Showing 15 of total 15 results (show query)
glmmtmb
glmmTMB:Generalized Linear Mixed Models using Template Model Builder
Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.
Maintained by Mollie Brooks. Last updated 4 hours ago.
314 stars 16.83 score 3.7k scripts 24 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-inferencebrmsmultilevel-modelsstanstatistical-models
1.3k stars 16.64 score 13k scripts 35 dependents
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 18 days ago.
566 stars 12.53 score 396 scripts 6 dependents
pbs-assess
sdmTMB:Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'
Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2024) <doi:10.1101/2022.03.24.485545>.
Maintained by Sean C. Anderson. Last updated 16 hours ago.
ecologyglmmspatial-analysisspecies-distribution-modellingtmbcpp
205 stars 11.04 score 848 scripts 1 dependents
nicholasjclark
mvgam:Multivariate (Dynamic) Generalized Additive Models
Fit Bayesian Dynamic Generalized Additive Models to multivariate observations. Users can build nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software 'Stan'. References: Clark & Wells (2023) <doi:10.1111/2041-210X.13974>.
Maintained by Nicholas J Clark. Last updated 10 hours ago.
bayesian-statisticsdynamic-factor-modelsecological-modellingforecastinggaussian-processgeneralised-additive-modelsgeneralized-additive-modelsjoint-species-distribution-modellingmultilevel-modelsmultivariate-timeseriesstantime-series-analysistimeseriesvector-autoregressionvectorautoregressioncpp
148 stars 9.92 score 117 scripts
azure
azuremlsdk:Interface to the 'Azure Machine Learning' 'SDK'
Interface to the 'Azure Machine Learning' Software Development Kit ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and manage machine learning models on the 'Azure Machine Learning' service. To learn more about 'Azure Machine Learning' visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.
Maintained by Diondra Peck. Last updated 3 years ago.
amlcomputeazureazure-machine-learningazuremldsimachine-learningrstudiosdk-r
105 stars 8.91 score 221 scripts
wwiecek
baggr:Bayesian Aggregate Treatment Effects
Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) <doi:10.1257/app.20170299>.
Maintained by Witold Wiecek. Last updated 5 days ago.
bayesian-statisticsmeta-analysisquantile-regressionstantreatment-effectscpp
49 stars 7.24 score 88 scripts
flyaflya
causact:Fast, Easy, and Visual Bayesian Inference
Accelerate Bayesian analytics workflows in 'R' through interactive modelling, visualization, and inference. Define probabilistic graphical models using directed acyclic graphs (DAGs) as a unifying language for business stakeholders, statisticians, and programmers. This package relies on interfacing with the 'numpyro' python package.
Maintained by Adam Fleischhacker. Last updated 2 months ago.
bayesian-inferencedagsposterior-probabilityprobabilistic-graphical-modelsprobabilistic-programming
45 stars 6.97 score 52 scripts
vast-lib
tinyVAST:Multivariate Spatio-Temporal Models using Structural Equations
Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the 'mgcv' package to specify splines via the formula interface. See Thorson et al. (2024) <doi:10.48550/arXiv.2401.10193> for more details.
Maintained by James T. Thorson. Last updated 8 days ago.
vector-autoregressive-spatio-temporal-modelcpp
14 stars 6.83 score
seananderson
glmmfields:Generalized Linear Mixed Models with Robust Random Fields for Spatiotemporal Modeling
Implements Bayesian spatial and spatiotemporal models that optionally allow for extreme spatial deviations through time. 'glmmfields' uses a predictive process approach with random fields implemented through a multivariate-t distribution instead of the usual multivariate normal. Sampling is conducted with 'Stan'. References: Anderson and Ward (2019) <doi:10.1002/ecy.2403>.
Maintained by Sean C. Anderson. Last updated 1 years ago.
ecologyextremesspatial-analysisspatiotemporalcpp
50 stars 6.74 score 55 scripts
epinowcast
epidist:Estimate Epidemiological Delay Distributions With brms
Understanding and accurately estimating epidemiological delay distributions is important for public health policy. These estimates influence epidemic situational awareness, control strategies, and resource allocation. This package provides methods to address the key challenges in estimating these distributions, including truncation, interval censoring, and dynamical biases. These issues are frequently overlooked, resulting in biased conclusions. Built on top of 'brms', it allows for flexible modelling including time-varying spatial components and partially pooled estimates of demographic characteristics.
Maintained by Sam Abbott. Last updated 22 days ago.
14 stars 6.52 score 7 scripts
noaa-fims
FIMS:The Fisheries Integrated Modeling System
The Fisheries Integrated Modeling System is a next-generation framework of stock assessment models, assisting fishery managers with the goal of achieving sustainable fisheries. This system, when completed in a few years, offers the NOAA Fisheries and global fisheries science communities an advanced set of stock assessment models. These tools can be used separately or in combination to incorporate ecosystem and socioeconomic data and models, as well as climate effects and other drivers within the marine environment, into stock assessment models.
Maintained by Kelli F. Johnson. Last updated 2 months ago.
23 stars 5.67 score 24 scripts
andrea-havron
clustTMB:Spatio-Temporal Finite Mixture Model using 'TMB'
Fits a spatio-temporal finite mixture model using 'TMB'. Covariate, spatial and temporal random effects can be incorporated into the gating formula using multinomial logistic regression, the expert formula using a generalized linear mixed model framework, or both.
Maintained by Andrea M. Havron. Last updated 6 months ago.
4 stars 5.38 score 9 scripts
onofriandreapg
drcte:Statistical Approaches for Time-to-Event Data in Agriculture
A specific and comprehensive framework for the analyses of time-to-event data in agriculture. Fit non-parametric and parametric time-to-event models. Compare time-to-event curves for different experimental groups. Plots and other displays. It is particularly tailored to the analyses of data from germination and emergence assays. The methods are described in Onofri et al. (2022) "A unified framework for the analysis of germination, emergence, and other time-to-event data in weed science", Weed Science, 70, 259-271 <doi:10.1017/wsc.2022.8>.
Maintained by Andrea Onofri. Last updated 5 days ago.
non-linear-regressionseed-germinationtime-to-event
4.07 score 39 scripts 2 dependents