R-universe search: autoregressive

bsvars

bsvars:Bayesian Estimation of Structural Vector Autoregressive Models

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

Maintained by Tomasz Woźniak. Last updated 1 months ago.

bayesian-inference econometrics vector-autoregression openblas cpp openmp

57.1 match 46 stars 7.67 score 32 scripts 1 dependents

matthieustigler

tsDyn:Nonlinear Time Series Models with Regime Switching

Implements nonlinear autoregressive (AR) time series models. For univariate series, a non-parametric approach is available through additive nonlinear AR. Parametric modeling and testing for regime switching dynamics is available when the transition is either direct (TAR: threshold AR) or smooth (STAR: smooth transition AR, LSTAR). For multivariate series, one can estimate a range of TVAR or threshold cointegration TVECM models with two or three regimes. Tests can be conducted for TVAR as well as for TVECM (Hansen and Seo 2002 and Seo 2006).

Maintained by Matthieu Stigler. Last updated 5 months ago.

18.0 match 34 stars 10.56 score 684 scripts 3 dependents

nk027

BVAR:Hierarchical Bayesian Vector Autoregression

Estimation of hierarchical Bayesian vector autoregressive models following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>. Functions to compute and identify impulse responses, calculate forecasts, forecast error variance decompositions and scenarios are available. Several methods to print, plot and summarise results facilitate analysis.

Maintained by Nikolas Kuschnig. Last updated 4 months ago.

bayesian bvar forecasts impulse-responses vector-autoregressions

19.4 match 51 stars 7.30 score 68 scripts 1 dependents

roga11

MSTest:Hypothesis Testing for Markov Switching Models

Implementation of hypothesis testing procedures described in Hansen (1992) <doi:10.1002/jae.3950070506>, Carrasco, Hu, & Ploberger (2014) <doi:10.3982/ECTA8609>, Dufour & Luger (2017) <doi:10.1080/07474938.2017.1307548>, and Rodriguez Rondon & Dufour (2024) <https://grodriguezrondon.com/files/RodriguezRondon_Dufour_2024_MonteCarlo_LikelihoodRatioTest_MarkovSwitchingModels_20241015.pdf> that can be used to identify the number of regimes in Markov switching models.

Maintained by Gabriel Rodriguez Rondon. Last updated 20 days ago.

autoregressive bootstrap hypothesis-testing likelihood-ratio-test markov-chain moments monte-carlo non-linear regime-switching time-series openblas cpp openmp

32.9 match 5 stars 4.18 score 3 scripts

r-spatial

spatialreg:Spatial Regression Analysis

A collection of all the estimation functions for spatial cross-sectional models (on lattice/areal data using spatial weights matrices) contained up to now in 'spdep'. These model fitting functions include maximum likelihood methods for cross-sectional models proposed by 'Cliff' and 'Ord' (1973, ISBN:0850860369) and (1981, ISBN:0850860814), fitting methods initially described by 'Ord' (1975) <doi:10.1080/01621459.1975.10480272>. The models are further described by 'Anselin' (1988) <doi:10.1007/978-94-015-7799-1>. Spatial two stage least squares and spatial general method of moment models initially proposed by 'Kelejian' and 'Prucha' (1998) <doi:10.1023/A:1007707430416> and (1999) <doi:10.1111/1468-2354.00027> are provided. Impact methods and MCMC fitting methods proposed by 'LeSage' and 'Pace' (2009) <doi:10.1201/9781420064254> are implemented for the family of cross-sectional spatial regression models. Methods for fitting the log determinant term in maximum likelihood and MCMC fitting are compared by 'Bivand et al.' (2013) <doi:10.1111/gean.12008>, and model fitting methods by 'Bivand' and 'Piras' (2015) <doi:10.18637/jss.v063.i18>; both of these articles include extensive lists of references. A recent review is provided by 'Bivand', 'Millo' and 'Piras' (2021) <doi:10.3390/math9111276>. 'spatialreg' >= 1.1-* corresponded to 'spdep' >= 1.1-1, in which the model fitting functions were deprecated and passed through to 'spatialreg', but masked those in 'spatialreg'. From versions 1.2-*, the functions have been made defunct in 'spdep'. From version 1.3-6, add Anselin-Kelejian (1997) test to `stsls` for residual spatial autocorrelation <doi:10.1177/016001769702000109>.

Maintained by Roger Bivand. Last updated 4 days ago.

bayesian impacts maximum-likelihood spatial-dependence spatial-econometrics spatial-regression openblas

10.3 match 46 stars 12.92 score 916 scripts 24 dependents

ykang

gratis:Generating Time Series with Diverse and Controllable Characteristics

Generates synthetic time series based on various univariate time series models including MAR and ARIMA processes. Kang, Y., Hyndman, R.J., Li, F.(2020) <doi:10.1002/sam.11461>.

Maintained by Feng Li. Last updated 11 months ago.

data-generation mixture-autoregressive statistical-computing time-series

17.8 match 76 stars 6.98 score 25 scripts

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 1 days ago.

bayesian-statistics dynamic-factor-models ecological-modelling forecasting gaussian-process generalised-additive-models generalized-additive-models joint-species-distribution-modelling multilevel-models multivariate-timeseries stan time-series-analysis timeseries vector-autoregression vectorautoregression cpp

12.1 match 139 stars 9.85 score 117 scripts

paul-buerkner

brms:Bayesian Regression Models using 'Stan'

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

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

bayesian-inference brms multilevel-models stan statistical-models

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

franzmohr

bvartools:Bayesian Inference of Vector Autoregressive and Error Correction Models

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

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

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

16.4 match 31 stars 6.80 score 34 scripts 1 dependents

ygeunkim

bvhar:Bayesian Vector Heterogeneous Autoregressive Modeling

Tools to model and forecast multivariate time series including Bayesian Vector heterogeneous autoregressive (VHAR) model by Kim & Baek (2023) (<doi:10.1080/00949655.2023.2281644>). 'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.

Maintained by Young Geun Kim. Last updated 17 days ago.

bayesian bayesian-econometrics bvar eigen forecasting har pybind11 python rcppeigen time-series vector-autoregression cpp openmp

16.7 match 6 stars 6.42 score 25 scripts

bsvars

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

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

Maintained by Xiaolei Wang. Last updated 2 months ago.

bayesian-inference econometrics vector-autoregression openblas cpp openmp

16.0 match 13 stars 6.21 score 10 scripts

rstudio

tfprobability:Interface to 'TensorFlow Probability'

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

Maintained by Tomasz Kalinowski. Last updated 3 years ago.

11.4 match 54 stars 8.63 score 221 scripts 3 dependents

tyee001

VGAM:Vector Generalized Linear and Additive Models

An implementation of about 6 major classes of statistical regression models. The central algorithm is Fisher scoring and iterative reweighted least squares. At the heart of this package are the vector generalized linear and additive model (VGLM/VGAM) classes. VGLMs can be loosely thought of as multivariate GLMs. VGAMs are data-driven VGLMs that use smoothing. The book "Vector Generalized Linear and Additive Models: With an Implementation in R" (Yee, 2015) <DOI:10.1007/978-1-4939-2818-7> gives details of the statistical framework and the package. Currently only fixed-effects models are implemented. Many (100+) models and distributions are estimated by maximum likelihood estimation (MLE) or penalized MLE. The other classes are RR-VGLMs (reduced-rank VGLMs), quadratic RR-VGLMs, doubly constrained RR-VGLMs, quadratic RR-VGLMs, reduced-rank VGAMs, RCIMs (row-column interaction models)---these classes perform constrained and unconstrained quadratic ordination (CQO/UQO) models in ecology, as well as constrained additive ordination (CAO). Hauck-Donner effect detection is implemented. Note that these functions are subject to change; see the NEWS and ChangeLog files for latest changes.

Maintained by Thomas Yee. Last updated 1 months ago.

fortran

8.8 match 10 stars 10.67 score 3.6k scripts 169 dependents

icasas

tvReg:Time-Varying Coefficient for Single and Multi-Equation Regressions

Fitting time-varying coefficient models for single and multi-equation regressions, using kernel smoothing techniques.

Maintained by Isabel Casas. Last updated 2 years ago.

autoregressive nonparametric regression sure vectorautoregressive

14.9 match 19 stars 6.25 score 62 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.

assymetric heteroskedasticity mixture-autoregressive student-t time-series

34.5 match 1 stars 2.70 score 6 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 1 days ago.

vector-autoregressive-spatio-temporal-model cpp

13.3 match 13 stars 6.80 score

gjmvanboxtel

gsignal:Signal Processing

R implementation of the 'Octave' package 'signal', containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

Maintained by Geert van Boxtel. Last updated 2 months ago.

signal-processing signals cpp

8.9 match 24 stars 10.03 score 133 scripts 34 dependents

aniebee

ClusterVAR:Fitting Latent Class Vector-Autoregressive (VAR) Models

Estimates latent class vector-autoregressive models via EM algorithm on time-series data for model-based clustering and classification. Includes model selection criteria for selecting the number of lags and clusters.

Maintained by Anja Ernst. Last updated 2 months ago.

clustering latent-class-model multivariate-timeseries time-series-analysis vector-autoregression vector-autoregression-models

17.5 match 2 stars 4.88 score 1 scripts

smac-group

simts:Time Series Analysis Tools

A system contains easy-to-use tools as a support for time series analysis courses. In particular, it incorporates a technique called Generalized Method of Wavelet Moments (GMWM) as well as its robust implementation for fast and robust parameter estimation of time series models which is described, for example, in Guerrier et al. (2013) <doi: 10.1080/01621459.2013.799920>. More details can also be found in the paper linked to via the URL below.

Maintained by Stéphane Guerrier. Last updated 2 years ago.

rcpp rcpparmadillo simulation time-series timeseries timeseries-data openblas cpp

10.4 match 15 stars 7.68 score 59 scripts 4 dependents

haeran-cho

fnets:Factor-Adjusted Network Estimation and Forecasting for High-Dimensional Time Series

Implements methods for network estimation and forecasting of high-dimensional time series exhibiting strong serial and cross-sectional correlations under a factor-adjusted vector autoregressive model. See Barigozzi, Cho and Owens (2024) <doi:10.1080/07350015.2023.2257270> for further descriptions of FNETS methodology and Owens, Cho and Barigozzi (2024) <arXiv:2301.11675> accompanying the R package.

Maintained by Haeran Cho. Last updated 4 months ago.

factor-models forecasting high-dimensional network-estimation time-series vector-autoregression cpp

14.8 match 7 stars 5.33 score 28 scripts

bayesiandemography

bage:Bayesian Estimation and Forecasting of Age-Specific Rates

Fast Bayesian estimation and forecasting of age-specific rates, probabilities, and means, based on 'Template Model Builder'.

Maintained by John Bryant. Last updated 2 months ago.

cpp

10.5 match 3 stars 7.30 score 39 scripts

mboeck11

BGVAR:Bayesian Global Vector Autoregressions

Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 <doi:10.18637/jss.v104.i09>.

Maintained by Maximilian Boeck. Last updated 3 months ago.

openblas cpp

8.8 match 27 stars 7.58 score 156 scripts

matthieustigler

partsm:Periodic Autoregressive Time Series Models

Basic functions to fit and predict periodic autoregressive time series models. These models are discussed in the book P.H. Franses (1996) "Periodicity and Stochastic Trends in Economic Time Series", Oxford University Press. Data set analyzed in that book is also provided. NOTE: the package was orphaned during several years. It is now only maintained, but no major enhancements are expected, and the maintainer cannot provide any support.

Maintained by Matthieu Stigler. Last updated 4 years ago.

13.7 match 3 stars 4.57 score 25 scripts

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

5.5 match 52 stars 10.34 score 596 scripts 3 dependents

saviviro

sstvars:Toolkit for Reduced Form and Structural Smooth Transition Vector Autoregressive Models

Penalized and non-penalized maximum likelihood estimation of smooth transition vector autoregressive models with various types of transition weight functions, conditional distributions, and identification methods. Constrained estimation with various types of constraints is available. Residual based model diagnostics, forecasting, simulations, and calculation of impulse response functions, generalized impulse response functions, and generalized forecast error variance decompositions. See Heather Anderson, Farshid Vahid (1998) <doi:10.1016/S0304-4076(97)00076-6>, Helmut Lütkepohl, Aleksei Netšunajev (2017) <doi:10.1016/j.jedc.2017.09.001>, Markku Lanne, Savi Virolainen (2025) <doi:10.48550/arXiv.2403.14216>, Savi Virolainen (2025) <doi:10.48550/arXiv.2404.19707>.

Maintained by Savi Virolainen. Last updated 17 days ago.

openblas cpp openmp

7.7 match 4 stars 6.36 score 41 scripts

rtsay1

MTS:All-Purpose Toolkit for Analyzing Multivariate Time Series (MTS) and Estimating Multivariate Volatility Models

Multivariate Time Series (MTS) is a general package for analyzing multivariate linear time series and estimating multivariate volatility models. It also handles factor models, constrained factor models, asymptotic principal component analysis commonly used in finance and econometrics, and principal volatility component analysis. (a) For the multivariate linear time series analysis, the package performs model specification, estimation, model checking, and prediction for many widely used models, including vector AR models, vector MA models, vector ARMA models, seasonal vector ARMA models, VAR models with exogenous variables, multivariate regression models with time series errors, augmented VAR models, and Error-correction VAR models for co-integrated time series. For model specification, the package performs structural specification to overcome the difficulties of identifiability of VARMA models. The methods used for structural specification include Kronecker indices and Scalar Component Models. (b) For multivariate volatility modeling, the MTS package handles several commonly used models, including multivariate exponentially weighted moving-average volatility, Cholesky decomposition volatility models, dynamic conditional correlation (DCC) models, copula-based volatility models, and low-dimensional BEKK models. The package also considers multiple tests for conditional heteroscedasticity, including rank-based statistics. (c) Finally, the MTS package also performs forecasting using diffusion index , transfer function analysis, Bayesian estimation of VAR models, and multivariate time series analysis with missing values.Users can also use the package to simulate VARMA models, to compute impulse response functions of a fitted VARMA model, and to calculate theoretical cross-covariance matrices of a given VARMA model.

