KFAS - Kalman Filter and Smoother for Exponential Family State Space Models
State space modelling is an efficient and flexible framework for statistical inference of a broad class of time series and other data. KFAS includes computationally efficient functions for Kalman filtering, smoothing, forecasting, and simulation of multivariate exponential family state space models, with observations from Gaussian, Poisson, binomial, negative binomial, and gamma distributions. See the paper by Helske (2017) <doi:10.18637/jss.v078.i10> for details.
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dynamic-linear-modelexponential-familyfortrangaussian-modelsstate-spacetime-seriesopenblas
11.08 score 73 stars 21 dependents 360 scripts 9.0k downloadsseqHMM - Mixture Hidden Markov Models for Social Sequence Data and Other Multivariate, Multichannel Categorical Time Series
Designed for estimating variants of hidden (latent) Markov models (HMMs), mixture HMMs, and non-homogeneous HMMs (NHMMs) for social sequence data and other categorical time series. Special cases include feedback-augmented NHMMs, Markov models without latent layer, mixture Markov models, and latent class models. The package supports models for one or multiple subjects with one or multiple parallel sequences (channels). External covariates can be added to explain cluster membership in mixture models as well as initial, transition and emission probabilities in NHMMs. The package provides functions for evaluating and comparing models, as well as functions for visualizing of multichannel sequence data and HMMs. For NHMMs, methods for computing average causal effects and marginal state and emission probabilities are available. Models are estimated using maximum likelihood via the EM algorithm or direct numerical maximization with analytical gradients. Documentation is available via several vignettes, and Helske and Helske (2019, <doi:10.18637/jss.v088.i03>). For methodology behind the NHMMs, see Helske (2025, <doi:10.48550/arXiv.2503.16014>).
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categorical-dataem-algorithmhidden-markov-modelshmmmixture-markov-modelstime-seriesopenblascppopenmp
9.76 score 104 stars 1 dependents 170 scripts 783 downloadsbssm - Bayesian Inference of Non-Linear and Non-Gaussian State Space Models
Efficient methods for Bayesian inference of state space models via Markov chain Monte Carlo (MCMC) based on parallel importance sampling type weighted estimators (Vihola, Helske, and Franks, 2020, <doi:10.1111/sjos.12492>), particle MCMC, and its delayed acceptance version. Gaussian, Poisson, binomial, negative binomial, and Gamma observation densities and basic stochastic volatility models with linear-Gaussian state dynamics, as well as general non-linear Gaussian models and discretised diffusion models are supported. See Helske and Vihola (2021, <doi:10.32614/RJ-2021-103>) for details.
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bayesian-inferencecppmarkov-chain-monte-carloparticle-filterstate-spacetime-seriesopenblascppopenmp
7.02 score 49 stars 16 scripts 3.3k downloadsramcmc - Robust Adaptive Metropolis Algorithm
Function for adapting the shape of the random walk Metropolis proposal as specified by robust adaptive Metropolis algorithm by Vihola (2012) <doi:10.1007/s11222-011-9269-5>. The package also includes fast functions for rank-one Cholesky update and downdate. These functions can be used directly from R or the corresponding C++ header files can be easily linked to other R packages.
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openblascpp
6.71 score 6 stars 12 dependents 13 scripts 3.6k downloadswalker - Bayesian Generalized Linear Models with Time-Varying Coefficients
Efficient Bayesian generalized linear models with time-varying coefficients as in Helske (2022, <doi:10.1016/j.softx.2022.101016>). Gaussian, Poisson, and binomial observations are supported. The Markov chain Monte Carlo (MCMC) computations are done using Hamiltonian Monte Carlo provided by Stan, using a state space representation of the model in order to marginalise over the coefficients for efficient sampling. For non-Gaussian models, the package uses the importance sampling type estimators based on approximate marginal MCMC as in Vihola, Helske, Franks (2020, <doi:10.1111/sjos.12492>).
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bayesiangeneralized-linear-modelsmcmcstantime-seriesopenblascpp
6.47 score 46 stars 16 scripts 270 downloadsRlibeemd - Ensemble Empirical Mode Decomposition (EEMD) and Its Complete Variant (CEEMDAN)
An R interface for libeemd (Luukko, Helske, Räsänen, 2016) <doi:10.1007/s00180-015-0603-9>, a C library of highly efficient parallelizable functions for performing the ensemble empirical mode decomposition (EEMD), its complete variant (CEEMDAN), the regular empirical mode decomposition (EMD), and bivariate EMD (BEMD). Due to the possible portability issues CRAN version no longer supports OpenMP, but you can install OpenMP-supported version from GitHub: <https://github.com/helske/Rlibeemd/>.
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cdecompositioneemdemdtime-seriesgslcppopenmp
6.11 score 40 stars 12 dependents 18 scripts 519 downloadsdiagis - Diagnostic Plot and Multivariate Summary Statistics of Weighted Samples from Importance Sampling
Fast functions for effective sample size, weighted multivariate mean, variance, and quantile computation, and weight diagnostic plot for generic importance sampling type or other probability weighted samples.
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cppimportance-samplingweighted-samplesopenblascpp
5.06 score 1 stars 1 dependents 18 scripts 4.3k downloadschanger - Change R Package Name
Changing the name of an existing R package is annoying but common task especially in the early stages of package development. This package (mostly) automates this task.
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4.37 score 47 stars 4 scripts 220 downloadsparticlefield - 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.
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cpp
2.18 score 3 stars 4 scripts