R-universe search: dlm

Showing 19 of total 19 results (show query)

gpetris

dlm:Bayesian and Likelihood Analysis of Dynamic Linear Models

Provides routines for Maximum likelihood, Kalman filtering and smoothing, and Bayesian analysis of Normal linear State Space models, also known as Dynamic Linear Models.

Maintained by Giovanni Petris. Last updated 6 months ago.

openblas

87.1 match 9 stars 7.72 score 470 scripts 11 dependents

atlas-aai

ratlas:ATLAS Formatting Functions and Templates

Provides templates, formatting tools, and 'ggplot2' themes tailored for the Accessible Teaching, Learning, and Assessment Systems (ATLAS) organization. These templates facilitate the creation of topic guides and technical reports, while the formatting functions enable users to customize numbers and tables to meet specific requirements. Additionally, the themes ensure a uniform visual style across graphics.

Maintained by W. Jake Thompson. Last updated 5 months ago.

bookdown ggplot2 ggplot2-themes rmarkdown rmarkdown-template

6.0 match 29 stars 7.28 score 20 scripts

paws-r

paws:Amazon Web Services Software Development Kit

Interface to Amazon Web Services <https://aws.amazon.com>, including storage, database, and compute services, such as 'Simple Storage Service' ('S3'), 'DynamoDB' 'NoSQL' database, and 'Lambda' functions-as-a-service.

Maintained by Dyfan Jones. Last updated 10 hours ago.

aws aws-sdk

3.3 match 332 stars 11.16 score 177 scripts 11 dependents

schw4b

DGM:Dynamic Graphical Models

Dynamic graphical models for multivariate time series data to estimate directed dynamic networks in functional magnetic resonance imaging (fMRI), see Schwab et al. (2017) <doi:10.1016/j.neuroimage.2018.03.074>.

Maintained by Simon Schwab. Last updated 3 years ago.

dynamic-graphical-models functional-connectivity time-varying-connectivity openblas cpp openmp

6.4 match 25 stars 5.49 score 25 scripts

atsa-es

mvdlm:Multivariate Dynamic Linear Modelling With Stan

Fits multivariate dynamic linear models in a Bayesian framework using Stan.

Maintained by Eric J. Ward. Last updated 10 months ago.

bayesian dlm stan time-series cpp

11.0 match 1 stars 3.18 score 3 scripts

paws-r

paws.storage:'Amazon Web Services' Storage Services

Interface to 'Amazon Web Services' storage services, including 'Simple Storage Service' ('S3') and more <https://aws.amazon.com/>.

Maintained by Dyfan Jones. Last updated 10 hours ago.

aws aws-sdk

3.3 match 332 stars 9.66 score 17 scripts 17 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 2 months ago.

fortran cpp openmp

5.0 match 21 stars 5.40 score 17 scripts

cran

BayesMortalityPlus:Bayesian Mortality Modelling

Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in Heligman, L., & Pollard, J. H. (1980) <doi:10.1017/S0020268100040257> and Dellaportas, Petros, et al. (2001) <doi:10.1111/1467-985X.00202>, and dynamic linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009) <doi:10.1007/b135794_2>). While Heligman-Pollard has parameters with a straightforward interpretation yielding some rich analysis, the dynamic linear model provides a very flexible adjustment of the mortality curves by controlling the discount factor value. Closing methods for both Heligman-Pollard and dynamic linear model were also implemented according to Dodd, Erengul, et al. (2018) <https://www.jstor.org/stable/48547511>. The Bayesian Lee-Carter model is also implemented to fit historical mortality tables time series to predict the mortality in the following years and to do improvement analysis, as seen in Lee, R. D., & Carter, L. R. (1992) <doi:10.1080/01621459.1992.10475265> and Pedroza, C. (2006) <doi:10.1093/biostatistics/kxj024>.

Maintained by Luiz Fernando Figueiredo. Last updated 10 months ago.

19.0 match 1.00 score

cszang

treeclim:Numerical Calibration of Proxy-Climate Relationships

Bootstrapped response and correlation functions, seasonal correlations and evaluation of reconstruction skills for use in dendroclimatology and dendroecology, see Zang and Biondi (2015) <doi:10.1111/ecog.01335>.

Maintained by Christian Zang. Last updated 4 months ago.

climate-relationships dendroclimatology dendroecology openblas cpp

3.3 match 18 stars 5.66 score 36 scripts

bioc

ASSET:An R package for subset-based association analysis of heterogeneous traits and subtypes

An R package for subset-based analysis of heterogeneous traits and disease subtypes. The package allows the user to search through all possible subsets of z-scores to identify the subset of traits giving the best meta-analyzed z-score. Further, it returns a p-value adjusting for the multiple-testing involved in the search. It also allows for searching for the best combination of disease subtypes associated with each variant.

Maintained by Samsiddhi Bhattacharjee. Last updated 5 months ago.

statisticalmethod snp genomewideassociation multiplecomparison

2.3 match 5.71 score 85 scripts 1 dependents

blue-matter

MSEtool:Management Strategy Evaluation Toolkit

Development, simulation testing, and implementation of management procedures for fisheries (see Carruthers & Hordyk (2018) <doi:10.1111/2041-210X.13081>).