Maintained by Ruey S. Tsay. Last updated 3 years ago.

cpp

7.5 match 6 stars 6.52 score 272 scripts 6 dependents

saviviro

uGMAR:Estimate Univariate Gaussian and Student's t Mixture Autoregressive Models

Maximum likelihood estimation of univariate Gaussian Mixture Autoregressive (GMAR), Student's t Mixture Autoregressive (StMAR), and Gaussian and Student's t Mixture Autoregressive (G-StMAR) models, quantile residual tests, graphical diagnostics, forecast and simulate from GMAR, StMAR and G-StMAR processes. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2015) <doi:10.1111/jtsa.12108>, Mika Meitz, Daniel Preve, Pentti Saikkonen (2023) <doi:10.1080/03610926.2021.1916531>, Savi Virolainen (2022) <doi:10.1515/snde-2020-0060>.

Maintained by Savi Virolainen. Last updated 2 months ago.

10.0 match 1 stars 4.88 score 51 scripts

arinams

saeHB.spatial:Small Area Estimation Hierarchical Bayes For Spatial Model

Provides several functions and datasets for area level of Small Area Estimation under Spatial Model using Hierarchical Bayesian (HB) Method. Model-based estimators include the HB estimators based on a Spatial Fay-Herriot model with univariate normal distribution for variable of interest.The 'rjags' package is employed to obtain parameter estimates. For the reference, see Rao and Molina (2015) <doi:10.1002/9781118735855>.

Maintained by Arina Mana Sikana. Last updated 4 months ago.

jags cpp

11.4 match 4.00 score 6 scripts

saviviro

gmvarkit:Estimate Gaussian and Student's t Mixture Vector Autoregressive Models

Unconstrained and constrained maximum likelihood estimation of structural and reduced form Gaussian mixture vector autoregressive, Student's t mixture vector autoregressive, and Gaussian and Student's t mixture vector autoregressive models, quantile residual tests, graphical diagnostics, simulations, forecasting, and estimation of generalized impulse response function and generalized forecast error variance decomposition. Leena Kalliovirta, Mika Meitz, Pentti Saikkonen (2016) <doi:10.1016/j.jeconom.2016.02.012>, Savi Virolainen (2025) <doi:10.1080/07350015.2024.2322090>, Savi Virolainen (2022) <doi:10.48550/arXiv.2109.13648>.

Maintained by Savi Virolainen. Last updated 2 months ago.

8.2 match 3 stars 5.32 score 45 scripts

pulongma

ARCokrig:Autoregressive Cokriging Models for Multifidelity Codes

For emulating multifidelity computer models. The major methods include univariate autoregressive cokriging and multivariate autoregressive cokriging. The autoregressive cokriging methods are implemented for both hierarchically nested design and non-nested design. For hierarchically nested design, the model parameters are estimated via standard optimization algorithms; For non-nested design, the model parameters are estimated via Monte Carlo expectation-maximization (MCEM) algorithms. In both cases, the priors are chosen such that the posterior distributions are proper. Notice that the uniform priors on range parameters in the correlation function lead to improper posteriors. This should be avoided when Bayesian analysis is adopted. The development of objective priors for autoregressive cokriging models can be found in Pulong Ma (2020) <DOI:10.1137/19M1289893>. The development of the multivariate autoregressive cokriging models with possibly non-nested design can be found in Pulong Ma, Georgios Karagiannis, Bledar A Konomi, Taylor G Asher, Gabriel R Toro, and Andrew T Cox (2019) <arXiv:1909.01836>.

Maintained by Pulong Ma. Last updated 3 years ago.

openblas cpp openmp

17.9 match 2 stars 2.30 score 2 scripts

cran

bifurcatingr:Bifurcating Autoregressive Models

Estimation of bifurcating autoregressive models of any order, p, BAR(p) as well as several types of bias correction for the least squares estimators of the autoregressive parameters as described in Zhou and Basawa (2005) <doi:10.1016/j.spl.2005.04.024> and Elbayoumi and Mostafa (2020) <doi:10.1002/sta4.342>. Currently, the bias correction methods supported include bootstrap (single, double and fast-double) bias correction and linear-bias-function-based bias correction. Functions for generating and plotting bifurcating autoregressive data from any BAR(p) model are also included. This new version includes calculating several type of bias-corrected and -uncorrected confidence intervals for the least squares estimators of the autoregressive parameters as described in Elbayoumi and Mostafa (2023) <doi:10.6339/23-JDS1092>.

Maintained by Tamer Elbayoumi. Last updated 11 months ago.

15.0 match 2.70 score

tidymodels

broom:Convert Statistical Objects into Tidy Tibbles

Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.

Maintained by Simon Couch. Last updated 4 months ago.

modeling tidy-data

1.8 match 1.5k stars 21.56 score 37k scripts 1.4k dependents

rjdverse

rjd3sts:State Space Framework and Structural Time Series with 'JDemetra+ 3.x'

R Interface to 'JDemetra+ 3.x' (<https://github.com/jdemetra>) time series analysis software. It offers access to several functions on state space models and structural time series.

Maintained by Jean Palate. Last updated 8 months ago.

openjdk

5.8 match 2 stars 6.64 score 25 scripts 4 dependents

zebang

tensorTS:Factor and Autoregressive Models for Tensor Time Series

Factor and autoregressive models for matrix and tensor valued time series. We provide functions for estimation, simulation and prediction. The models are discussed in Li et al (2021) <doi:10.48550/arXiv.2110.00928>, Chen et al (2020) <DOI:10.1080/01621459.2021.1912757>, Chen et al (2020) <DOI:10.1016/j.jeconom.2020.07.015>, and Xiao et al (2020) <doi:10.48550/arXiv.2006.02611>.

Maintained by Zebang Li. Last updated 14 days ago.

6.9 match 123 stars 5.27 score 4 scripts

daandejongen

hystar:Fit the Hysteretic Threshold Autoregressive Model

Estimate parameters of the hysteretic threshold autoregressive (HysTAR) model, using conditional least squares. In addition, you can generate time series data from the HysTAR model. For details, see Li, Guan, Li and Yu (2015) <doi:10.1093/biomet/asv017>.

Maintained by Daan de Jong. Last updated 1 years ago.

autoregression estimation hysteresis simulation statistics threshold time-series-analysis cpp

13.5 match 2.70 score 3 scripts

business-science

modeltime.ensemble:Ensemble Algorithms for Time Series Forecasting with Modeltime

A 'modeltime' extension that implements time series ensemble forecasting methods including model averaging, weighted averaging, and stacking. These techniques are popular methods to improve forecast accuracy and stability.

Maintained by Matt Dancho. Last updated 8 months ago.

ensemble ensemble-learning forecast forecasting modeltime stacking stacking-ensemble tidymodels time time-series timeseries

4.4 match 77 stars 8.30 score 143 scripts

spatlyu

HSAR:Hierarchical Spatial Autoregressive Model

A Hierarchical Spatial Autoregressive Model (HSAR), based on a Bayesian Markov Chain Monte Carlo (MCMC) algorithm (Dong and Harris (2014) <doi:10.1111/gean.12049>). The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.

Maintained by Wenbo Lv. Last updated 3 months ago.

spatial-econometrics spatial-regression spatial-statistics openblas cpp openmp

6.6 match 6 stars 5.43 score 30 scripts

tylerjpike

sovereign:State-Dependent Empirical Analysis

A set of tools for state-dependent empirical analysis through both VAR- and local projection-based state-dependent forecasts, impulse response functions, historical decompositions, and forecast error variance decompositions.

Maintained by Tyler J. Pike. Last updated 2 years ago.

econometrics forecasting impulse-response local-projection macroeconomics state-dependent time-series vector-autoregression

7.5 match 11 stars 4.74 score 8 scripts

vlyubchich

funtimes:Functions for Time Series Analysis

Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.

Maintained by Vyacheslav Lyubchich. Last updated 2 years ago.

5.3 match 7 stars 6.69 score 93 scripts

fernandalschumacher

ARCensReg:Fitting Univariate Censored Linear Regression Model with Autoregressive Errors

It fits a univariate left, right, or interval censored linear regression model with autoregressive errors, considering the normal or the Student-t distribution for the innovations. It provides estimates and standard errors of the parameters, predicts future observations, and supports missing values on the dependent variable. References used for this package: Schumacher, F. L., Lachos, V. H., & Dey, D. K. (2017). Censored regression models with autoregressive errors: A likelihood-based perspective. Canadian Journal of Statistics, 45(4), 375-392 <doi:10.1002/cjs.11338>. Schumacher, F. L., Lachos, V. H., Vilca-Labra, F. E., & Castro, L. M. (2018). Influence diagnostics for censored regression models with autoregressive errors. Australian & New Zealand Journal of Statistics, 60(2), 209-229 <doi:10.1111/anzs.12229>. Valeriano, K. A., Schumacher, F. L., Galarza, C. E., & Matos, L. A. (2021). Censored autoregressive regression models with Student-t innovations. arXiv preprint <arXiv:2110.00224>.

Maintained by Fernanda L. Schumacher. Last updated 2 years ago.

openblas cpp openmp

12.5 match 1 stars 2.70 score 9 scripts

atsa-es

tvvarss:Time Varying Vector Autoregressive State Space Models

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.

Maintained by Eric Ward. Last updated 3 years ago.

bayesian multivariate-timeseries state-space time-series cpp

8.2 match 10 stars 4.04 score 11 scripts

robjhyndman

forecast:Forecasting Functions for Time Series and Linear Models

Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling.

Maintained by Rob Hyndman. Last updated 7 months ago.

forecast forecasting openblas cpp

1.7 match 1.1k stars 18.63 score 16k scripts 239 dependents

nk027

BVARverse:Tidy Bayesian Vector Autoregression

Functions to prepare tidy objects from estimated models via 'BVAR' (see Kuschnig & Vashold, 2019 <doi:10.13140/RG.2.2.25541.60643>) and visualisation thereof. Bridges the gap between estimating models with 'BVAR' and plotting the results in a more sophisticated way with 'ggplot2' as well as passing them on in a tidy format.

Maintained by Lukas Vashold. Last updated 5 years ago.

bayesian data-science vector-autoregressions

10.6 match 2 stars 3.00 score 7 scripts

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.

11.3 match 1 stars 2.70 score 2 scripts

ineswilms

bigtime:Sparse Estimation of Large Time Series Models

Estimation of large Vector AutoRegressive (VAR), Vector AutoRegressive with Exogenous Variables X (VARX) and Vector AutoRegressive Moving Average (VARMA) Models with Structured Lasso Penalties, see Nicholson, Wilms, Bien and Matteson (2020) <https://jmlr.org/papers/v21/19-777.html> and Wilms, Basu, Bien and Matteson (2021) <doi:10.1080/01621459.2021.1942013>.

Maintained by Ines Wilms. Last updated 2 years ago.

openblas cpp

6.0 match 30 stars 4.94 score 29 scripts

adrian-bowman

sm:Smoothing Methods for Nonparametric Regression and Density Estimation

This is software linked to the book 'Applied Smoothing Techniques for Data Analysis - The Kernel Approach with S-Plus Illustrations' Oxford University Press.

Maintained by Adrian Bowman. Last updated 1 years ago.

fortran

4.1 match 1 stars 6.99 score 732 scripts 36 dependents

usepa

spmodel:Spatial Statistical Modeling and Prediction

Fit, summarize, and predict for a variety of spatial statistical models applied to point-referenced and areal (lattice) data. Parameters are estimated using various methods. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more. Model-fit statistics are used to summarize, visualize, and compare models. Predictions at unobserved locations are readily obtainable. For additional details, see Dumelle et al. (2023) <doi:10.1371/journal.pone.0282524>.

Maintained by Michael Dumelle. Last updated 4 days ago.

3.7 match 15 stars 7.66 score 112 scripts 3 dependents

jmbh

mgm:Estimating Time-Varying k-Order Mixed Graphical Models

Estimation of k-Order time-varying Mixed Graphical Models and mixed VAR(p) models via elastic-net regularized neighborhood regression. For details see Haslbeck & Waldorp (2020) <doi:10.18637/jss.v093.i08>.

Maintained by Jonas Haslbeck. Last updated 6 days ago.

3.4 match 29 stars 8.16 score 125 scripts 6 dependents

fk83

bvarsv:Bayesian Analysis of a Vector Autoregressive Model with Stochastic Volatility and Time-Varying Parameters

R/C++ implementation of the model proposed by Primiceri ("Time Varying Structural Vector Autoregressions and Monetary Policy", Review of Economic Studies, 2005), with functionality for computing posterior predictive distributions and impulse responses.

Maintained by Fabian Krueger. Last updated 6 months ago.

openblas cpp

4.9 match 30 stars 5.43 score 60 scripts 1 dependents

munchfab

mlts:Multilevel Latent Time Series Models with 'R' and 'Stan'

Fit multilevel manifest or latent time-series models, including popular Dynamic Structural Equation Models (DSEM). The models can be set up and modified with user-friendly functions and are fit to the data using 'Stan' for Bayesian inference. Path models and formulas for user-defined models can be easily created with functions using 'knitr'. Asparouhov, Hamaker, & Muthen (2018) <doi:10.1080/10705511.2017.1406803>.

Maintained by Kenneth Koslowski. Last updated 9 months ago.

cpp

4.6 match 2 stars 5.68 score 9 scripts

stan-dev

loo:Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

Efficient approximate leave-one-out cross-validation (LOO) for Bayesian models fit using Markov chain Monte Carlo, as described in Vehtari, Gelman, and Gabry (2017) <doi:10.1007/s11222-016-9696-4>. The approximation uses Pareto smoothed importance sampling (PSIS), a new procedure for regularizing importance weights. As a byproduct of the calculations, we also obtain approximate standard errors for estimated predictive errors and for the comparison of predictive errors between models. The package also provides methods for using stacking and other model weighting techniques to average Bayesian predictive distributions.

