atsa-es r-universe repositoryhttps://atsa-es.r-universe.devPackage updated in atsa-escranlike-server 0.17.1https://github.com/atsa-es.png?size=400atsa-es r-universe repositoryhttps://atsa-es.r-universe.devMon, 26 Feb 2024 18:52:13 GMT[atsa-es] bayesdfa 1.3.3eric.ward@noaa.gov (Eric J. Ward)Implements Bayesian dynamic factor analysis with 'Stan'.
Dynamic factor analysis is a dimension reduction tool for
multivariate time series. 'bayesdfa' extends conventional
dynamic factor models in several ways. First, extreme events
may be estimated in the latent trend by modeling process error
with a student-t distribution. Second, alternative constraints
(including proportions are allowed). Third, the estimated
dynamic factors can be analyzed with hidden Markov models to
evaluate support for latent regimes.https://github.com/r-universe/atsa-es/actions/runs/8448248895Mon, 26 Feb 2024 18:52:13 GMTbayesdfa1.3.3successhttps://atsa-es.r-universe.devhttps://github.com/fate-ewi/bayesdfaa2_combining_data.Rmda2_combining_data.htmlCombining data with bayesdfa2023-04-20 07:29:262023-04-24 18:12:27a5_estimate_process_sigma.Rmda5_estimate_process_sigma.htmlEstimating process trend variability with bayesdfa2023-04-20 07:29:262023-04-20 07:29:26a7_bigdata.Rmda7_bigdata.htmlExamples of fitting DFA models with lots of data2023-04-20 07:29:262023-04-20 07:29:26a4_smooth.Rmda4_smooth.htmlExamples of fitting smooth trend DFA models2023-04-20 07:29:262023-04-20 07:29:26a3_covariates.Rmda3_covariates.htmlExamples of including covariates with bayesdfa2023-04-20 07:29:262023-04-20 07:29:26a6_compositional.Rmda6_compositional.htmlFitting compositional dynamic factor models with bayesdfa2023-04-20 07:29:262023-04-20 07:29:26a1_bayesdfa.Rmda1_bayesdfa.htmlOverview of the bayesdfa package2023-04-20 07:29:262023-12-07 17:21:10[atsa-es] MARSS 3.11.9eli.holmes@noaa.gov (Elizabeth Eli Holmes)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/>.https://github.com/r-universe/atsa-es/actions/runs/8355049134Mon, 19 Feb 2024 06:52:59 GMTMARSS3.11.9successhttps://atsa-es.r-universe.devhttps://github.com/atsa-es/MARSSEMDerivation.RnwEMDerivation.pdfEM_Derivation2017-03-28 20:26:032023-05-20 03:08:58Learning_MARSS.RmdLearning_MARSS.htmlLearning MARSS2023-05-17 01:18:532023-05-20 06:09:26Quick_Start.RmdQuick_Start.htmlQuick Start Guide2023-05-16 05:48:142023-05-20 03:08:58Residuals.RnwResiduals.pdfResiduals2017-03-28 20:57:252023-05-20 03:08:58UserGuide.RnwUserGuide.pdfUser Guide2017-03-28 20:26:032023-05-20 03:08:58[atsa-es] atsar 0.1.6eric.ward@noaa.gov (Eric J. Ward)Bundles univariate and multivariate STAN scripts for FISH
507 class.https://github.com/r-universe/atsa-es/actions/runs/8228678501Tue, 05 Sep 2023 18:12:24 GMTatsar0.1.6successhttps://atsa-es.r-universe.devhttps://github.com/atsa-es/atsardlm.Rmddlm.htmlDynamic linear models in package atsar2017-03-07 19:11:352021-02-02 15:39:04fit_stan.Rmdfit_stan.htmlFitting time series models in package atsar2017-03-07 19:11:352021-02-02 14:14:30[atsa-es] maxnet 0.1.4mrmaxent@gmail.com (Steven Phillips)Procedures to fit species distributions models from
occurrence records and environmental variables, using 'glmnet'
for model fitting. Model structure is the same as for the
'Maxent' Java package, version 3.4.0, with the same feature
types and regularization options. See the 'Maxent' website
<http://biodiversityinformatics.amnh.org/open_source/maxent>
for more details.https://github.com/r-universe/atsa-es/actions/runs/8586659523Wed, 05 Jul 2023 14:27:13 GMTmaxnet0.1.4successhttps://atsa-es.r-universe.devhttps://github.com/BigelowLab/maxnet[atsa-es] MAR1 2.3eli.holmes@noaa.gov (Elizabeth Eli Holmes)The MAR1 package provides basic tools for preparing
