained by Dai Feng. Last updated 5 years ago.
2.34 score 44 scripts
patning
mbsts:Multivariate Bayesian Structural Time Series
Tools for data analysis with multivariate Bayesian structural time series (MBSTS) models. Specifically, the package provides facilities for implementing general structural time series models, flexibly adding on different time series components (trend, season, cycle, and regression), simulating them, fitting them to multivariate correlated time series data, conducting feature selection on the regression component.
Maintained by Ning Ning. Last updated 2 years ago.
2 stars 2.30 score 5 scriptsbioc
GraphAT:Graph Theoretic Association Tests
Functions and data used in Balasubramanian, et al. (2004)
Maintained by Thomas LaFramboise. Last updated 5 months ago.
2.30 score 4 scriptsdaifengstat
miscF:Miscellaneous Functions
Various functions for random number generation, density estimation, classification, curve fitting, and spatial data analysis.
Maintained by Dai Feng. Last updated 5 years ago.
2.26 score 15 scripts 2 dependentsdaifengstat
PottsUtils:Utility Functions of the Potts Models
There are three sets of functions. The first produces basic properties of a graph and generates samples from multinomial distributions to facilitate the simulation functions (they maybe used for other purposes as well). The second provides various simulation functions for a Potts model in Potts, R. B. (1952) <doi:10.1017/S0305004100027419>. The third currently includes only one function which computes the normalizing constant of a Potts model based on simulation results.
Maintained by Dai Feng. Last updated 5 months ago.
2.11 score 13 scripts
sscogges
noncomplyR:Bayesian Analysis of Randomized Experiments with Non-Compliance
Functions for Bayesian analysis of data from randomized experiments with non-compliance. The functions are based on the models described in Imbens and Rubin (1997) <doi:10.1214/aos/1034276631>. Currently only two types of outcome models are supported: binary outcomes and normally distributed outcomes. Models can be fit with and without the exclusion restriction and/or the strong access monotonicity assumption. Models are fit using the data augmentation algorithm as described in Tanner and Wong (1987) <doi:10.2307/2289457>.
Maintained by Scott Coggeshall. Last updated 8 years ago.
2.00 score 7 scripts
github-js
BayesESS:Determining Effective Sample Size
Determines effective sample size of a parametric prior distribution in Bayesian models. For a web-based Shiny application related to this package, see <https://implement.shinyapps.io/bayesess/>.
Maintained by Jaejoon Song. Last updated 5 years ago.
2.00 score 4 scripts
zsviolet
PRSPGx:Construct PGx PRS
Construct pharmacogenomics (PGx) polygenic risk score (PRS) with PRS-PGx-Unadj (unadjusted), PRS-PGx-CT (clumping and thresholding), PRS-PGx-L, -GL, -SGL (penalized regression), PRS-PGx-Bayes (Bayesian regression). Package is based on ''Pharmacogenomics Polyenic Risk Score for Drug Response Prediction Using PRS-PGx Methods'' by Zhai, S., Zhang, H., Mehrotra, D.V., and Shen, J., 2021 (submitted).
Maintained by Song Zhai. Last updated 3 years ago.
2.00 score 3 scripts
jo-karl
ccpsyc:Methods for Cross-Cultural Psychology
With the development of new cross-cultural methods this package is intended to combine multiple functions automating and simplifying functions providing a unified analysis approach for commonly employed methods.
Maintained by Johannes Karl. Last updated 2 years ago.
1 stars 2.00 score 1 scriptsmrc-ide
jointlyr:Joint estimation of initial incidence and reproduction number
Provides a Stan implementation of a method to jointly estimate incidence and reproduction number.
Maintained by Sangeeta Bhatia. Last updated 3 years ago.
2.00 score 1 scripts
cran
MCPAN:Multiple Comparisons Using Normal Approximation
Multiple contrast tests and simultaneous confidence intervals based on normal approximation. With implementations for binomial proportions in a 2xk setting (risk difference and odds ratio), poly-3-adjusted tumour rates, biodiversity indices (multinomial data) and expected values under lognormal assumption. Approximative power calculation for multiple contrast tests of binomial and Gaussian data.
Maintained by Frank Schaarschmidt. Last updated 7 years ago.
1 stars 1.95 score 3 dependents
joshuaraytanzer
brxx:Bayesian Test Reliability Estimation
When samples contain missing data, are small, or are suspected of bias, estimation of scale reliability may not be trustworthy. A recommended solution for this common problem has been Bayesian model estimation. Bayesian methods rely on user specified information from historical data or researcher intuition to more accurately estimate the parameters. This package provides a user friendly interface for estimating test reliability. Here, reliability is modeled as a beta distributed random variable with shape parameters alpha=true score variance and beta=error variance (Tanzer & Harlow, 2020) <doi:10.1080/00273171.2020.1854082>.