Maintained by Jonah Gabry. Last updated 3 days ago.

bayes bayesian bayesian-data-analysis bayesian-inference bayesian-methods bayesian-statistics cross-validation information-criterion model-comparison stan

1.5 match 152 stars 17.30 score 2.6k scripts 297 dependents

famuvie

breedR:Statistical Methods for Forest Genetic Resources Analysts

Statistical tools to build predictive models for the breeders community. It aims to assess the genetic value of individuals under a number of situations, including spatial autocorrelation, genetic/environment interaction and competition. It is under active development as part of the Trees4Future project, particularly developed having forest genetic trials in mind. But can be used for animals or other situations as well.

Maintained by Facundo Muñoz. Last updated 8 months ago.

4.7 match 33 stars 5.44 score 24 scripts

cran

ConnectednessApproach:Connectedness Approach

The estimation of static and dynamic connectedness measures is created in a modular and user-friendly way. Besides, the time domain connectedness approaches, this package further allows to estimate the frequency connectedness approach, the joint spillover index and the extended joint connectedness approach. In addition, all connectedness frameworks can be based upon orthogonalized and generalized VAR, QVAR, LASSO VAR, Ridge VAR, Elastic Net VAR and TVP-VAR models. Furthermore, the package includes the conditional, decomposed and partial connectedness measures as well as the pairwise connectedness index, influence index and corrected total connectedness index. Finally, a battery of datasets are available allowing to replicate a variety of connectedness papers.

Maintained by David Gabauer. Last updated 16 days ago.

9.7 match 4 stars 2.60 score

gpiras

sphet:Estimation of Spatial Autoregressive Models with and without Heteroskedastic Innovations

Functions for fitting Cliff-Ord-type spatial autoregressive models with and without heteroskedastic innovations using Generalized Method of Moments estimation are provided. Some support is available for fitting spatial HAC models, and for fitting with non-spatial endogeneous variables using instrumental variables.

Maintained by Gianfranco Piras. Last updated 7 days ago.

3.4 match 8 stars 7.43 score 188 scripts 3 dependents

cran

fGarch:Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

Analyze and model heteroskedastic behavior in financial time series.

Maintained by Georgi N. Boshnakov. Last updated 12 months ago.

fortran

3.0 match 6 stars 8.20 score 1.1k scripts 51 dependents

yuimaproject

yuima:The YUIMA Project Package for SDEs

Simulation and Inference for SDEs and Other Stochastic Processes.

Maintained by Stefano M. Iacus. Last updated 3 days ago.

openblas cpp

3.4 match 9 stars 7.26 score 92 scripts 2 dependents

openpharma

brms.mmrm:Bayesian MMRMs using 'brms'

The mixed model for repeated measures (MMRM) is a popular model for longitudinal clinical trial data with continuous endpoints, and 'brms' is a powerful and versatile package for fitting Bayesian regression models. The 'brms.mmrm' R package leverages 'brms' to run MMRMs, and it supports a simplified interfaced to reduce difficulty and align with the best practices of the life sciences. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>, Mallinckrodt (2008) <doi:10.1177/009286150804200402>.

Maintained by William Michael Landau. Last updated 6 months ago.

brms life-sciences mc-stan mmrm stan statistics

2.8 match 21 stars 8.80 score 13 scripts

haghbinh

Rsfar:Seasonal Functional Autoregressive Models

This is a collection of functions designed for simulating, estimating and forecasting seasonal functional autoregressive time series of order one. These methods are addressed in the manuscript: <https://www.monash.edu/business/ebs/research/publications/ebs/wp16-2019.pdf>.

Maintained by Hossein Haghbin. Last updated 4 years ago.

7.1 match 5 stars 3.40 score

zaenalium

gstar:Generalized Space-Time Autoregressive Model

A package for analyzing multivariate time series data using method Generalized Space-Time Autoregressive Model by Ruchjana et al(2012) <doi:10.1063/1.4724118>.

Maintained by Ahmad Zaenal. Last updated 6 years ago.

7.0 match 5 stars 3.40 score 9 scripts

vladimirholy

gasmodel:Generalized Autoregressive Score Models

Estimation, forecasting, and simulation of generalized autoregressive score (GAS) models of Creal, Koopman, and Lucas (2013) <doi:10.1002/jae.1279> and Harvey (2013) <doi:10.1017/cbo9781139540933>. Model specification allows for various data types and distributions, different parametrizations, exogenous variables, joint and separate modeling of exogenous variables and dynamics, higher score and autoregressive orders, custom and unconditional initial values of time-varying parameters, fixed and bounded values of coefficients, and missing values. Model estimation is performed by the maximum likelihood method.

Maintained by Vladimír Holý. Last updated 1 years ago.

dcs gas time-series

3.9 match 14 stars 5.45 score 2 scripts

sciurus365

quadVAR:Quadratic Vector Autoregression

Estimate quadratic vector autoregression models with the strong hierarchy using the Regularization Algorithm under Marginality Principle (RAMP) by Hao et al. (2018) <doi:10.1080/01621459.2016.1264956>, compare the performance with linear models, and construct networks with partial derivatives.

Maintained by Jingmeng Cui. Last updated 1 months ago.

5.6 match 3.78 score 3 scripts

zejiang-unsw

synthesis:Generate Synthetic Data from Statistical Models

Generate synthetic time series from commonly used statistical models, including linear, nonlinear and chaotic systems. Applications to testing methods can be found in Jiang, Z., Sharma, A., & Johnson, F. (2019) <doi:10.1016/j.advwatres.2019.103430> and Jiang, Z., Sharma, A., & Johnson, F. (2020) <doi:10.1029/2019WR026962> associated with an open-source tool by Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>.

Maintained by Ze Jiang. Last updated 9 months ago.

4.5 match 3 stars 4.56 score 12 scripts

f-rousset

spaMM:Mixed-Effect Models, with or without Spatial Random Effects

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.

Maintained by François Rousset. Last updated 9 months ago.

gsl cpp openmp

4.1 match 4.94 score 208 scripts 5 dependents

wbnicholson

BigVAR:Dimension Reduction Methods for Multivariate Time Series

Estimates VAR and VARX models with Structured Penalties.

Maintained by Will Nicholson. Last updated 6 months ago.

openblas cpp

2.8 match 57 stars 7.23 score 100 scripts 1 dependents

cran

BHSBVAR:Structural Bayesian Vector Autoregression Models

Provides a function for estimating the parameters of Structural Bayesian Vector Autoregression models with the method developed by Baumeister and Hamilton (2015) <doi:10.3982/ECTA12356>, Baumeister and Hamilton (2017) <doi:10.3386/w24167>, and Baumeister and Hamilton (2018) <doi:10.1016/j.jmoneco.2018.06.005>. Functions for plotting impulse responses, historical decompositions, and posterior distributions of model parameters are also provided.

Maintained by Paul Richardson. Last updated 13 days ago.

openblas cpp openmp

10.1 match 1 stars 2.00 score

cran

ftsa:Functional Time Series Analysis

Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.

Maintained by Han Lin Shang. Last updated 23 days ago.

3.4 match 6 stars 5.95 score 96 scripts 10 dependents

fk83

scoringRules:Scoring Rules for Parametric and Simulated Distribution Forecasts

Dictionary-like reference for computing scoring rules in a wide range of situations. Covers both parametric forecast distributions (such as mixtures of Gaussians) and distributions generated via simulation. Further details can be found in the package vignettes <doi:10.18637/jss.v090.i12>, <doi:10.18637/jss.v110.i08>.

Maintained by Fabian Krueger. Last updated 6 months ago.

openblas cpp

1.8 match 59 stars 11.33 score 408 scripts 13 dependents

andyphilips

dynamac:Dynamic Simulation and Testing for Single-Equation ARDL Models

While autoregressive distributed lag (ARDL) models allow for extremely flexible dynamics, interpreting substantive significance of complex lag structures remains difficult. This package is designed to assist users in dynamically simulating and plotting the results of various ARDL models. It also contains post-estimation diagnostics, including a test for cointegration when estimating the error-correction variant of the autoregressive distributed lag model (Pesaran, Shin, and Smith 2001 <doi:10.1002/jae.616>).

Maintained by Soren Jordan. Last updated 4 years ago.

ardl stata time-series time-series-analysis

3.5 match 7 stars 5.59 score 37 scripts 1 dependents

jeksterslab

simStateSpace:Simulate Data from State Space Models

Provides a streamlined and user-friendly framework for simulating data in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. For an introduction to state space models in social and behavioral sciences, refer to Chow, Ho, Hamaker, and Dolan (2010) <doi:10.1080/10705511003661553>.

Maintained by Ivan Jacob Agaloos Pesigan. Last updated 30 days ago.

simulation state-space-model openblas cpp openmp

3.4 match 1 stars 5.78 score 75 scripts 2 dependents

cran

ref.ICAR:Objective Bayes Intrinsic Conditional Autoregressive Model for Areal Data

Implements an objective Bayes intrinsic conditional autoregressive prior. This model provides an objective Bayesian approach for modeling spatially correlated areal data using an intrinsic conditional autoregressive prior on a vector of spatial random effects.

Maintained by Erica M. Porter. Last updated 2 months ago.

6.9 match 2.81 score 13 scripts

hanwengutierrez

TAR:Bayesian Modeling of Autoregressive Threshold Time Series Models

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

Maintained by Hanwen Zhang. Last updated 8 years ago.

7.1 match 5 stars 2.74 score 11 scripts

sachaepskamp

qgraph:Graph Plotting Methods, Psychometric Data Visualization and Graphical Model Estimation

Fork of qgraph - Weighted network visualization and analysis, as well as Gaussian graphical model computation. See Epskamp et al. (2012) <doi:10.18637/jss.v048.i04>.

Maintained by Sacha Epskamp. Last updated 1 years ago.

cpp

1.7 match 69 stars 11.43 score 1.2k scripts 63 dependents

tkrisztin

estimateW:Estimation of Spatial Weight Matrices

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

Maintained by Tamas Krisztin. Last updated 2 years ago.

openblas cpp

7.1 match 2.70 score 2 scripts

cran

spass:Study Planning and Adaptation of Sample Size

Sample size estimation and blinded sample size reestimation in Adaptive Study Design.

Maintained by Marius Placzek. Last updated 4 years ago.

cpp

14.4 match 1.30 score

looping027

far:Modelization for Functional AutoRegressive Processes

Modelizations and previsions functions for Functional AutoRegressive processes using nonparametric methods: functional kernel, estimation of the covariance operator in a subspace, ...

Maintained by Julien Damon. Last updated 6 months ago.

3.7 match 4 stars 5.06 score 64 scripts 3 dependents

paolomaranzano

SCDA:Spatially-Clustered Data Analysis

Contains functions for statistical data analysis based on spatially-clustered techniques. The package allows estimating the spatially-clustered spatial regression models presented in Cerqueti, Maranzano \& Mattera (2024), "Spatially-clustered spatial autoregressive models with application to agricultural market concentration in Europe", arXiv preprint 2407.15874 <doi:10.48550/arXiv.2407.15874>. Specifically, the current release allows the estimation of the spatially-clustered linear regression model (SCLM), the spatially-clustered spatial autoregressive model (SCSAR), the spatially-clustered spatial Durbin model (SCSEM), and the spatially-clustered linear regression model with spatially-lagged exogenous covariates (SCSLX). From release 0.0.2, the library contains functions to estimate spatial clustering based on Adiajacent Matrix K-Means (AMKM) as described in Zhou, Liu \& Zhu (2019), "Weighted adjacent matrix for K-means clustering", Multimedia Tools and Applications, 78 (23) <doi:10.1007/s11042-019-08009-x>.

Maintained by Paolo Maranzano. Last updated 5 months ago.

10.0 match 1.79 score 31 scripts

mingstat

ZIM:Zero-Inflated Models (ZIM) for Count Time Series with Excess Zeros

Analyze count time series with excess zeros. Two types of statistical models are supported: Markov regression by Yang et al. (2013) <doi:10.1016/j.stamet.2013.02.001> and state-space models by Yang et al. (2015) <doi:10.1177/1471082X14535530>. They are also known as observation-driven and parameter-driven models respectively in the time series literature. The functions used for Markov regression or observation-driven models can also be used to fit ordinary regression models with independent data under the zero-inflated Poisson (ZIP) or zero-inflated negative binomial (ZINB) assumption. Besides, the package contains some miscellaneous functions to compute density, distribution, quantile, and generate random numbers from ZIP and ZINB distributions.

Maintained by Ming Yang. Last updated 1 years ago.

3.0 match 8 stars 5.95 score 32 scripts

connordonegan

geostan:Bayesian Spatial Analysis

For spatial data analysis; provides exploratory spatial analysis tools, spatial regression, spatial econometric, and disease mapping models, model diagnostics, and special methods for inference with small area survey data (e.g., the America Community Survey (ACS)) and censored population health monitoring data. Models are pre-specified using the Stan programming language, a platform for Bayesian inference using Markov chain Monte Carlo (MCMC). References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Donegan (2021) <doi:10.31219/osf.io/3ey65>; Donegan (2022) <doi:10.21105/joss.04716>; Donegan, Chun and Hughes (2020) <doi:10.1016/j.spasta.2020.100450>; Donegan, Chun and Griffith (2021) <doi:10.3390/ijerph18136856>; Morris et al. (2019) <doi:10.1016/j.sste.2019.100301>.

Maintained by Connor Donegan. Last updated 3 months ago.

bayesian bayesian-inference bayesian-statistics epidemiology modeling public-health rspatial spatial stan cpp

2.0 match 80 stars 8.80 score 46 scripts

cran

hdiVAR:Statistical Inference for Noisy Vector Autoregression

The model is high-dimensional vector autoregression with measurement error, also known as linear gaussian state-space model. Provable sparse expectation-maximization algorithm is provided for the estimation of transition matrix and noise variances. Global and simultaneous testings are implemented for transition matrix with false discovery rate control. For more information, see the accompanying paper: Lyu, X., Kang, J., & Li, L. (2023). "Statistical inference for high-dimensional vector autoregression with measurement error", Statistica Sinica.

Maintained by Xiang Lyu. Last updated 2 years ago.

