R-universe search: mrf

Showing 14 of total 14 results (show query)

quirinms

mrf:Multiresolution Forecasting

Forecasting of univariate time series using feature extraction with variable prediction methods is provided. Feature extraction is done with a redundant Haar wavelet transform with filter h = (0.5, 0.5). The advantage of the approach compared to typical Fourier based methods is an dynamic adaptation to varying seasonalities. Currently implemented prediction methods based on the selected wavelets levels and scales are a regression and a multi-layer perceptron. Forecasts can be computed for horizon 1 or higher. Model selection is performed with an evolutionary optimization. Selection criteria are currently the AIC criterion, the Mean Absolute Error or the Mean Root Error. The data is split into three parts for model selection: Training, test, and evaluation dataset. The training data is for computing the weights of a parameter set. The test data is for choosing the best parameter set. The evaluation data is for assessing the forecast performance of the best parameter set on new data unknown to the model. This work is published in Stier, Q.; Gehlert, T.; Thrun, M.C. Multiresolution Forecasting for Industrial Applications. Processes 2021, 9, 1697. <https://doi.org/10.3390/pr9101697>.

Maintained by Quirin Stier. Last updated 3 years ago.

58.8 match 2 stars 4.28 score 19 scripts

freguglia

mrf2d:Markov Random Field Models for Image Analysis

Model fitting, sampling and visualization for the (Hidden) Markov Random Field model with pairwise interactions and general interaction structure from Freguglia, Garcia & Bicas (2020) <doi:10.1002/env.2613>, which has many popular models used in 2-dimensional lattices as particular cases, like the Ising Model and Potts Model. A complete manuscript describing the package is available in Freguglia & Garcia (2022) <doi:10.18637/jss.v101.i08>.

Maintained by Victor Freguglia. Last updated 2 years ago.

gibbs-energy hidden-markov-model hidden-markov-models markov-random-field mrf mrf-models neighborhood cpp

24.1 match 9 stars 4.95 score 10 scripts

gavinsimpson

MRFtools:Tools for Constructing and Plotting Markov Random Fields in R for Graphical Data

Utility functions for using Markov Random Field smooths in Generalized Additive Models fitted with the 'mgcv' package.

Maintained by Eric J. Petersen. Last updated 4 days ago.

27.3 match 2.18 score

cran

mgcv:Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

Generalized additive (mixed) models, some of their extensions and other generalized ridge regression with multiple smoothing parameter estimation by (Restricted) Marginal Likelihood, Generalized Cross Validation and similar, or using iterated nested Laplace approximation for fully Bayesian inference. See Wood (2017) <doi:10.1201/9781315370279> for an overview. Includes a gam() function, a wide variety of smoothers, 'JAGS' support and distributions beyond the exponential family.

Maintained by Simon Wood. Last updated 1 years ago.

openblas openmp

3.7 match 32 stars 12.71 score 17k scripts 7.8k dependents

nicholasjclark

MRFcov:Markov Random Fields with Additional Covariates

Approximate node interaction parameters of Markov Random Fields graphical networks. Models can incorporate additional covariates, allowing users to estimate how interactions between nodes in the graph are predicted to change across covariate gradients. The general methods implemented in this package are described in Clark et al. (2018) <doi:10.1002/ecy.2221>.

Maintained by Nicholas J Clark. Last updated 12 months ago.

conditional-random-fields graphical-models machine-learning markov-random-field multivariate-analysis multivariate-statistics network-analysis networks

6.5 match 24 stars 6.03 score 30 scripts

cran

GiRaF:Gibbs Random Fields Analysis

Allows calculation on, and sampling from Gibbs Random Fields, and more precisely general homogeneous Potts model. The primary tool is the exact computation of the intractable normalising constant for small rectangular lattices. Beside the latter function, it contains method that give exact sample from the likelihood for small enough rectangular lattices or approximate sample from the likelihood using MCMC samplers for large lattices.