Maintained by Joshua Ray Tanzer. Last updated 4 years ago.
1.70 score
mqbssppe
factor.switching:Post-Processing MCMC Outputs of Bayesian Factor Analytic Models
A well known identifiability issue in factor analytic models is the invariance with respect to orthogonal transformations. This problem burdens the inference under a Bayesian setup, where Markov chain Monte Carlo (MCMC) methods are used to generate samples from the posterior distribution. The package applies a series of rotation, sign and permutation transformations (Papastamoulis and Ntzoufras (2022) <DOI:10.1007/s11222-022-10084-4>) into raw MCMC samples of factor loadings, which are provided by the user. The post-processed output is identifiable and can be used for MCMC inference on any parametric function of factor loadings. Comparison of multiple MCMC chains is also possible.
Maintained by Panagiotis Papastamoulis. Last updated 1 years ago.
1 stars 1.48 score 2 scripts 1 dependentscran
BSagri:Safety Assessment in Agricultural Field Trials
Collection of functions, data sets and code examples for evaluations of field trials with the objective of equivalence assessment.
Maintained by Frank Schaarschmidt. Last updated 7 years ago.
1 stars 1.48 score
manuelaott
pCalibrate:Bayesian Calibrations of p-Values
Implements transformations of p-values to the smallest possible Bayes factor within the specified class of alternative hypotheses, as described in Held & Ott (2018, <doi:10.1146/annurev-statistics-031017-100307>). Covers several common testing scenarios such as z-tests, t-tests, likelihood ratio tests and the F-test.
Maintained by Manuela Ott. Last updated 5 years ago.
1.36 score 23 scripts
benoit-liquet
MBSGS:Multivariate Bayesian Sparse Group Selection with Spike and Slab
An implementation of a Bayesian sparse group model using spike and slab priors in a regression context. It is designed for regression with a multivariate response variable, but also provides an implementation for univariate response.
Maintained by Benoit Liquet. Last updated 8 years ago.
2 stars 1.34 score 11 scriptscran
MBSP:Multivariate Bayesian Model with Shrinkage Priors
Gibbs sampler for fitting multivariate Bayesian linear regression with shrinkage priors (MBSP), using the three parameter beta normal family. The method is described in Bai and Ghosh (2018) <doi:10.1016/j.jmva.2018.04.010>.
Maintained by Ray Bai. Last updated 2 years ago.
1.30 scorecran
pairwiseCI:Confidence Intervals for Two Sample Comparisons
Calculation of the parametric, nonparametric confidence intervals for the difference or ratio of location parameters, nonparametric confidence interval for the Behrens-Fisher problem and for the difference, ratio and odds-ratio of binomial proportions for comparison of independent samples. Common wrapper functions to split data sets and apply confidence intervals or tests to these subsets. A by-statement allows calculation of CI separately for the levels of further factors. CI are not adjusted for multiplicity.
Maintained by Frank Schaarschmidt. Last updated 6 years ago.
1.30 score
freyrray
riAFTBART:A Flexible Approach for Causal Inference with Multiple Treatments and Clustered Survival Outcomes
Random-intercept accelerated failure time (AFT) model utilizing Bayesian additive regression trees (BART) for drawing causal inferences about multiple treatments while accounting for the multilevel survival data structure. It also includes an interpretable sensitivity analysis approach to evaluate how the drawn causal conclusions might be altered in response to the potential magnitude of departure from the no unmeasured confounding assumption.This package implements the methods described by Hu et al. (2022) <doi:10.1002/sim.9548>.
Maintained by Fengrui Zhang. Last updated 10 months ago.
1.28 score 19 scripts
christopherdhare
anominate:Alpha-NOMINATE Ideal Point Estimator
Provides functions to estimate and interpret the alpha-NOMINATE ideal point model developed in Carroll et al. (2013, <doi:10.1111/ajps.12029>). alpha-NOMINATE extends traditional spatial voting frameworks by allowing for a mixture of Gaussian and quadratic utility functions, providing flexibility in modeling political actors' preferences. The package uses Markov Chain Monte Carlo (MCMC) methods for parameter estimation, supporting robust inference about individuals' ideological positions and the shape of their utility functions. It also contains functions to simulate data from the model and to calculate the probability of a vote passing given the ideal points of the legislators/voters and the estimated location of the choice alternatives.
Maintained by Christopher Hare. Last updated 4 months ago.
1.20 score 16 scripts
dorminiakao
HMMmlselect:Determine the Number of States in Hidden Markov Models via Marginal Likelihood
Provide functions to make estimate the number of states for a hidden Markov model (HMM) using marginal likelihood method proposed by the authors. See the Manual.pdf file a detail description of all functions, and a detail tutorial.
Maintained by Chu-Lan Michael Kao. Last updated 5 years ago.