8.8 match 2.00 score 3 scripts

cran

glarma:Generalized Linear Autoregressive Moving Average Models

Functions are provided for estimation, testing, diagnostic checking and forecasting of generalized linear autoregressive moving average (GLARMA) models for discrete valued time series with regression variables. These are a class of observation driven non-linear non-Gaussian state space models. The state vector consists of a linear regression component plus an observation driven component consisting of an autoregressive-moving average (ARMA) filter of past predictive residuals. Currently three distributions (Poisson, negative binomial and binomial) can be used for the response series. Three options (Pearson, score-type and unscaled) for the residuals in the observation driven component are available. Estimation is via maximum likelihood (conditional on initializing values for the ARMA process) optimized using Fisher scoring or Newton Raphson iterative methods. Likelihood ratio and Wald tests for the observation driven component allow testing for serial dependence in generalized linear model settings. Graphical diagnostics including model fits, autocorrelation functions and probability integral transform residuals are included in the package. Several standard data sets are included in the package.

Maintained by "William T.M. Dunsmuir". Last updated 7 years ago.

5.4 match 2 stars 3.26 score 1 dependents

cran

VAR.spec:Allows Specifying a Bivariate VAR (Vector Autoregression) with Desired Spectral Characteristics

The spectral characteristics of a bivariate series (Marginal Spectra, Coherency- and Phase-Spectrum) determine whether there is a strong presence of short-, medium-, or long-term fluctuations (components of certain frequencies in the spectral representation of the series) in each one of them. These are induced by strong peaks of the marginal spectra of each series at the corresponding frequencies. The spectral characteristics also determine how strongly these short-, medium-, or long-term fluctuations of the two series are correlated between the two series. Information on this is provided by the Coherency spectrum at the corresponding frequencies. Finally, certain fluctuations of the two series may be lagged to each other. Information on this is provided by the Phase spectrum at the corresponding frequencies. The idea in this package is to define a VAR (Vector autoregression) model with desired spectral characteristics by specifying a number of polynomials, required to define the VAR. See Ioannidis(2007) <doi:10.1016/j.jspi.2005.12.013>. These are specified via their roots, instead of via their coefficients. This is an idea borrowed from the Time Series Library of R. Dahlhaus, where it is used for defining ARMA models for univariate time series. This way, one may e.g. specify a VAR inducing a strong presence of long-term fluctuations in series 1 and in series 2, which are weakly correlated, but lagged by a number of time units to each other, while short-term fluctuations in series 1 and in series 2, are strongly present only in one of the two series, while they are strongly correlated to each other between the two series. Simulation from such models allows studying the behavior of data-analysis tools, such as estimation of the spectra, under different circumstances, as e.g. peaks in the spectra, generating bias, induced by leakage.

Maintained by Evangelos Ioannidis. Last updated 9 months ago.

16.8 match 1.00 score

geobosh

pcts:Periodically Correlated and Periodically Integrated Time Series

Classes and methods for modelling and simulation of periodically correlated (PC) and periodically integrated time series. Compute theoretical periodic autocovariances and related properties of PC autoregressive moving average models. Some original methods including Boshnakov & Iqelan (2009) <doi:10.1111/j.1467-9892.2009.00617.x>, Boshnakov (1996) <doi:10.1111/j.1467-9892.1996.tb00281.x>.

Maintained by Georgi N. Boshnakov. Last updated 1 years ago.

par-models periodic periodic-models piar-models seasonal time-series time-series-models

4.2 match 2 stars 4.00 score 3 scripts

bioc

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 16 days ago.

spatial singlecell transcriptomics visualization openblas cpp openmp

3.5 match 4.54 score 3 scripts

r-forge

signal:Signal Processing

A set of signal processing functions originally written for 'Matlab' and 'Octave'. Includes filter generation utilities, filtering functions, resampling routines, and visualization of filter models. It also includes interpolation functions.

Maintained by Uwe Ligges. Last updated 1 years ago.

fortran

1.8 match 8.78 score 828 scripts 151 dependents

morrowcj

remotePARTS:Spatiotemporal Autoregression Analyses for Large Data Sets

These tools were created to test map-scale hypotheses about trends in large remotely sensed data sets but any data with spatial and temporal variation can be analyzed. Tests are conducted using the PARTS method for analyzing spatially autocorrelated time series (Ives et al., 2021: <doi:10.1016/j.rse.2021.112678>). The method's unique approach can handle extremely large data sets that other spatiotemporal models cannot, while still appropriately accounting for spatial and temporal autocorrelation. This is done by partitioning the data into smaller chunks, analyzing chunks separately and then combining the separate analyses into a single, correlated test of the map-scale hypotheses.

Maintained by Clay Morrow. Last updated 2 years ago.

autocorrelation big-data remote-sensing-in-r statistical-analysis cpp openmp

2.9 match 22 stars 5.25 score 16 scripts

cran

CSTools:Assessing Skill of Climate Forecasts on Seasonal-to-Decadal Timescales

Exploits dynamical seasonal forecasts in order to provide information relevant to stakeholders at the seasonal timescale. The package contains process-based methods for forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination and multivariate verification, as well as basic and advanced tools to obtain tailored products. This package was developed in the context of the 'ERA4CS' project 'MEDSCOPE' and the 'H2020 S2S4E' project and includes contributions from 'ArticXchange' project founded by 'EU-PolarNet 2'. 'Pérez-Zanón et al. (2022) <doi:10.5194/gmd-15-6115-2022>'. 'Doblas-Reyes et al. (2005) <doi:10.1111/j.1600-0870.2005.00104.x>'. 'Mishra et al. (2018) <doi:10.1007/s00382-018-4404-z>'. 'Sanchez-Garcia et al. (2019) <doi:10.5194/asr-16-165-2019>'. 'Straus et al. (2007) <doi:10.1175/JCLI4070.1>'. 'Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>'. 'Torralba et al. (2017) <doi:10.1175/JAMC-D-16-0204.1>'. 'D'Onofrio et al. (2014) <doi:10.1175/JHM-D-13-096.1>'. 'Verfaillie et al. (2017) <doi:10.5194/gmd-10-4257-2017>'. 'Van Schaeybroeck et al. (2019) <doi:10.1016/B978-0-12-812372-0.00010-8>'. 'Yiou et al. (2013) <doi:10.1007/s00382-012-1626-3>'.

Maintained by Victoria Agudetse. Last updated 1 years ago.

fortran

2.9 match 2 stars 5.32 score 62 scripts 1 dependents

christophergandrud

dynsim:Dynamic Simulations of Autoregressive Relationships

Dynamic simulations and graphical depictions of autoregressive relationships.

Maintained by Christopher Gandrud. Last updated 4 years ago.

5.6 match 1 stars 2.70 score 4 scripts

sciurus365

NVAR:Nonlinear Vector Autoregression Models

Estimate nonlinear vector autoregression models (also known as the next generation reservoir computing) for nonlinear dynamic systems. The algorithm was described by Gauthier et al. (2021) <doi:10.1038/s41467-021-25801-2>.

Maintained by Jingmeng Cui. Last updated 1 years ago.

5.4 match 1 stars 2.70 score 5 scripts

openpharma

mmrm:Mixed Models for Repeated Measures

Mixed models for repeated measures (MMRM) are a popular choice for analyzing longitudinal continuous outcomes in randomized clinical trials and beyond; see Cnaan, Laird and Slasor (1997) <doi:10.1002/(SICI)1097-0258(19971030)16:20%3C2349::AID-SIM667%3E3.0.CO;2-E> for a tutorial and Mallinckrodt, Lane, Schnell, Peng and Mancuso (2008) <doi:10.1177/009286150804200402> for a review. This package implements MMRM based on the marginal linear model without random effects using Template Model Builder ('TMB') which enables fast and robust model fitting. Users can specify a variety of covariance matrices, weight observations, fit models with restricted or standard maximum likelihood inference, perform hypothesis testing with Satterthwaite or Kenward-Roger adjustment, and extract least square means estimates by using 'emmeans'.

Maintained by Daniel Sabanes Bove. Last updated 10 days ago.

cpp

1.2 match 138 stars 12.15 score 113 scripts 4 dependents

martin3141

spant:MR Spectroscopy Analysis Tools

Tools for reading, visualising and processing Magnetic Resonance Spectroscopy data. The package includes methods for spectral fitting: Wilson (2021) <DOI:10.1002/mrm.28385> and spectral alignment: Wilson (2018) <DOI:10.1002/mrm.27605>.

Maintained by Martin Wilson. Last updated 1 months ago.

brain mri mrs mrshub spectroscopy fortran

1.7 match 25 stars 8.52 score 81 scripts

yogasatria30

sgstar:Seasonal Generalized Space Time Autoregressive (S-GSTAR) Model

A set of function that implements for seasonal multivariate time series analysis based on Seasonal Generalized Space Time Autoregressive with Seemingly Unrelated Regression (S-GSTAR-SUR) Model by Setiawan(2016)<https://www.researchgate.net/publication/316517889_S-GSTAR-SUR_model_for_seasonal_spatio_temporal_data_forecasting>.

Maintained by M. Yoga Satria Utama Developer. Last updated 4 years ago.

5.0 match 2.70 score 8 scripts

r-cas

Ryacas:R Interface to the 'Yacas' Computer Algebra System

Interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).

Maintained by Mikkel Meyer Andersen. Last updated 2 years ago.

cpp

1.3 match 40 stars 10.15 score 167 scripts 14 dependents

atsa-es

MAR1:Multivariate Autoregressive Modeling for Analysis of Community Time-Series Data

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.

Maintained by Elizabeth Eli Holmes. Last updated 2 years ago.

multivariate-timeseries time-series

4.5 match 1 stars 3.00 score

gateslab

gimme:Group Iterative Multiple Model Estimation

Data-driven approach for arriving at person-specific time series models. The method first identifies which relations replicate across the majority of individuals to detect signal from noise. These group-level relations are then used as a foundation for starting the search for person-specific (or individual-level) relations. See Gates & Molenaar (2012) <doi:10.1016/j.neuroimage.2012.06.026>.

Maintained by Kathleen M Gates. Last updated 6 months ago.

1.8 match 26 stars 7.71 score 53 scripts

cran

astrochron:A Computational Tool for Astrochronology

Routines for astrochronologic testing, astronomical time scale construction, and time series analysis <doi:10.1016/j.earscirev.2018.11.015>. Also included are a range of statistical analysis and modeling routines that are relevant to time scale development and paleoclimate analysis.

Maintained by Stephen Meyers. Last updated 6 months ago.

fortran

3.5 match 5 stars 3.85 score 141 scripts

cran

hglm:Hierarchical Generalized Linear Models

Implemented here are procedures for fitting hierarchical generalized linear models (HGLM). It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the mean model. As statistical models, HGLMs were initially developed by Lee and Nelder (1996) <https://www.jstor.org/stable/2346105?seq=1>. We provide an implementation (Ronnegard, Alam and Shen 2010) <https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Roennegaard~et~al.pdf> following Lee, Nelder and Pawitan (2006) <ISBN: 9781420011340> with algorithms extended for spatial modeling (Alam, Ronnegard and Shen 2015) <https://journal.r-project.org/archive/2015/RJ-2015-017/RJ-2015-017.pdf>.

Maintained by Xia Shen. Last updated 6 years ago.

4.0 match 2 stars 3.30 score

gbhumphrey1

validann:Validation Tools for Artificial Neural Networks

Methods and tools for analysing and validating the outputs and modelled functions of artificial neural networks (ANNs) in terms of predictive, replicative and structural validity. Also provides a method for fitting feed-forward ANNs with a single hidden layer.

Maintained by Greer B. Humphrey. Last updated 8 years ago.

3.6 match 3 stars 3.67 score 31 scripts

convfunctimeseries

NTS:Nonlinear Time Series Analysis

Simulation, estimation, prediction procedure, and model identification methods for nonlinear time series analysis, including threshold autoregressive models, Markov-switching models, convolutional functional autoregressive models, nonlinearity tests, Kalman filters and various sequential Monte Carlo methods. More examples and details about this package can be found in the book "Nonlinear Time Series Analysis" by Ruey S. Tsay and Rong Chen, John Wiley & Sons, 2018 (ISBN: 978-1-119-26407-1).

Maintained by Xialu Liu. Last updated 1 years ago.

4.3 match 2 stars 2.94 score 48 scripts

ecor

RMAWGEN:Multi-Site Auto-Regressive Weather GENerator

S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time series of temperature and precipitation. These tools make use of Vector AutoRegressive models (VARs). The weather generator model is then saved as an object and is calibrated by daily instrumental "Gaussianized" time series through the 'vars' package tools. Once obtained this model, it can it can be used for weather generations and be adapted to work with several climatic monthly time series.

Maintained by Emanuele Cordano. Last updated 27 days ago.

2.3 match 3 stars 5.62 score 115 scripts 4 dependents

cran

mlVAR:Multi-Level Vector Autoregression

Estimates the multi-level vector autoregression model on time-series data. Three network structures are obtained: temporal networks, contemporaneous networks and between-subjects networks.

Maintained by Sacha Epskamp. Last updated 1 years ago.

3.7 match 3.46 score 81 scripts 2 dependents

jonathancornelissen

highfrequency:Tools for Highfrequency Data Analysis

Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity. A detailed vignette can be found in the paper "Analyzing Intraday Financial Data in R: The highfrequency Package" by Boudt, Kleen, and Sjoerup (2022, <doi:10.18637/jss.v104.i08>). The DOI in the CITATION is for a new Journal of Statistical Software publication that will be registered after publication on CRAN. A working paper version can be found on SSRN: <doi:10.2139/ssrn.3917548>.

Maintained by Kris Boudt. Last updated 2 years ago.

openblas cpp openmp

1.7 match 152 stars 7.37 score 286 scripts

gsucarrat

gets:General-to-Specific (GETS) Modelling and Indicator Saturation Methods

Automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and indicator saturation methods for detecting and testing for structural breaks in the mean, see Pretis, Reade and Sucarrat (2018) <doi:10.18637/jss.v086.i03> for an overview of the package. In advanced use, the estimator and diagnostics tests can be fully user-specified, see Sucarrat (2021) <doi:10.32614/RJ-2021-024>.

Maintained by Genaro Sucarrat. Last updated 8 months ago.