ecological community time-series data for MAR modeling,
building MAR-1 models via model selection and bootstrapping,
and visualizing and exporting model results. It is intended to
make MAR analysis sensu Ives et al. (2003) Analysis of
community stability and ecological interactions from
time-series data) a more accessible tool for anyone studying
community dynamics. The user need not necessarily be familiar
with time-series modeling or command-based statistics programs
such as R.https://github.com/r-universe/atsa-es/actions/runs/8488626754Wed, 07 Jun 2023 00:47:37 GMTMAR12.3successhttps://atsa-es.r-universe.devhttps://github.com/atsa-es/MAR1Getting_Started.RmdGetting_Started.htmlGetting_Started2023-05-25 22:18:422023-05-26 16:04:52MAR1-State-Space.RmdMAR1-State-Space.htmlMAR1-State-Space2023-05-26 16:04:522023-06-01 00:59:34[atsa-es] marssTMB 0.0.14eli.holmes@noaa.gov (Elizabeth E. Holmes)Companion to the MARSS package. Fast fitting of MARSS
models with TMB. See the MARSS documentation. All the model
syntax and features are the same as for the MARSS package.https://github.com/r-universe/atsa-es/actions/runs/8566064065Tue, 23 May 2023 14:53:17 GMTmarssTMB0.0.14successhttps://atsa-es.r-universe.devhttps://github.com/atsa-es/marssTMBdfaTMB.RmddfaTMB.htmldfaTMB: Dynamic Factor Analysis2023-05-01 16:35:422023-05-11 04:41:43MARSS_TMB.RmdMARSS_TMB.htmlDynamic Factor Analysis2023-05-01 16:35:422023-05-11 04:41:14Optimization.RmdOptimization.htmlOptimization discussion2023-05-04 16:16:142023-05-07 07:06:21Quick_Start.RmdQuick_Start.htmlQuick Start2023-05-04 06:21:152023-05-11 04:41:22Comparisons.RmdComparisons.htmlSpeed Comparisons2023-05-11 04:40:472023-05-20 14:02:29[atsa-es] atsalibrary 1.5eli.holmes@noaa.gov (Elizabeth E. Holmes)This package will load the needed packages and data files
for the ATSA course material when students install from GitHub.https://github.com/r-universe/atsa-es/actions/runs/8259893778Wed, 03 May 2023 17:58:00 GMTatsalibrary1.5successhttps://atsa-es.r-universe.devhttps://github.com/atsa-es/atsalibrary[atsa-es] mvdlm 0.1.0eric.ward@noaa.gov (Eric J. Ward)Fits multivariate dynamic linear models in a Bayesian
framework using Stan.https://github.com/r-universe/atsa-es/actions/runs/8228402900Tue, 08 Nov 2022 23:29:30 GMTmvdlm0.1.0successhttps://atsa-es.r-universe.devhttps://github.com/atsa-es/mvdlma01_overview.Rmda01_overview.htmlOverview of mvdlm package2022-09-19 22:50:372022-11-08 23:18:51[atsa-es] varlasso 0.0.1eric.ward@noaa.gov (Eric Ward)The varlasso package uses Stan (mc-stan.org) to fit VAR
state space models with optional shrinkage priors on B matrix
elements (autoregression coefficients).https://github.com/r-universe/atsa-es/actions/runs/8566504705Thu, 29 Sep 2022 23:33:36 GMTvarlasso0.0.1successhttps://atsa-es.r-universe.devhttps://github.com/atsa-es/varlassoa1_examples.Rmda1_examples.htmlExamples using the varlasso package2022-09-29 23:13:412022-09-29 23:33:36[atsa-es] tvvarss 0.1.1eric.ward@noaa.gov (Eric Ward)The tvvarss package uses Stan (mc-stan.org) to fit
multi-site multivariate autoregressive (aka vector
autoregressive) state space models with a time varying
interaction matrix.https://github.com/r-universe/atsa-es/actions/runs/8228402906Mon, 04 Oct 2021 16:48:14 GMTtvvarss0.1.1successhttps://atsa-es.r-universe.devhttps://github.com/atsa-es/tvvarssintro_tvvarss.Rmdintro_tvvarss.htmlIntro to tvvarss2021-02-25 06:21:242021-02-25 06:21:24