1 stars 1.11 score 13 scripts
rosineide
llbayesireg:The L-Logistic Bayesian Regression
R functions and data sets for the work Paz, R.F., Balakrishnan, N and Bazรกn, J.L. (2018). L-logistic regression models: Prior sensitivity analysis, robustness to outliers and applications. Brazilian Journal of Probability and Statistics, <https://www.imstat.org/wp-content/uploads/2018/05/BJPS397.pdf>.
Maintained by Rosineide Fernando da Paz. Last updated 6 years ago.
1.00 score 7 scriptscran
uskewFactors:Model-Based Clustering via Mixtures of Unrestricted Skew-t Sactor Analyzer Models
Implements mixtures of unrestricted skew-t factor analyzer models via the EM algorithm.
Maintained by Paula M. Murray. Last updated 9 years ago.
1.00 scorecran
mvst:Bayesian Inference for the Multivariate Skew-t Model
Estimates the multivariate skew-t and nested models, as described in the articles Liseo, B., Parisi, A. (2013). Bayesian inference for the multivariate skew-normal model: a population Monte Carlo approach. Comput. Statist. Data Anal. <doi:10.1016/j.csda.2013.02.007> and in Parisi, A., Liseo, B. (2017). Objective Bayesian analysis for the multivariate skew-t model. Statistical Methods & Applications <doi: 10.1007/s10260-017-0404-0>.
Maintained by Antonio Parisi. Last updated 1 years ago.
1.00 scorecran
clickb:Web Data Analysis by Bayesian Mixture of Markov Models
Designed for web usage data analysis, it implements tools to process web sequences and identify web browsing profiles through sequential classification. Sequences' clusters are identified by using a model-based approach, specifically mixture of discrete time first-order Markov models for categorical web sequences. A Bayesian approach is used to estimate model parameters and identify sequences classification as proposed by Fruehwirth-Schnatter and Pamminger (2010) <doi:10.1214/10-BA606>.
Maintained by Furio Urso. Last updated 2 years ago.
1.00 score
sourish-cmi
PortRisk:Portfolio Risk Analysis
Risk Attribution of a portfolio with Volatility Risk Analysis.
Maintained by Sourish Das. Last updated 9 years ago.
1 stars 1.00 score 8 scriptscran
BHAI:Estimate the Burden of Healthcare-Associated Infections
Provides an approach which is based on the methodology of the Burden of Communicable Diseases in Europe (BCoDE) and can be used for large and small samples such as individual countries. The Burden of Healthcare-Associated Infections (BHAI) is estimated in disability-adjusted life years, number of infections as well as number of deaths per year. Results can be visualized with various plotting functions and exported into tables.
Maintained by Benedikt Zacher. Last updated 5 years ago.
1.00 scorecran
vampyr:Factor Analysis Controlling the Effects of Response Bias
Vampirize the response biases from a dataset! Performs factor analysis controlling the effects of social desirability and acquiescence using the method described in Ferrando, Lorenzo-Seva & Chico (2009) <doi:10.1080/10705510902751374>.
Maintained by David Navarro-Gonzalez. Last updated 4 years ago.
1.00 score
riko-k
bayest:Effect Size Targeted Bayesian Two-Sample t-Tests via Markov Chain Monte Carlo in Gaussian Mixture Models
Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.
Maintained by Riko Kelter. Last updated 12 months ago.
1.00 scorecran
DPtree:Dirichlet-Based Polya Tree
Contains functions to perform copula estimation by the non-parametric Bayesian method, Dirichlet-based Polya Tree. See Ning (2018) <doi:10.1080/00949655.2017.1421194>.
Maintained by Shaoyang Ning. Last updated 7 years ago.
1.00 score
ratkovic
sparsereg:Sparse Bayesian Models for Regression, Subgroup Analysis, and Panel Data
Sparse modeling provides a mean selecting a small number of non-zero effects from a large possible number of candidate effects. This package includes a suite of methods for sparse modeling: estimation via EM or MCMC, approximate confidence intervals with nominal coverage, and diagnostic and summary plots. The method can implement sparse linear regression and sparse probit regression. Beyond regression analyses, applications include subgroup analysis, particularly for conjoint experiments, and panel data. Future versions will include extensions to models with truncated outcomes, propensity score, and instrumental variable analysis.
Maintained by Marc Ratkovic. Last updated 9 years ago.
1.00 score 6 scriptscran
adaptsmoFMRI:Adaptive Smoothing of FMRI Data
Adaptive smoothing functions for estimating the blood oxygenation level dependent (BOLD) effect by using functional Magnetic Resonance Imaging (fMRI) data, based on adaptive Gauss Markov random fields, for real as well as simulated data. The implemented models make use of efficient Markov Chain Monte Carlo methods. Implemented methods are based on the research developed by A. Brezger, L. Fahrmeir, A. Hennerfeind (2007) <https://www.jstor.org/stable/4626770>.