1.8 match 8 stars 6.89 score 73 scripts 3 dependents

rominsal

spsur:Spatial Seemingly Unrelated Regression Models

A collection of functions to test and estimate Seemingly Unrelated Regression (usually called SUR) models, with spatial structure, by maximum likelihood and three-stage least squares. The package estimates the most common spatial specifications, that is, SUR with Spatial Lag of X regressors (called SUR-SLX), SUR with Spatial Lag Model (called SUR-SLM), SUR with Spatial Error Model (called SUR-SEM), SUR with Spatial Durbin Model (called SUR-SDM), SUR with Spatial Durbin Error Model (called SUR-SDEM), SUR with Spatial Autoregressive terms and Spatial Autoregressive Disturbances (called SUR-SARAR), SUR-SARAR with Spatial Lag of X regressors (called SUR-GNM) and SUR with Spatially Independent Model (called SUR-SIM). The methodology of these models can be found in next references: Mur, J., Lopez, F., and Herrera, M. (2010) <doi:10.1080/17421772.2010.516443>; Lopez, F.A., Mur, J., and Angulo, A. (2014) <doi:10.1007/s00168-014-0624-2> and Lopez, F.A., Minguez, R. and Mur, J. (2020) <doi:10.1007/s00168-019-00914-1>.

Maintained by Roman Minguez. Last updated 3 years ago.

2.0 match 11 stars 6.15 score 32 scripts

fcheysson

starma:Modelling Space Time AutoRegressive Moving Average (STARMA) Processes

Statistical functions to identify, estimate and diagnose a Space-Time AutoRegressive Moving Average (STARMA) model.

Maintained by Felix Cheysson. Last updated 4 years ago.

openblas cpp

5.0 match 2 stars 2.38 score 12 scripts

nelson-n

lmForc:Linear Model Forecasting

Introduces in-sample, out-of-sample, pseudo out-of-sample, and benchmark model forecast tests and a new class for working with forecast data, Forecast.

Maintained by Nelson Rayl. Last updated 7 months ago.

forecasting linear-models

2.3 match 6 stars 5.26 score 20 scripts

eliaskrainski

INLAspacetime:Spatial and Spatio-Temporal Models using 'INLA'

Prepare objects to implement models over spatial and spacetime domains with the 'INLA' package (<https://www.r-inla.org>). These objects contain data to for the 'cgeneric' interface in 'INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal models proposed in Lindgren et. al. (2023) <https://www.idescat.cat/sort/sort481/48.1.1.Lindgren-etal.pdf>. Details are provided in the available vignettes and from the URL bellow.

Maintained by Elias Teixeira Krainski. Last updated 3 days ago.

openblas

1.7 match 4 stars 7.05 score 56 scripts

sachaepskamp

psychonetrics:Structural Equation Modeling and Confirmatory Network Analysis

Multi-group (dynamical) structural equation models in combination with confirmatory network models from cross-sectional, time-series and panel data <doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing and fit as well as exploratory model search.

Maintained by Sacha Epskamp. Last updated 13 days ago.

openblas cpp

1.7 match 51 stars 6.82 score 41 scripts 1 dependents

cran

ACDm:Tools for Autoregressive Conditional Duration Models

Package for Autoregressive Conditional Duration (ACD, Engle and Russell, 1998) models. Creates trade, price or volume durations from transactions (tic) data, performs diurnal adjustments, fits various ACD models and tests them.

Maintained by Markus Belfrage. Last updated 1 years ago.

5.3 match 3 stars 2.18 score 17 scripts 1 dependents

cran

SNSeg:Self-Normalization(SN) Based Change-Point Estimation for Time Series

Implementations self-normalization (SN) based algorithms for change-points estimation in time series data. This comprises nested local-window algorithms for detecting changes in both univariate and multivariate time series developed in Zhao, Jiang and Shao (2022) <doi:10.1111/rssb.12552>.

Maintained by Zifeng Zhao. Last updated 10 months ago.

cpp

4.8 match 1 stars 2.30 score

datarob

panelvar:Panel Vector Autoregression

We extend two general methods of moment estimators to panel vector autoregression models (PVAR) with p lags of endogenous variables, predetermined and strictly exogenous variables. This general PVAR model contains the first difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also provide specification tests (Hansen overidentification test, lag selection criterion and stability test of the PVAR polynomial) and classical structural analysis for PVAR models such as orthogonal and generalized impulse response functions, bootstrapped confidence intervals for impulse response analysis and forecast error variance decompositions.

Maintained by Robert Ferstl. Last updated 4 months ago.

3.8 match 9 stars 2.84 score 76 scripts

namgillee

VARshrink:Shrinkage Estimation Methods for Vector Autoregressive Models

Vector autoregressive (VAR) model is a fundamental and effective approach for multivariate time series analysis. Shrinkage estimation methods can be applied to high-dimensional VAR models with dimensionality greater than the number of observations, contrary to the standard ordinary least squares method. This package is an integrative package delivering nonparametric, parametric, and semiparametric methods in a unified and consistent manner, such as the multivariate ridge regression in Golub, Heath, and Wahba (1979) <doi:10.2307/1268518>, a James-Stein type nonparametric shrinkage method in Opgen-Rhein and Strimmer (2007) <doi:10.1186/1471-2105-8-S2-S3>, and Bayesian estimation methods using noninformative and informative priors in Lee, Choi, and S.-H. Kim (2016) <doi:10.1016/j.csda.2016.03.007> and Ni and Sun (2005) <doi:10.1198/073500104000000622>.

Maintained by Namgil Lee. Last updated 5 years ago.

3.4 match 3 stars 3.18 score 6 scripts

cran

msae:Multivariate Fay Herriot Models for Small Area Estimation

Implements multivariate Fay-Herriot models for small area estimation. It uses empirical best linear unbiased prediction (EBLUP) estimator. Multivariate models consider the correlation of several target variables and borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. Models which accommodated by this package are univariate model with several target variables (model 0), multivariate model (model 1), autoregressive multivariate model (model 2), and heteroscedastic autoregressive multivariate model (model 3). Functions provide EBLUP estimators and mean squared error (MSE) estimator for each model. These models were developed by Roberto Benavent and Domingo Morales (2015) <doi:10.1016/j.csda.2015.07.013>.

Maintained by Novia Permatasari. Last updated 3 years ago.

10.7 match 1.00 score

bioc

GenProSeq:Generating Protein Sequences with Deep Generative Models

Generative modeling for protein engineering is key to solving fundamental problems in synthetic biology, medicine, and material science. Machine learning has enabled us to generate useful protein sequences on a variety of scales. Generative models are machine learning methods which seek to model the distribution underlying the data, allowing for the generation of novel samples with similar properties to those on which the model was trained. Generative models of proteins can learn biologically meaningful representations helpful for a variety of downstream tasks. Furthermore, they can learn to generate protein sequences that have not been observed before and to assign higher probability to protein sequences that satisfy desired criteria. In this package, common deep generative models for protein sequences, such as variational autoencoder (VAE), generative adversarial networks (GAN), and autoregressive models are available. In the VAE and GAN, the Word2vec is used for embedding. The transformer encoder is applied to protein sequences for the autoregressive model.

Maintained by Dongmin Jung. Last updated 5 months ago.

software proteomics openjdk

2.6 match 4.00 score 3 scripts

thl2019

qfa:Quantile-Frequency Analysis (QFA) of Time Series

Quantile-frequency analysis (QFA) of time series based on trigonometric quantile regression. Spline quantile regression (SQR) for regression coefficient estimation. References: [1] Li, T.-H. (2012) "Quantile periodograms", Journal of the American Statistical Association, 107, 765–776, <doi:10.1080/01621459.2012.682815>. [2] Li, T.-H. (2014) Time Series with Mixed Spectra, CRC Press, <doi:10.1201/b15154> [3] Li, T.-H. (2022) "Quantile Fourier transform, quantile series, and nonparametric estimation of quantile spectra", <doi:10.48550/arXiv.2211.05844>. [4] Li, T.-H. (2024) "Quantile crossing spectrum and spline autoregression estimation," <doi:10.48550/arXiv.2412.02513>. [5] Li, T.-H. (2024) "Spline autoregression method for estimation of quantile spectrum", <doi:10.48550/arXiv.2412.17163>.

Maintained by Ta-Hsin Li. Last updated 2 months ago.

fortran openblas

7.9 match 1.34 score 11 scripts

sebkrantz

dfms:Dynamic Factor Models

Efficient estimation of Dynamic Factor Models using the Expectation Maximization (EM) algorithm or Two-Step (2S) estimation, supporting datasets with missing data. The estimation options follow advances in the econometric literature: either running the Kalman Filter and Smoother once with initial values from PCA - 2S estimation as in Doz, Giannone and Reichlin (2011) <doi:10.1016/j.jeconom.2011.02.012> - or via iterated Kalman Filtering and Smoothing until EM convergence - following Doz, Giannone and Reichlin (2012) <doi:10.1162/REST_a_00225> - or using the adapted EM algorithm of Banbura and Modugno (2014) <doi:10.1002/jae.2306>, allowing arbitrary patterns of missing data. The implementation makes heavy use of the 'Armadillo' 'C++' library and the 'collapse' package, providing for particularly speedy estimation. A comprehensive set of methods supports interpretation and visualization of the model as well as forecasting. Information criteria to choose the number of factors are also provided - following Bai and Ng (2002) <doi:10.1111/1468-0262.00273>.

Maintained by Sebastian Krantz. Last updated 6 months ago.

dynamic-factor-models time-series openblas cpp

1.9 match 31 stars 5.57 score 12 scripts

mpiktas

midasr:Mixed Data Sampling Regression

Methods and tools for mixed frequency time series data analysis. Allows estimation, model selection and forecasting for MIDAS regressions.

Maintained by Vaidotas Zemlys-Balevičius. Last updated 3 years ago.

1.8 match 77 stars 5.76 score 150 scripts

atsa-es

varlasso:Vector Autoregressive State Space Models With Shrinkage

The varlasso package uses Stan (mc-stan.org) to fit VAR state space models with optional shrinkage priors on B matrix elements (autoregression coefficients).

Maintained by Eric Ward. Last updated 2 years ago.

bayesian multivariate-timeseries time-series cpp

3.4 match 2 stars 3.00 score 2 scripts

cran

EXPARMA:Fitting of Exponential Autoregressive Moving Average (EXPARMA) Model

The amplitude-dependent autoregressive time series model (EXPAR) proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> was improved by incorporating the moving average (MA) framework for capturing the variability efficiently. Parameters of the EXPARMA model can be estimated using this package. The user is provided with the best fitted EXPARMA model for the data set under consideration.

Maintained by Bishal Gurung. Last updated 2 years ago.

10.2 match 1.00 score

cran

mAr:Multivariate AutoRegressive Analysis

R functions for the estimation and eigen-decomposition of multivariate autoregressive models.

Maintained by S. M. Barbosa. Last updated 3 years ago.

5.7 match 1 stars 1.78 score 2 dependents

ajmcneil

tscopula:Time Series Copula Models

Functions for the analysis of time series using copula models. The package is based on methodology described in the following references. McNeil, A.J. (2021) <doi:10.3390/risks9010014>, Bladt, M., & McNeil, A.J. (2021) <doi:10.1016/j.ecosta.2021.07.004>, Bladt, M., & McNeil, A.J. (2022) <doi:10.1515/demo-2022-0105>.

Maintained by Alexander McNeil. Last updated 25 days ago.

1.8 match 2 stars 5.53 score 12 scripts

swihart

repeated:Non-Normal Repeated Measurements Models

Various functions to fit models for non-normal repeated measurements, such as Binary Random Effects Models with Two Levels of Nesting, Bivariate Beta-binomial Regression Models, Marginal Bivariate Binomial Regression Models, Cormack capture-recapture models, Continuous-time Hidden Markov Chain Models, Discrete-time Hidden Markov Chain Models, Changepoint Location Models using a Continuous-time Two-state Hidden Markov Chain, generalized nonlinear autoregression models, multivariate Gaussian copula models, generalized non-linear mixed models with one random effect, generalized non-linear mixed models using h-likelihood for one random effect, Repeated Measurements Models for Counts with Frailty or Serial Dependence, Repeated Measurements Models for Continuous Variables with Frailty or Serial Dependence, Ordinal Random Effects Models with Dropouts, marginal homogeneity models for square contingency tables, correlated negative binomial models with Kalman update. References include Lindsey's text books, JK Lindsey (2001) <isbn:10-0198508123> and JK Lindsey (1999) <isbn:10-0198505590>.

Maintained by Bruce Swihart. Last updated 6 months ago.

fortran

2.5 match 3.97 score 31 scripts

cran

tseriesTARMA:Analysis of Nonlinear Time Series Through Threshold Autoregressive Moving Average Models (TARMA) Models

Routines for nonlinear time series analysis based on Threshold Autoregressive Moving Average (TARMA) models. It provides functions and methods for: TARMA model fitting and forecasting, including robust estimators, see Goracci et al. JBES (2025) <doi:10.1080/07350015.2024.2412011>; tests for threshold effects, see Giannerini et al. JoE (2024) <doi:10.1016/j.jeconom.2023.01.004>, Goracci et al. Statistica Sinica (2023) <doi:10.5705/ss.202021.0120>, Angelini et al. (2024) <doi:10.48550/arXiv.2308.00444>; unit-root tests based on TARMA models, see Chan et al. Statistica Sinica (2024) <doi:10.5705/ss.202022.0125>.

Maintained by Simone Giannerini. Last updated 5 months ago.

fortran openblas

3.2 match 3.06 score

franciscomartinezdelrio

tsfgrnn:Time Series Forecasting Using GRNN

A general regression neural network (GRNN) is a variant of a Radial Basis Function Network characterized by a fast single-pass learning. 'tsfgrnn' allows you to forecast time series using a GRNN model Francisco Martinez et al. (2019) <doi:10.1007/978-3-030-20521-8_17> and Francisco Martinez et al. (2022) <doi:10.1016/j.neucom.2021.12.028>. When the forecasting horizon is higher than 1, two multi-step ahead forecasting strategies can be used. The model built is autoregressive, that is, it is only based on the observations of the time series. You can consult and plot how the prediction was done. It is also possible to assess the forecasting accuracy of the model using rolling origin evaluation.