Maintained by Julien Stoehr. Last updated 4 years ago.

cpp

6.8 match 3.95 score 3 dependents

fabian-s

spikeSlabGAM:Bayesian Variable Selection and Model Choice for Generalized Additive Mixed Models

Bayesian variable selection, model choice, and regularized estimation for (spatial) generalized additive mixed regression models via stochastic search variable selection with spike-and-slab priors.

Maintained by Fabian Scheipl. Last updated 5 months ago.

openblas

3.3 match 14 stars 6.28 score 15 scripts 1 dependents

rapler

dst:Using the Theory of Belief Functions

Using the Theory of Belief Functions for evidence calculus. Basic probability assignments, or mass functions, can be defined on the subsets of a set of possible values and combined. A mass function can be extended to a larger frame. Marginalization, i.e. reduction to a smaller frame can also be done. These features can be combined to analyze small belief networks and take into account situations where information cannot be satisfactorily described by probability distributions.

Maintained by Peiyuan Zhu. Last updated 3 months ago.

3.3 match 6 stars 5.96 score 126 scripts

mfasiolo

mgcViz:Visualisations for Generalized Additive Models

Extension of the 'mgcv' package, providing visual tools for Generalized Additive Models that exploit the additive structure of such models, scale to large data sets and can be used in conjunction with a wide range of response distributions. The focus is providing visual methods for better understanding the model output and for aiding model checking and development beyond simple exponential family regression. The graphical framework is based on the layering system provided by 'ggplot2'.

Maintained by Matteo Fasiolo. Last updated 7 months ago.

2.0 match 77 stars 9.38 score 1000 scripts

maartenmarsman

bgms:Bayesian Analysis of Networks of Binary and/or Ordinal Variables

Bayesian variable selection methods for analyzing the structure of a Markov Random Field model for a network of binary and/or ordinal variables. Details of the implemented methods can be found in: Marsman, van den Bergh, and Haslbeck (in press) <doi:10.31234/osf.io/ukwrf>.

Maintained by Maarten Marsman. Last updated 11 days ago.

cpp

1.9 match 4 stars 6.94 score 30 scripts 1 dependents

gamlss-dev

gamlss.spatial:Spatial Terms in Generalized Additive Models for Location Scale and Shape

The packages enables fitting Gaussian Markov Random Fields within the Generalized Additive Models for Location Scale and Shape algorithms.

Maintained by Fernanda De Bastiani. Last updated 1 years ago.

3.3 match 3.74 score 11 scripts

ocbe-uio

BayesSurvive:Bayesian Survival Models for High-Dimensional Data

An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database.

Maintained by Zhi Zhao. Last updated 5 months ago.

bayesian-cox-models bayesian-variable-selection graph-learning high-dimensional-statistics omics-data-integration survival-analysis cpp

2.4 match 1 stars 4.70 score 1 scripts

hoxo-m

sGMRFmix:Sparse Gaussian Markov Random Field Mixtures for Anomaly Detection

An implementation of sparse Gaussian Markov random field mixtures presented by Ide et al. (2016) <doi:10.1109/ICDM.2016.0119>. It provides a novel anomaly detection method for multivariate noisy sensor data. It can automatically handle multiple operational modes. And it can also compute variable-wise anomaly scores.

Maintained by Koji Makiyama. Last updated 7 years ago.

2.9 match 1 stars 2.34 score 22 scripts

razrahman

IntegratedMRF:Integrated Prediction using Uni-Variate and Multivariate Random Forests

An implementation of a framework for drug sensitivity prediction from various genetic characterizations using ensemble approaches. Random Forests or Multivariate Random Forest predictive models can be generated from each genetic characterization that are then combined using a Least Square Regression approach. It also provides options for the use of different error estimation approaches of Leave-one-out, Bootstrap, N-fold cross validation and 0.632+Bootstrap along with generation of prediction confidence interval using Jackknife-after-Bootstrap approach.

Maintained by Raziur Rahman. Last updated 7 years ago.

cpp

5.0 match 1.26 score 18 scripts