Maintained by Maximilian Hughes. Last updated 3 years ago.
1.00 scorecran
StReg:Student's t Regression Models
It contains functions to estimate multivariate Student's t dynamic and static regression models for given degrees of freedom and lag length. Users can also specify the trends and dummies of any kind in matrix form. Poudyal, N., and Spanos, A. (2022) <doi:10.3390/econometrics10020017>. Spanos, A. (1994) <http://www.jstor.org/stable/3532870>.
Maintained by Niraj Poudyal. Last updated 2 years ago.
1.00 score
hyungsuktak
Rdta:Data Transforming Augmentation for Linear Mixed Models
We provide a toolbox to fit univariate and multivariate linear mixed models via data transforming augmentation. Users can also fit these models via typical data augmentation for a comparison. It returns either maximum likelihood estimates of unknown model parameters (hyper-parameters) via an EM algorithm or posterior samples of those parameters via MCMC. Also see Tak et al. (2019) <doi:10.1080/10618600.2019.1704295>.
Maintained by Hyungsuk Tak. Last updated 1 years ago.
1.00 scorecran
manet:Multiple Allocation Model for Actor-Event Networks
Mixture model with overlapping clusters for binary actor-event data. Parameters are estimated in a Bayesian framework. Model and inference are described in Ranciati, Vinciotti, Wit (2017) Modelling actor-event network data via a mixture model under overlapping clusters. Submitted.
Maintained by Veronica Vinciotti. Last updated 7 years ago.
1.00 scorecran
prolsirm:Procrustes Matching for Latent Space Item Response Model
Procrustes matching of the posterior samples of person and item latent positions from latent space item response models. The methods implemented in this package are based on work by Borg, I., Groenen, P. (1997, ISBN:978-0-387-94845-4), Jeon, M., Jin, I. H., Schweinberger, M., Baugh, S. (2021) <doi:10.1007/s11336-021-09762-5>, and Andrew, D. M., Kevin M. Q., Jong Hee Park. (2011) <doi:10.18637/jss.v042.i09>.
Maintained by Jinwen Luo. Last updated 2 years ago.
1.00 score
kapelner
optDesignSlopeInt:Optimal Designs for Estimating the Slope Divided by the Intercept
Aids practitioners to optimally design experiments that measure the slope divided by the intercept and provides confidence intervals for the ratio.
Maintained by Adam Kapelner. Last updated 2 years ago.
1.00 score 2 scripts
boyanglu
NMADiagT:Network Meta-Analysis of Multiple Diagnostic Tests
Implements HSROC (hierarchical summary receiver operating characteristic) model developed by Ma, Lian, Chu, Ibrahim, and Chen (2018) <doi:10.1093/biostatistics/kxx025> and hierarchical model developed by Lian, Hodges, and Chu (2019) <doi:10.1080/01621459.2018.1476239> for performing meta-analysis for 1-5 diagnostic tests to simultaneously compare multiple tests within a missing data framework. This package evaluates the accuracy of multiple diagnostic tests and also gives graphical representation of the results.
Maintained by Boyang Lu. Last updated 5 years ago.
1.00 score 1 scripts
cran
PICBayes:Bayesian Models for Partly Interval-Censored Data
Contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.
Maintained by Chun Pan. Last updated 4 years ago.
1.00 score
rikoke
bayesanova:Bayesian Inference in the Analysis of Variance via Markov Chain Monte Carlo in Gaussian Mixture Models
Provides a Bayesian version of the analysis of variance based on a three-component Gaussian mixture for which a Gibbs sampler produces posterior draws. For details about the Bayesian ANOVA based on Gaussian mixtures, see Kelter (2019) <arXiv:1906.07524>.
Maintained by Riko Kelter. Last updated 1 years ago.
1.00 score 5 scripts
cran
bacr:Bayesian Adjustment for Confounding
Estimating the average causal effect based on the Bayesian Adjustment for Confounding (BAC) algorithm.
Maintained by Chi Wang. Last updated 8 years ago.
1 stars 1.00 score
sam-data-guy-iam
animalEKF:Extended Kalman Filters for Animal Movement
Synthetic generation of 1-D and 2-D correlated random walks (CRWs) for animal movement with behavioral switching, and particle filter estimation of movement parameters from observed trajectories using Extended Kalman Filter (EKF) model. See Ackerman (2018) <https://digital.library.temple.edu/digital/collection/p245801coll10/id/499150>.
Maintained by Samuel Ackerman. Last updated 1 years ago.
1 stars 1.00 scorecran
robustsae:Robust Bayesian Small Area Estimation
Functions for Robust Bayesian Small Area Estimation.
Maintained by Jiyoun Myung. Last updated 8 years ago.
1.00 score