Maintained by Francisco Martinez. Last updated 1 years ago.

cpp

2.0 match 8 stars 4.83 score 17 scripts

akai01

caretForecast:Conformal Time Series Forecasting Using State of Art Machine Learning Algorithms

Conformal time series forecasting using the caret infrastructure. It provides access to state-of-the-art machine learning models for forecasting applications. The hyperparameter of each model is selected based on time series cross-validation, and forecasting is done recursively.

Maintained by Resul Akay. Last updated 2 years ago.

caret conformal-prediction data-science econometrics forecast forecasting forecasting-models machine-learning macroeconometrics microeconometrics time-series time-series-forcasting time-series-prediction

1.7 match 25 stars 5.62 score 28 scripts 4 dependents

r-cas

Ryacas0:Legacy 'Ryacas' (Interface to 'Yacas' Computer Algebra System)

A legacy version of 'Ryacas', an interface to the 'yacas' computer algebra system (<http://www.yacas.org/>).

Maintained by Mikkel Meyer Andersen. Last updated 2 years ago.

cpp

1.3 match 2 stars 7.11 score 36 scripts 6 dependents

fate-ewi

bayesdfa:Bayesian Dynamic Factor Analysis (DFA) with 'Stan'

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.

Maintained by Eric J. Ward. Last updated 5 months ago.

cpp

1.1 match 28 stars 8.28 score 101 scripts

jsocolar

flocker:Flexible Occupancy Estimation with Stan

Fit occupancy models in 'Stan' via 'brms'. The full variety of 'brms' formula-based effects structures are available to use in multiple classes of occupancy model, including single-season models, models with data augmentation for never-observed species, dynamic (multiseason) models with explicit colonization and extinction processes, and dynamic models with autologistic occupancy dynamics. Formulas can be specified for all relevant distributional terms, including detection and one or more of occupancy, colonization, extinction, and autologistic depending on the model type. Several important forms of model post-processing are provided. References: Bürkner (2017) <doi:10.18637/jss.v080.i01>; Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Socolar & Mills (2023) <doi:10.1101/2023.10.26.564080>.

Maintained by Jacob B. Socolar. Last updated 2 months ago.

1.3 match 30 stars 6.78 score 20 scripts

smac-group

wv:Wavelet Variance

Provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <arXiv:1607.05858>.

Maintained by Stéphane Guerrier. Last updated 2 years ago.

signal-processing time-series wavelet-variance openblas cpp

1.7 match 17 stars 5.43 score 15 scripts 1 dependents

mfaymon

spINAR:(Semi)Parametric Estimation and Bootstrapping of INAR Models

Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) <doi:10.1007/s10260-022-00655-0>) for the semiparametric setting introduced in Drost et al. (2009) <doi:10.1111/j.1467-9868.2008.00687.x>, different procedures to bootstrap INAR data (Jentsch, C. and Weiß, C.H. (2017) <doi:10.3150/18-BEJ1057>) and flexible simulation of INAR data.

Maintained by Maxime Faymonville. Last updated 10 months ago.

bootstrapping count-data parametric-estimation penalization semiparametric-estimation simulation time-series validation

1.7 match 4 stars 5.20 score 7 scripts

franciscomartinezdelrio

utsf:Univariate Time Series Forecasting

An engine for univariate time series forecasting using different regression models in an autoregressive way. The engine provides an uniform interface for applying the different models. Furthermore, it is extensible so that users can easily apply their own regression models to univariate time series forecasting and benefit from all the features of the engine, such as preprocessings or estimation of forecast accuracy.

Maintained by Francisco Martinez. Last updated 28 days ago.

1.7 match 2 stars 5.23 score 4 scripts

james-thorson-noaa

dsem:Fit Dynamic Structural Equation Models

Applies dynamic structural equation models to time-series data with generic and simplified specification for simultaneous and lagged effects. Methods are described in Thorson et al. (2024) "Dynamic structural equation models synthesize ecosystem dynamics constrained by ecological mechanisms."

Maintained by James Thorson. Last updated 6 days ago.

cpp openmp

1.3 match 11 stars 6.90 score 24 scripts

techtonique

ahead:Time Series Forecasting with uncertainty quantification

Univariate and multivariate time series forecasting with uncertainty quantification.

Maintained by T. Moudiki. Last updated 28 days ago.

forecasting machine-learning predictive-modeling statistical-learning time-series time-series-forecasting uncertainty-quantification cpp

1.8 match 21 stars 4.77 score 51 scripts

cran

VGAMextra:Additions and Extensions of the 'VGAM' Package

Extending the functionalities of the 'VGAM' package with additional functions and datasets. At present, 'VGAMextra' comprises new family functions (ffs) to estimate several time series models by maximum likelihood using Fisher scoring, unlike popular packages in CRAN relying on optim(), including ARMA-GARCH-like models, the Order-(p, d, q) ARIMAX model (non- seasonal), the Order-(p) VAR model, error correction models for cointegrated time series, and ARMA-structures with Student-t errors. For independent data, new ffs to estimate the inverse- Weibull, the inverse-gamma, the generalized beta of the second kind and the general multivariate normal distributions are available. In addition, 'VGAMextra' incorporates new VGLM-links for the mean-function, and the quantile-function (as an alternative to ordinary quantile modelling) of several 1-parameter distributions, that are compatible with the class of VGLM/VGAM family functions. Currently, only fixed-effects models are implemented. All functions are subject to change; see the NEWS for further details on the latest changes.

Maintained by Victor Miranda. Last updated 1 months ago.

6.5 match 1 stars 1.26 score

rominsal

pspatreg:Spatial and Spatio-Temporal Semiparametric Regression Models with Spatial Lags

Estimation and inference of spatial and spatio-temporal semiparametric models including spatial or spatio-temporal non-parametric trends, parametric and non-parametric covariates and, possibly, a spatial lag for the dependent variable and temporal correlation in the noise. The spatio-temporal trend can be decomposed in ANOVA way including main and interaction functional terms. Use of SAP algorithm to estimate the spatial or spatio-temporal trend and non-parametric covariates. The methodology of these models can be found in next references Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>; Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and, particularly referred to the focus of the package, Minguez, R., Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.

Maintained by Roman Minguez. Last updated 3 years ago.

1.3 match 12 stars 6.44 score 77 scripts

smac-group

avar:Allan Variance

Implements the allan variance and allan variance linear regression estimator for latent time series models. More details about the method can be found, for example, in Guerrier, S., Molinari, R., & Stebler, Y. (2016) <doi:10.1109/LSP.2016.2541867>.

Maintained by Stéphane Guerrier. Last updated 3 years ago.

allan-variance inertial-sensors statistics time-series cpp

1.7 match 5 stars 4.88 score 9 scripts

andreamrau

ebdbNet:Empirical Bayes Estimation of Dynamic Bayesian Networks

Infer the adjacency matrix of a network from time course data using an empirical Bayes estimation procedure based on Dynamic Bayesian Networks.

Maintained by Andrea Rau. Last updated 2 years ago.

openblas

1.9 match 4 stars 4.28 score 19 scripts

hugogogo

varband:Variable Banding of Large Precision Matrices

Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at <https://arxiv.org/abs/1604.07451>.

Maintained by Guo Yu. Last updated 7 years ago.

openblas cpp

2.0 match 2 stars 4.00 score 10 scripts

lhvanegasp

mtarm:Bayesian Estimation of Multivariate Threshold Autoregressive Models

Estimation, inference and forecasting using the Bayesian approach for multivariate threshold autoregressive (TAR) models in which the distribution used to describe the noise process belongs to the class of Gaussian variance mixtures.

Maintained by Luis Hernando Vanegas. Last updated 8 months ago.

5.2 match 1.48 score 1 scripts

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 9 months ago.

bayesian stan time-series cpp

1.3 match 48 stars 5.68 score 33 scripts

obriet

gsarima:Two Functions for Generalized SARIMA Time Series Simulation

Write SARIMA models in (finite) AR representation and simulate generalized multiplicative seasonal autoregressive moving average (time) series with Normal / Gaussian, Poisson or negative binomial distribution. The methodology of this method is described in Briet OJT, Amerasinghe PH, and Vounatsou P (2013) <doi:10.1371/journal.pone.0065761>.

Maintained by Olivier Briet. Last updated 5 years ago.

4.1 match 1.78 score 3 scripts 2 dependents

susanabarbosa

ArDec:Time Series Autoregressive-Based Decomposition

Autoregressive-based decomposition of a time series based on the approach in West (1997). Particular cases include the extraction of trend and seasonal components.

Maintained by Susana Barbosa. Last updated 3 years ago.

7.2 match 1.00 score 5 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 1 years ago.

2.3 match 2 stars 3.18 score 127 scripts

cran

sym.arma:Autoregressive and Moving Average Symmetric Models

Functions for fitting the Autoregressive and Moving Average Symmetric Model for univariate time series introduced by Maior and Cysneiros (2018), <doi:10.1007/s00362-016-0753-z>. Fitting method: conditional maximum likelihood estimation. For details see: Wei (2006), Time Series Analysis: Univariate and Multivariate Methods, Section 7.2.

Maintained by Vinicius Quintas Souto Maior. Last updated 6 years ago.

7.1 match 1.00 score

takouajendoubi

iCARH:Integrative Conditional Autoregressive Horseshoe Model

Implements the integrative conditional autoregressive horseshoe model discussed in Jendoubi, T., Ebbels, T.M. Integrative analysis of time course metabolic data and biomarker discovery. BMC Bioinformatics 21, 11 (2020) <doi:10.1186/s12859-019-3333-0>. The model consists in three levels: Metabolic pathways level modeling interdependencies between variables via a conditional auto-regressive (CAR) component, integrative analysis level to identify potential associations between heterogeneous omic variables via a Horseshoe prior and experimental design level to capture experimental design conditions through a mixed-effects model. The package also provides functions to simulate data from the model, construct pathway matrices, post process and plot model parameters.

Maintained by Takoua Jendoubi. Last updated 5 years ago.

3.5 match 2.00 score 6 scripts

edmhlin

BAYSTAR:On Bayesian Analysis of Threshold Autoregressive Models

Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods.

Maintained by Edward M.H. Lin. Last updated 3 years ago.

5.3 match 2 stars 1.30 score 4 scripts

tidyverts

fable:Forecasting Models for Tidy Time Series

Provides a collection of commonly used univariate and multivariate time series forecasting models including automatically selected exponential smoothing (ETS) and autoregressive integrated moving average (ARIMA) models. These models work within the 'fable' framework provided by the 'fabletools' package, which provides the tools to evaluate, visualise, and combine models in a workflow consistent with the tidyverse.

Maintained by Mitchell OHara-Wild. Last updated 4 months ago.

forecasting cpp

0.5 match 565 stars 13.52 score 2.1k scripts 6 dependents

jeksterslab

bootStateSpace:Bootstrap for State Space Models

Provides a streamlined and user-friendly framework for bootstrapping in state space models, particularly when the number of subjects/units (n) exceeds one, a scenario commonly encountered in social and behavioral sciences. For an introduction to state space models in social and behavioral sciences, refer to Chow, Ho, Hamaker, and Dolan (2010) <doi:10.1080/10705511003661553>.

Maintained by Ivan Jacob Agaloos Pesigan. Last updated 30 days ago.

bootstrap state-space-model

1.7 match 4.01 score 51 scripts

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 Vieira dos Santos Junior. Last updated 4 days ago.

1.2 match 2 stars 5.70 score 9 scripts

stscl

sesp:Spatially Explicit Stratified Power

Assesses spatial associations between variables through an equivalent geographical detector (q-statistic) within a regression framework and incorporates a spatially explicit stratified power model by integrating spatial dependence and spatial stratified heterogeneity, facilitating the modeling of complex spatial relationships.

Maintained by Wenbo Lv. Last updated 2 months ago.

spatial-explicit-geographical-detector spatial-stratified-heterogeneity cpp

1.3 match 15 stars 5.43 score

cran

DBfit:A Double Bootstrap Method for Analyzing Linear Models with Autoregressive Errors

Computes the double bootstrap as discussed in McKnight, McKean, and Huitema (2000) <doi:10.1037/1082-989X.5.1.87>. The double bootstrap method provides a better fit for a linear model with autoregressive errors than ARIMA when the sample size is small.

Maintained by Shaofeng Zhang. Last updated 4 years ago.

6.8 match 1.00 score

eiotoakhia

ardl.nardl:Linear and Nonlinear Autoregressive Distributed Lag Models: General-to-Specific Approach

Estimate the linear and nonlinear autoregressive distributed lag (ARDL & NARDL) models and the corresponding error correction models, and test for longrun and short-run asymmetric. The general-to-specific approach is also available in estimating the ARDL and NARDL models. The Pesaran, Shin & Smith (2001) (<doi:10.1002/jae.616>) bounds test for level relationships is also provided. The 'ardl.nardl' package also performs short-run and longrun symmetric restrictions available at Shin et al. (2014) <doi:10.1007/978-1-4899-8008-3_9> and their corresponding tests.

Maintained by Eric I. Otoakhia. Last updated 1 years ago.

6.7 match 1 stars 1.00 score

cran

GVARX:Perform Global Vector Autoregression Estimation and Inference

Light procedures for learning Global Vector Autoregression model (GVAR) of Pesaran, Schuermann and Weiner (2004) <DOI:10.1198/073500104000000019> and Dees, di Mauro, Pesaran and Smith (2007) <DOI:10.1002/jae.932>.

Maintained by Ho Tsung-wu. Last updated 2 years ago.

3.4 match 5 stars 1.90 score 16 scripts

objornstad

nlts:Nonlinear Time Series Analysis

R functions for (non)linear time series analysis with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.

Maintained by Ottar N. Bjornstad. Last updated 2 years ago.

2.2 match 1 stars 3.00 score 10 scripts

alexanderjwhite

mixedLSR:Mixed, Low-Rank, and Sparse Multivariate Regression on High-Dimensional Data

Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when the coefficient matrix is low-rank and sparse. 'mixedLSR' allows subgroup identification by alternating optimization with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically performing parameter selection to identify low-rank substructures in the coefficient matrix.

Maintained by Alexander White. Last updated 2 years ago.