Maintained by Adrian Hordyk. Last updated 7 days ago.

cpp

1.6 match 8 stars 7.71 score 163 scripts 3 dependents

silvaneojunior

kDGLM:Bayesian Analysis of Dynamic Generalized Linear Models

Provide routines for filtering and smoothing, forecasting, sampling and Bayesian analysis of Dynamic Generalized Linear Models using the methodology described in Alves et al. (2024)<doi:10.48550/arXiv.2201.05387> and dos Santos Jr. et al. (2024)<doi:10.48550/arXiv.2403.13069>.

Maintained by Silvaneo dos Santos Jr.. Last updated 14 days ago.

1.9 match 2 stars 5.70 score 9 scripts

haydarde

dLagM:Time Series Regression Models with Distributed Lag Models

Provides time series regression models with one predictor using finite distributed lag models, polynomial (Almon) distributed lag models, geometric distributed lag models with Koyck transformation, and autoregressive distributed lag models. It also consists of functions for computation of h-step ahead forecasts from these models. See Demirhan (2020)(<doi:10.1371/journal.pone.0228812>) and Baltagi (2011)(<doi:10.1007/978-3-642-20059-5>) for more information.

Maintained by Haydar Demirhan. Last updated 2 years ago.

3.3 match 2 stars 3.18 score 127 scripts

cran

eDMA:Dynamic Model Averaging with Grid Search

Perform dynamic model averaging with grid search as in Dangl and Halling (2012) <doi:10.1016/j.jfineco.2012.04.003> using parallel computing.

Maintained by Leopoldo Catania. Last updated 7 years ago.

openblas cpp openmp

5.2 match 1.70 score

atsa-es

atsar:Stan Routines For Univariate And Multivariate Time Series

Bundles univariate and multivariate STAN scripts for FISH 507 class.

Maintained by Eric J. Ward. Last updated 10 months ago.

bayesian stan time-series cpp

1.3 match 48 stars 5.68 score 33 scripts

bking124

countSTAR:Flexible Modeling of Count Data

For Bayesian and classical inference and prediction with count-valued data, Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable, and easy-to-use approach. STAR models the observed count data using a rounded continuous data model and incorporates a transformation for greater flexibility. Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of (i) transforming count-valued data and subsequently (ii) modeling the transformed data using Gaussian models. STAR is well-defined for count-valued data, which is reflected in predictive accuracy, and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion. Importantly, STAR is easy to combine with existing MCMC or point estimation methods for continuous data, which allows seamless adaptation of continuous data models (such as linear regressions, additive models, BART, random forests, and gradient boosting machines) for count-valued data. The package also includes several methods for modeling count time series data, namely via warped Dynamic Linear Models. For more details and background on these methodologies, see the works of Kowal and Canale (2020) <doi:10.1214/20-EJS1707>, Kowal and Wu (2022) <doi:10.1111/biom.13617>, King and Kowal (2023) <doi:10.1214/23-BA1394>, and Kowal and Wu (2023) <arXiv:2110.12316>.

Maintained by Brian King. Last updated 2 years ago.

cpp

1.7 match 3 stars 4.18 score 3 scripts

dandempsey

DiscreteDLM:Bayesian Distributed Lag Model Fitting for Binary and Count Response Data

Tools for fitting Bayesian Distributed Lag Models (DLMs) to longitudinal response data that is a count or binary. Count data is fit using negative binomial regression and binary is fit using quantile regression. The contribution of the lags are fit via b-splines. In addition, infers the predictor inclusion uncertainty. Multimomial models are not supported. Based on Dempsey and Wyse (2025) <doi:10.48550/arXiv.2403.03646>.

Maintained by Daniel Dempsey. Last updated 2 months ago.

1.9 match 3.60 score

atsa-es

MARSS:Multivariate Autoregressive State-Space Modeling

The MARSS package provides maximum-likelihood parameter estimation for constrained and unconstrained linear multivariate autoregressive state-space (MARSS) models, including partially deterministic models. MARSS models are a class of dynamic linear model (DLM) and vector autoregressive model (VAR) model. Fitting available via Expectation-Maximization (EM), BFGS (using optim), and 'TMB' (using the 'marssTMB' companion package). Functions are provided for parametric and innovations bootstrapping, Kalman filtering and smoothing, model selection criteria including bootstrap AICb, confidences intervals via the Hessian approximation or bootstrapping, and all conditional residual types. See the user guide for examples of dynamic factor analysis, dynamic linear models, outlier and shock detection, and multivariate AR-p models. Online workshops (lectures, eBook, and computer labs) at <https://atsa-es.github.io/>.

Maintained by Elizabeth Eli Holmes. Last updated 1 years ago.

multivariate-timeseries state-space-models statistics time-series

0.5 match 52 stars 10.34 score 596 scripts 3 dependents

rabarata

exdqlm:Extended Dynamic Quantile Linear Models

Routines for Bayesian estimation and analysis of dynamic quantile linear models utilizing the extended asymmetric Laplace error distribution, also known as extended dynamic quantile linear models (exDQLM) described in Barata et al (2020) <doi:10.1214/21-AOAS1497>.

Maintained by Raquel Barata. Last updated 2 years ago.

1.8 match 1 stars 1.00 score