R-universe search: exports:viterbi

Showing 8 of total 8 results (show query)

rqtl

qtl2:Quantitative Trait Locus Mapping in Experimental Crosses

Provides a set of tools to perform quantitative trait locus (QTL) analysis in experimental crosses. It is a reimplementation of the 'R/qtl' package to better handle high-dimensional data and complex cross designs. Broman et al. (2019) <doi:10.1534/genetics.118.301595>.

Maintained by Karl W Broman. Last updated 22 days ago.

cpp

34 stars 9.48 score 1.1k scripts 5 dependents

theomichelot

moveHMM:Animal Movement Modelling using Hidden Markov Models

Provides tools for animal movement modelling using hidden Markov models. These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process, etc. <doi:10.1111/2041-210X.12578>.

Maintained by Theo Michelot. Last updated 1 years ago.

openblas cpp

38 stars 8.63 score 112 scripts

bmcclintock

momentuHMM:Maximum Likelihood Analysis of Animal Movement Behavior Using Multivariate Hidden Markov Models

Extended tools for analyzing telemetry data using generalized hidden Markov models. Features of momentuHMM (pronounced ``momentum'') include data pre-processing and visualization, fitting HMMs to location and auxiliary biotelemetry or environmental data, biased and correlated random walk movement models, discrete- or continuous-time HMMs, continuous- or discrete-space movement models, approximate Langevin diffusion models, hierarchical HMMs, multiple imputation for incorporating location measurement error and missing data, user-specified design matrices and constraints for covariate modelling of parameters, random effects, decoding of the state process, visualization of fitted models, model checking and selection, and simulation. See McClintock and Michelot (2018) <doi:10.1111/2041-210X.12995>.

Maintained by Brett McClintock. Last updated 2 months ago.

openblas cpp

43 stars 8.47 score 162 scripts

janoleko

LaMa:Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.

Maintained by Jan-Ole Koslik. Last updated 3 days ago.

openblas cpp openmp

9 stars 7.86 score 42 scripts

loelschlaeger

fHMM:Fitting Hidden Markov Models to Financial Data

Fitting (hierarchical) hidden Markov models to financial data via maximum likelihood estimation. See Oelschläger, L. and Adam, T. "Detecting Bearish and Bullish Markets in Financial Time Series Using Hierarchical Hidden Markov Models" (2021, Statistical Modelling) <doi:10.1177/1471082X211034048> for a reference on the method. A user guide is provided by the accompanying software paper "fHMM: Hidden Markov Models for Financial Time Series in R", Oelschläger, L., Adam, T., and Michels, R. (2024, Journal of Statistical Software) <doi:10.18637/jss.v109.i09>.

Maintained by Lennart Oelschläger. Last updated 7 days ago.

finance hidden-markov-models cpp openmp

17 stars 7.04 score 5 scripts

ingmarvisser

depmixS4:Dependent Mixture Models - Hidden Markov Models of GLMs and Other Distributions in S4

Fits latent (hidden) Markov models on mixed categorical and continuous (time series) data, otherwise known as dependent mixture models, see Visser & Speekenbrink (2010, <DOI:10.18637/jss.v036.i07>).

Maintained by Ingmar Visser. Last updated 4 years ago.

12 stars 6.85 score 308 scripts 4 dependents

cran

HMM:Hidden Markov Models

Easy to use library to setup, apply and make inference with discrete time and discrete space Hidden Markov Models.

Maintained by Lin Himmelmann. Last updated 3 years ago.

3 stars 3.56 score 4 dependents

cran

RcppHMM:Rcpp Hidden Markov Model

Collection of functions to evaluate sequences, decode hidden states and estimate parameters from a single or multiple sequences of a discrete time Hidden Markov Model. The observed values can be modeled by a multinomial distribution for categorical/labeled emissions, a mixture of Gaussians for continuous data and also a mixture of Poissons for discrete values. It includes functions for random initialization, simulation, backward or forward sequence evaluation, Viterbi or forward-backward decoding and parameter estimation using an Expectation-Maximization approach.

Maintained by Roberto A. Cardenas-Ovando. Last updated 7 years ago.

openblas cpp

1 stars 1.00 score