1.7 match 3.70 score 2 scripts

helske

particlefield:Sequential Monte Carlo for Latent Conditional Autoregressive Model

Functions for replicating the results of the latent Gaussian Markov random field experiment of Lindsten, Helske, Vihola (2018), XX. Contains also functions for performing particle Markov chain Monte Carlo estimation of the model parameters.

Maintained by Jouni Helske. Last updated 4 years ago.

cpp

2.9 match 3 stars 2.18 score 4 scripts

mnrzrad

TestIndVars:Testing the Independence of Variables for Specific Covariance Structures

Test the nullity of covariances, in a set of variables, using a simple univariate procedure. See Marques, Diogo, Norouzirad, Bispo (2023) <doi:10.1002/mma.9130>.

Maintained by Mina Norouzirad. Last updated 9 months ago.

1.8 match 3.48 score

cran

acp:Autoregressive Conditional Poisson

Analysis of count data exhibiting autoregressive properties, using the Autoregressive Conditional Poisson model (ACP(p,q)) proposed by Heinen (2003).

Maintained by Siakoulis Vasilios. Last updated 9 years ago.

6.0 match 1.00 score

cran

beyondWhittle:Bayesian Spectral Inference for Time Series

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

Maintained by Renate Meyer. Last updated 4 months ago.

openblas cpp

3.5 match 2 stars 1.68 score 12 scripts

funwithr

LongMemoryTS:Long Memory Time Series

Long Memory Time Series is a collection of functions for estimation, simulation and testing of long memory processes, spurious long memory processes and fractionally cointegrated systems.

Maintained by Christian Leschinski. Last updated 6 years ago.

openblas cpp

1.8 match 2 stars 3.40 score 42 scripts 1 dependents

cran

aTSA:Alternative Time Series Analysis

Contains some tools for testing, analyzing time series data and fitting popular time series models such as ARIMA, Moving Average and Holt Winters, etc. Most functions also provide nice and clear outputs like SAS does, such as identify, estimate and forecast, which are the same statements in PROC ARIMA in SAS.

Maintained by Debin Qiu. Last updated 1 years ago.

2.0 match 1 stars 2.98 score 5 dependents

bsiepe

tsnet:Fitting, Comparing, and Visualizing Networks Based on Time Series Data

Fit, compare, and visualize Bayesian graphical vector autoregressive (GVAR) network models using 'Stan'. These models are commonly used in psychology to represent temporal and contemporaneous relationships between multiple variables in intensive longitudinal data. Fitted models can be compared with a test based on matrix norm differences of posterior point estimates to quantify the differences between two estimated networks. See also Siepe, Kloft & Heck (2024) <doi:10.31234/osf.io/uwfjc>.

Maintained by Björn S. Siepe. Last updated 5 months ago.

cpp

2.2 match 1 stars 2.70 score 9 scripts

cran

PNAR:Poisson Network Autoregressive Models

Quasi likelihood-based methods for estimating linear and log-linear Poisson Network Autoregression models with p lags and covariates. Tools for testing the linearity versus several non-linear alternatives. Tools for simulation of multivariate count distributions, from linear and non-linear PNAR models, by using a specific copula construction. References include: Armillotta, M. and K. Fokianos (2023). "Nonlinear network autoregression". Annals of Statistics, 51(6): 2526--2552. <doi:10.1214/23-AOS2345>. Armillotta, M. and K. Fokianos (2024). "Count network autoregression". Journal of Time Series Analysis, 45(4): 584--612. <doi:10.1111/jtsa.12728>. Armillotta, M., Tsagris, M. and Fokianos, K. (2024). "Inference for Network Count Time Series with the R Package PNAR". The R Journal, 15/4: 255--269. <doi:10.32614/RJ-2023-094>.

Maintained by Michail Tsagris. Last updated 6 months ago.

5.9 match 1.00 score

ovvo-financial

NNS:Nonlinear Nonparametric Statistics

Nonlinear nonparametric statistics using partial moments. Partial moments are the elements of variance and asymptotically approximate the area of f(x). These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences. NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic dominance and Advanced Monte Carlo sampling. All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).

Maintained by Fred Viole. Last updated 6 days ago.

clustering econometrics machine-learning nonlinear nonparametric partial-moments statistics time-series cpp

0.5 match 71 stars 10.96 score 66 scripts 3 dependents

f-rousset

mafR:Interface for Masked Autoregressive Flows

Interfaces the Python library 'zuko' implementing Masked Autoregressive Flows. See Rozet, Divo and Schnake (2023) <doi:10.5281/zenodo.7625672> and Papamakarios, Pavlakou and Murray (2017) <doi:10.48550/arXiv.1705.07057>.

Maintained by François Rousset. Last updated 6 months ago.

5.5 match 1.00 score

crisvarin

lacm:Latent Autoregressive Count Models

Perform pairwise likelihood inference in latent autoregressive count models. See Pedeli and Varin (2020) for details.

Maintained by Cristiano Varin. Last updated 5 years ago.

5.4 match 1.00 score 7 scripts

cran

AutoregressionMDE:Minimum Distance Estimation in Autoregressive Model

Consider autoregressive model of order p where the distribution function of innovation is unknown, but innovations are independent and symmetrically distributed. The package contains a function named ARMDE which takes X (vector of n observations) and p (order of the model) as input argument and returns minimum distance estimator of the parameters in the model.

Maintained by Jiwoong Kim. Last updated 10 years ago.

5.3 match 1.00 score

gtromano

DeCAFS:Detecting Changes in Autocorrelated and Fluctuating Signals

Detect abrupt changes in time series with local fluctuations as a random walk process and autocorrelated noise as an AR(1) process. See Romano, G., Rigaill, G., Runge, V., Fearnhead, P. (2021) <doi:10.1080/01621459.2021.1909598>.

Maintained by Gaetano Romano. Last updated 2 years ago.

change-detection changepoint-detection time-series-analysis cpp

1.8 match 2 stars 3.00 score 2 scripts

cran

bentcableAR:Bent-Cable Regression for Independent Data or Autoregressive Time Series

Included are two main interfaces, bentcable.ar() and bentcable.dev.plot(), for fitting and diagnosing bent-cable regressions for autoregressive time-series data (Chiu and Lockhart 2010, <doi:10.1002/cjs.10070>) or independent data (time series or otherwise - Chiu, Lockhart and Routledge 2006, <doi:10.1198/016214505000001177>). Some components in the package can also be used as stand-alone functions. The bent cable (linear-quadratic-linear) generalizes the broken stick (linear-linear), which is also handled by this package. Version 0.2 corrected a glitch in the computation of confidence intervals for the CTP. References that were updated from Versions 0.2.1 and 0.2.2 appear in Version 0.2.3 and up. Version 0.3.0 improved robustness of the error-message producing mechanism. Version 0.3.1 improves the NAMESPACE file of the package. It is the author's intention to distribute any future updates via GitHub.

Maintained by Grace Chiu. Last updated 3 years ago.

5.1 match 1 stars 1.00 score

dnychka

LatticeKrig:Multi-Resolution Kriging Based on Markov Random Fields

Methods for the interpolation of large spatial datasets. This package uses a basis function approach that provides a surface fitting method that can approximate standard spatial data models. Using a large number of basis functions allows for estimates that can come close to interpolating the observations (a spatial model with a small nugget variance.) Moreover, the covariance model for this method can approximate the Matern covariance family but also allows for a multi-resolution model and supports efficient computation of the profile likelihood for estimating covariance parameters. This is accomplished through compactly supported basis functions and a Markov random field model for the basis coefficients. These features lead to sparse matrices for the computations and this package makes of the R spam package for sparse linear algebra. An extension of this version over previous ones ( < 5.4 ) is the support for different geometries besides a rectangular domain. The Markov random field approach combined with a basis function representation makes the implementation of different geometries simple where only a few specific R functions need to be added with most of the computation and evaluation done by generic routines that have been tuned to be efficient. One benefit of this package's model/approach is the facility to do unconditional and conditional simulation of the field for large numbers of arbitrary points. There is also the flexibility for estimating non-stationary covariances and also the case when the observations are a linear combination (e.g. an integral) of the spatial process. Included are generic methods for prediction, standard errors for prediction, plotting of the estimated surface and conditional and unconditional simulation. See the 'LatticeKrigRPackage' GitHub repository for a vignette of this package. Development of this package was supported in part by the National Science Foundation Grant 1417857 and the National Center for Atmospheric Research.

Maintained by Douglas Nychka. Last updated 5 months ago.

fortran

1.8 match 2.89 score 130 scripts 1 dependents

curiousxx

crosslag:Perform Linear or Nonlinear Cross Lag Analysis

Linear or nonlinear cross-lagged panel model can be built from input data. Users can choose the appropriate method from three methods for constructing nonlinear cross lagged models. These three methods include polynomial regression, generalized additive model and generalized linear mixed model.In addition, a function for determining linear relationships is provided. Relevant knowledge of cross lagged models can be learned through the paper by Fredrik Falkenström (2024) <doi:10.1016/j.cpr.2024.102435> and the paper by A Gasparrini (2010) <doi:10.1002/sim.3940>.

Maintained by Yaxin Li. Last updated 10 months ago.

5.1 match 1.00 score 4 scripts

cran

SparseTSCGM:Sparse Time Series Chain Graphical Models

Computes sparse vector autoregressive coefficients and precision matrices for time series chain graphical models. Fentaw Abegaz and Ernst Wit (2013) <doi:10.1093/biostatistics/kxt005>.

Maintained by Fentaw Abegaz. Last updated 4 years ago.

3.8 match 2 stars 1.30 score

paulsmirnov

robcor:Robust Correlations

Robust pairwise correlations based on estimates of scale, particularly on "FastQn" one-step M-estimate.

Maintained by Paul Smirnov. Last updated 3 years ago.

1.8 match 2.58 score 21 scripts 6 dependents

korry74

LPM:Linear Parametric Models Applied to Hydrological Series

Apply Univariate Long Memory Models, Apply Multivariate Short Memory Models To Hydrological Dataset, Estimate Intensity Duration Frequency curve to rainfall series. NEW -- Calculate the monthly water requirement for herbaceous and arboreal plants.

Maintained by Corrado Tallerini. Last updated 9 months ago.

2.0 match 2.18 score 10 scripts

jwboys26

KoulMde:Koul's Minimum Distance Estimation in Regression and Image Segmentation Problems

Many methods are developed to deal with two major statistical problems: image segmentation and nonparametric estimation in various regression models. Image segmentation is nowadays gaining a lot of attention from various scientific subfields. Especially, image segmentation has been popular in medical research such as magnetic resonance imaging (MRI) analysis. When a patient suffers from some brain diseases such as dementia and Parkinson's disease, those diseases can be easily diagnosed in brain MRI: the area affected by those diseases is brightly expressed in MRI, which is called a white lesion. For the purpose of medical research, locating and segment those white lesions in MRI is a critical issue; it can be done manually. However, manual segmentation is very expensive in that it is error-prone and demands a huge amount of time. Therefore, supervised machine learning has emerged as an alternative solution. Despite its powerful performance in a classification problem such as hand-written digits, supervised machine learning has not shown the same satisfactory result in MRI analysis. Setting aside all issues of the supervised machine learning, it exposed a critical problem when employed for MRI analysis: it requires time-consuming data labeling. Thus, there is a strong demand for an unsupervised approach, and this package - based on Hira L. Koul (1986) <DOI:10.1214/aos/1176350059> - proposes an efficient method for simple image segmentation - here, "simple" means that an image is black-and-white - which can easily be applied to MRI analysis. This package includes a function GetSegImage(): when a black-and-white image is given as an input, GetSegImage() separates an area of white pixels - which corresponds to a white lesion in MRI - from the given image. For the second problem, consider linear regression model and autoregressive model of order q where errors in the linear regression model and innovations in the autoregression model are independent and symmetrically distributed. Hira L. Koul (1986) <DOI:10.1214/aos/1176350059> proposed a nonparametric minimum distance estimation method by minimizing L2-type distance between certain weighted residual empirical processes. He also proposed a simpler version of the loss function by using symmetry of the integrating measure in the distance. Kim (2018) <DOI:10.1080/00949655.2017.1392527> proposed a fast computational method which enables practitioners to compute the minimum distance estimator of the vector of general multiple regression parameters for several integrating measures. This package contains three functions: KoulLrMde(), KoulArMde(), and Koul2StageMde(). The former two provide minimum distance estimators for linear regression model and autoregression model, respectively, where both are based on Koul's method. These two functions take much less time for the computation than those based on parametric minimum distance estimation methods. Koul2StageMde() provides estimators for regression and autoregressive coefficients of linear regression model with autoregressive errors through minimum distant method of two stages. The new version is written in Rcpp and dramatically reduces computational time.

Maintained by Jiwoong Kim. Last updated 5 years ago.

openblas cpp

4.3 match 1.00 score 3 scripts

mhunter1

EasyMx:Easy Model-Builder Functions for 'OpenMx'

Utilities for building certain kinds of common matrices and models in the extended structural equation modeling package, 'OpenMx'.

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

1.8 match 2.32 score 21 scripts

felipeelorrieta

iAR:Irregularly Observed Autoregressive Models

Data sets, functions and scripts with examples to implement autoregressive models for irregularly observed time series. The models available in this package are the irregular autoregressive model (Eyheramendy et al.(2018) <doi:10.1093/mnras/sty2487>), the complex irregular autoregressive model (Elorrieta et al.(2019) <doi:10.1051/0004-6361/201935560>) and the bivariate irregular autoregressive model (Elorrieta et al.(2021) <doi:10.1093/mnras/stab1216>).

Maintained by Elorrieta Felipe. Last updated 7 days ago.

openblas cpp openmp

4.1 match 1.00 score

mortamini

hhsmm:Hidden Hybrid Markov/Semi-Markov Model Fitting

Develops algorithms for fitting, prediction, simulation and initialization of the hidden hybrid Markov/semi-Markov model, introduced by Guedon (2005) <doi:10.1016/j.csda.2004.05.033>, which also includes several tools for handling missing data, nonparametric mixture of B-splines emissions (Langrock et al., 2015 <doi:10.1111/biom.12282>), fitting regime switching regression (Kim et al., 2008 <doi:10.1016/j.jeconom.2007.10.002>) and auto-regressive hidden hybrid Markov/semi-Markov model, and many other useful tools (read for more description: <arXiv:2109.12489>).

Maintained by Morteza Amini. Last updated 3 years ago.

1.6 match 3 stars 2.48 score 5 scripts

javlacalle

tsdecomp:Decomposition of Time Series Data

ARIMA-model-based decomposition of quarterly and monthly time series data. The methodology is developed and described, among others, in Burman (1980) <DOI:10.2307/2982132> and Hillmer and Tiao (1982) <DOI:10.2307/2287770>.

Maintained by Javier López-de-Lacalle. Last updated 8 years ago.

2.0 match 2.00 score

ddisab01

quest:Prepare Questionnaire Data for Analysis

Offers a suite of functions to prepare questionnaire data for analysis (perhaps other types of data as well). By data preparation, I mean data analytic tasks to get your raw data ready for statistical modeling (e.g., regression). There are functions to investigate missing data, reshape data, validate responses, recode variables, score questionnaires, center variables, aggregate by groups, shift scores (i.e., leads or lags), etc. It provides functions for both single level and multilevel (i.e., grouped) data. With a few exceptions (e.g., ncases()), functions without an "s" at the end of their primary word (e.g., center_by()) act on atomic vectors, while functions with an "s" at the end of their primary word (e.g., centers_by()) act on multiple columns of a data.frame.

Maintained by David Disabato. Last updated 1 years ago.

2.0 match 1.98 score 12 scripts

nredell

forecastML:Time Series Forecasting with Machine Learning Methods

The purpose of 'forecastML' is to simplify the process of multi-step-ahead forecasting with standard machine learning algorithms. 'forecastML' supports lagged, dynamic, static, and grouping features for modeling single and grouped numeric or factor/sequence time series. In addition, simple wrapper functions are used to support model-building with most R packages. This approach to forecasting is inspired by Bergmeir, Hyndman, and Koo's (2018) paper "A note on the validity of cross-validation for evaluating autoregressive time series prediction" <doi:10.1016/j.csda.2017.11.003>.

Maintained by Nickalus Redell. Last updated 5 years ago.

deep-learning direct-forecasting forecast forecasting machine-learning multi-step-ahead-forecasting neural-network python time-series

0.5 match 131 stars 7.64 score 134 scripts

cran

ARpLMEC:Censored Mixed-Effects Models with Different Correlation Structures

Left, right or interval censored mixed-effects linear model with autoregressive errors of order p or DEC correlation structure using the type-EM algorithm. The error distribution can be Normal or t-Student. It provides the parameter estimates, the standard errors and prediction of future observations (available only for the normal case). Olivari et all (2021) <doi:10.1080/10543406.2020.1852246>.

Maintained by Rommy C. Olivari. Last updated 3 years ago.

3.8 match 1.00 score 3 scripts

cran

freqdom:Frequency Domain Based Analysis: Dynamic PCA

Implementation of dynamic principal component analysis (DPCA), simulation of VAR and VMA processes and frequency domain tools. These frequency domain methods for dimensionality reduction of multivariate time series were introduced by David Brillinger in his book Time Series (1974). We follow implementation guidelines as described in Hormann, Kidzinski and Hallin (2016), Dynamic Functional Principal Component <doi:10.1111/rssb.12076>.

Maintained by Kidzinski L.. Last updated 11 months ago.

1.8 match 2.08 score 4 dependents

kylecaudle

LTAR:Tensor Forecasting Functions

A set of tools for forecasting the next step in a multidimensional setting using tensors. In the examples, a forecast is made of sea surface temperatures of a geographic grid (i.e. lat/long). Each observation is a matrix, the entries in the matrix and the sea surface temperature at a particular lattitude/longitude. Cates, J., Hoover, R. C., Caudle, K., Kopp, R., & Ozdemir, C. (2021) "Transform-Based Tensor Auto Regression for Multilinear Time Series Forecasting" in 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 461-466), IEEE <doi:10.1109/ICMLA52953.2021.00078>.

Maintained by Kyle Caudle. Last updated 2 years ago.

3.7 match 1.00 score

cran

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.

3.7 match 1.00 score

alexanderlange53

svars:Data-Driven Identification of SVAR Models

Implements data-driven identification methods for structural vector autoregressive (SVAR) models as described in Lange et al. (2021) <doi:10.18637/jss.v097.i05>. Based on an existing VAR model object (provided by e.g. VAR() from the 'vars' package), the structural impact matrix is obtained via data-driven identification techniques (i.e. changes in volatility (Rigobon, R. (2003) <doi:10.1162/003465303772815727>), patterns of GARCH (Normadin, M., Phaneuf, L. (2004) <doi:10.1016/j.jmoneco.2003.11.002>), independent component analysis (Matteson, D. S, Tsay, R. S., (2013) <doi:10.1080/01621459.2016.1150851>), least dependent innovations (Herwartz, H., Ploedt, M., (2016) <doi:10.1016/j.jimonfin.2015.11.001>), smooth transition in variances (Luetkepohl, H., Netsunajev, A. (2017) <doi:10.1016/j.jedc.2017.09.001>) or non-Gaussian maximum likelihood (Lanne, M., Meitz, M., Saikkonen, P. (2017) <doi:10.1016/j.jeconom.2016.06.002>)).

Maintained by Alexander Lange. Last updated 2 years ago.

openblas cpp openmp

0.5 match 46 stars 7.22 score 130 scripts

cran

SAGM:Spatial Autoregressive Graphical Model

Implements the methodological developments found in Hermes, van Heerwaarden, and Behrouzi (2023) <doi:10.48550/arXiv.2308.04325>, and allows for the statistical modeling of asymmetric between-location effects, as well as within-location effects using spatial autoregressive graphical models. The package allows for the generation of spatial weight matrices to capture asymmetric effects for strip-type intercropping designs, although it can handle any type of spatial data commonly found in other sciences.

Maintained by Sjoerd Hermes. Last updated 1 years ago.

3.6 match 1.00 score

tsmodels

tsgarch:Univariate GARCH Models

Multiple flavors of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model with a large choice of conditional distributions. Methods for specification, estimation, prediction, filtering, simulation, statistical testing and more. Represents a partial re-write and re-think of 'rugarch', making use of automatic differentiation for estimation.

Maintained by Alexios Galanos. Last updated 3 months ago.

garch cpp

0.5 match 13 stars 6.93 score 16 scripts 1 dependents

saikathd

EXPAR:Fitting of Exponential Autoregressive (EXPAR) Model

The amplitude-dependent exponential autoregressive (EXPAR) time series model, initially proposed by Haggan and Ozaki (1981) <doi:10.2307/2335819> has been implemented in this package. Throughout various studies, the model has been found to adequately capture the cyclical nature of datasets. Parameter estimation of such family of models has been tackled by the approach of minimizing the residual sum of squares (RSS). Model selection among various candidate orders has been implemented using various information criteria, viz., Akaike information criteria (AIC), corrected Akaike information criteria (AICc) and Bayesian information criteria (BIC). An illustration utilizing data of egg price indices has also been provided.

Maintained by Saikath Das. Last updated 10 months ago.

3.5 match 1.00 score

natsiopoulos

ARDL:ARDL, ECM and Bounds-Test for Cointegration

Creates complex autoregressive distributed lag (ARDL) models and constructs the underlying unrestricted and restricted error correction model (ECM) automatically, just by providing the order. It also performs the bounds-test for cointegration as described in Pesaran et al. (2001) <doi:10.1002/jae.616> and provides the multipliers and the cointegrating equation. The validity and the accuracy of this package have been verified by successfully replicating the results of Pesaran et al. (2001) in Natsiopoulos and Tzeremes (2022) <doi:10.1002/jae.2919>.

Maintained by Kleanthis Natsiopoulos. Last updated 2 years ago.

0.5 match 18 stars 6.64 score 86 scripts 1 dependents

cran

VMDML:Variational Mode Decomposition Based Machine Learning Models

Application of Variational Mode Decomposition based different Machine Learning models for univariate time series forecasting. For method details see (i) K. Dragomiretskiy and D. Zosso (2014) <doi:10.1109/TSP.2013.2288675>; (ii) Pankaj Das (2020) <http://krishi.icar.gov.in/jspui/handle/123456789/44138>.

Maintained by Pankaj Das. Last updated 2 years ago.

1.7 match 2.00 score 3 scripts

cran

BSPADATA:Bayesian Proposal to Fit Spatial Econometric Models

The purpose of this package is to fit the three Spatial Econometric Models proposed in Anselin (1988, ISBN:9024737354) in the homoscedastic and the heteroscedatic case. The fit is made through MCMC algorithms and observational working variables approach.

Maintained by Jorge Sicacha-Parada. Last updated 3 years ago.

3.3 match 1.00 score 8 scripts

bioc

dmrseq:Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing

This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions.

Maintained by Keegan Korthauer. Last updated 5 months ago.

immunooncology dnamethylation epigenetics multiplecomparison software sequencing differentialmethylation wholegenome regression functionalgenomics

0.5 match 6.39 score 59 scripts 1 dependents

berchuck

spBFA:Spatial Bayesian Factor Analysis

Implements a spatial Bayesian non-parametric factor analysis model with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC). Spatial correlation is introduced in the columns of the factor loadings matrix using a Bayesian non-parametric prior, the probit stick-breaking process. Areal spatial data is modeled using a conditional autoregressive (CAR) prior and point-referenced spatial data is treated using a Gaussian process. The response variable can be modeled as Gaussian, probit, Tobit, or Binomial (using Polya-Gamma augmentation). Temporal correlation is introduced for the latent factors through a hierarchical structure and can be specified as exponential or first-order autoregressive. Full details of the package can be found in the accompanying vignette. Furthermore, the details of the package can be found in "Bayesian Non-Parametric Factor Analysis for Longitudinal Spatial Surfaces", by Berchuck et al (2019), <arXiv:1911.04337>. The paper is in press at the journal Bayesian Analysis.

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

openblas cpp

0.8 match 3 stars 4.18 score 3 scripts

berchuck

womblR:Spatiotemporal Boundary Detection Model for Areal Unit Data

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

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

openblas cpp

0.8 match 1 stars 4.10 score 25 scripts

cran

AnomalyScore:Anomaly Scoring for Multivariate Time Series

Compute an anomaly score for multivariate time series based on the k-nearest neighbors algorithm. Different computations of distances between time series are provided.

Maintained by Guillermo Granados. Last updated 4 months ago.

1.8 match 1.70 score 1 scripts

ercrema

baorista:Bayesian Aoristic Analyses

Provides an alternative approach to aoristic analyses for archaeological datasets by fitting Bayesian parametric growth models and non-parametric random-walk Intrinsic Conditional Autoregressive (ICAR) models on time frequency data (Crema (2024)<doi:10.1111/arcm.12984>). It handles event typo-chronology based timespans defined by start/end date as well as more complex user-provided vector of probabilities.

Maintained by Enrico Crema. Last updated 6 months ago.

aoristic-analyses archaeology bayesian-inference

0.5 match 11 stars 5.78 score 7 scripts

dppalomar

imputeFin:Imputation of Financial Time Series with Missing Values and/or Outliers

Missing values often occur in financial data due to a variety of reasons (errors in the collection process or in the processing stage, lack of asset liquidity, lack of reporting of funds, etc.). However, most data analysis methods expect complete data and cannot be employed with missing values. One convenient way to deal with this issue without having to redesign the data analysis method is to impute the missing values. This package provides an efficient way to impute the missing values based on modeling the time series with a random walk or an autoregressive (AR) model, convenient to model log-prices and log-volumes in financial data. In the current version, the imputation is univariate-based (so no asset correlation is used). In addition, outliers can be detected and removed. The package is based on the paper: J. Liu, S. Kumar, and D. P. Palomar (2019). Parameter Estimation of Heavy-Tailed AR Model With Missing Data Via Stochastic EM. IEEE Trans. on Signal Processing, vol. 67, no. 8, pp. 2159-2172. <doi:10.1109/TSP.2019.2899816>.

Maintained by Daniel P. Palomar. Last updated 3 years ago.

financial-data missing-values outliers time-series

0.5 match 25 stars 5.80 score 25 scripts

haotianxu

changepoints:A Collection of Change-Point Detection Methods

Performs a series of offline and/or online change-point detection algorithms for 1) univariate mean: <doi:10.1214/20-EJS1710>, <arXiv:2006.03283>; 2) univariate polynomials: <doi:10.1214/21-EJS1963>; 3) univariate and multivariate nonparametric settings: <doi:10.1214/21-EJS1809>, <doi:10.1109/TIT.2021.3130330>; 4) high-dimensional covariances: <doi:10.3150/20-BEJ1249>; 5) high-dimensional networks with and without missing values: <doi:10.1214/20-AOS1953>, <arXiv:2101.05477>, <arXiv:2110.06450>; 6) high-dimensional linear regression models: <arXiv:2010.10410>, <arXiv:2207.12453>; 7) high-dimensional vector autoregressive models: <arXiv:1909.06359>; 8) high-dimensional self exciting point processes: <arXiv:2006.03572>; 9) dependent dynamic nonparametric random dot product graphs: <arXiv:1911.07494>; 10) univariate mean against adversarial attacks: <arXiv:2105.10417>.

Maintained by Haotian Xu. Last updated 1 years ago.

openblas cpp

0.5 match 12 stars 5.78 score 25 scripts

tsmodels

tsmarch:Multivariate ARCH Models

Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.

Maintained by Alexios Galanos. Last updated 3 months ago.

econometrics finance garch multivariate-timeseries time-series openblas cpp

0.5 match 6 stars 5.65 score 3 scripts

jeroendmulder

powRICLPM:Perform Power Analysis for the RI-CLPM and STARTS Model

Perform user-friendly power analyses for the random intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait autoregressive trait state (STARTS) model. The strategy as proposed by Mulder (2023) <doi:10.1080/10705511.2022.2122467> is implemented. Extensions include the use of parameter constraints over time, bounded estimation, generation of data with skewness and kurtosis, and the option to setup the power analysis for Mplus.

Maintained by Jeroen Mulder. Last updated 5 months ago.

0.5 match 4 stars 5.56 score 10 scripts