R-universe search: armadillo

molgenis

MolgenisArmadillo:Armadillo Client for the Armadillo Service

A set of functions to manage data shared on a 'MOLGENIS Armadillo' server.

Maintained by Mariska Slofstra. Last updated 15 days ago.

hacktoberfest

60.2 match 3 stars 7.51 score 28 scripts

rcppcore

RcppArmadillo:'Rcpp' Integration for the 'Armadillo' Templated Linear Algebra Library

'Armadillo' is a templated C++ linear algebra library (by Conrad Sanderson) that aims towards a good balance between speed and ease of use. Integer, floating point and complex numbers are supported, as well as a subset of trigonometric and statistics functions. Various matrix decompositions are provided through optional integration with LAPACK and ATLAS libraries. The 'RcppArmadillo' package includes the header files from the templated 'Armadillo' library. Thus users do not need to install 'Armadillo' itself in order to use 'RcppArmadillo'. From release 7.800.0 on, 'Armadillo' is licensed under Apache License 2; previous releases were under licensed as MPL 2.0 from version 3.800.0 onwards and LGPL-3 prior to that; 'RcppArmadillo' (the 'Rcpp' bindings/bridge to Armadillo) is licensed under the GNU GPL version 2 or later, as is the rest of 'Rcpp'.

Maintained by Dirk Eddelbuettel. Last updated 4 days ago.

armadillo c-plus-plus rcpp rcpparmadillo openblas cpp openmp

24.1 match 197 stars 18.77 score 1.9k scripts 3.4k dependents

pachadotdev

cpp11armadillo:An 'Armadillo' Interface

Provides function declarations and inline function definitions that facilitate communication between R and the 'Armadillo' 'C++' library for linear algebra and scientific computing. This implementation is detailed in Vargas Sepulveda and Schneider Malamud (2024) <doi:10.48550/arXiv.2408.11074>.

Maintained by Mauricio Vargas Sepulveda. Last updated 24 days ago.

armadillo cpp cpp11 hacktoberfest linear-algebra

27.5 match 9 stars 9.14 score 1 scripts 16 dependents

molgenis

DSMolgenisArmadillo:'DataSHIELD' Client for 'MOLGENIS Armadillo'

'DataSHIELD' is an infrastructure and series of R packages that enables the remote and 'non-disclosive' analysis of sensitive research data. This package is the 'DataSHIELD' interface implementation to analyze data shared on a 'MOLGENIS Armadillo' server. 'MOLGENIS Armadillo' is a light-weight 'DataSHIELD' server using a file store and an 'RServe' server.

Maintained by Mariska Slofstra. Last updated 8 months ago.

hacktoberfest

20.4 match 6.54 score 48 scripts

coatless-rpkg

RcppEnsmallen:Header-Only C++ Mathematical Optimization Library for 'Armadillo'

'Ensmallen' is a templated C++ mathematical optimization library (by the 'MLPACK' team) that provides a simple set of abstractions for writing an objective function to optimize. Provided within are various standard and cutting-edge optimizers that include full-batch gradient descent techniques, small-batch techniques, gradient-free optimizers, and constrained optimization. The 'RcppEnsmallen' package includes the header files from the 'Ensmallen' library and pairs the appropriate header files from 'armadillo' through the 'RcppArmadillo' package. Therefore, users do not need to install 'Ensmallen' nor 'Armadillo' to use 'RcppEnsmallen'. Note that 'Ensmallen' is licensed under 3-Clause BSD, 'Armadillo' starting from 7.800.0 is licensed under Apache License 2, 'RcppArmadillo' (the 'Rcpp' bindings/bridge to 'Armadillo') is licensed under the GNU GPL version 2 or later. Thus, 'RcppEnsmallen' is also licensed under similar terms. Note that 'Ensmallen' requires a compiler that supports 'C++14' and 'Armadillo' 10.8.2 or later.

Maintained by James Joseph Balamuta. Last updated 3 months ago.

armadillo cpp11 ensmallen optimization rcpp rcpparmadillo openblas cpp openmp

16.6 match 31 stars 7.67 score 1 scripts 14 dependents

graemeleehickey

joineRML:Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes

Fits the joint model proposed by Henderson and colleagues (2000) <doi:10.1093/biostatistics/1.4.465>, but extended to the case of multiple continuous longitudinal measures. The time-to-event data is modelled using a Cox proportional hazards regression model with time-varying covariates. The multiple longitudinal outcomes are modelled using a multivariate version of the Laird and Ware linear mixed model. The association is captured by a multivariate latent Gaussian process. The model is estimated using a Monte Carlo Expectation Maximization algorithm. This project was funded by the Medical Research Council (Grant number MR/M013227/1).

Maintained by Graeme L. Hickey. Last updated 1 months ago.

armadillo biostatistics clinical-trials cox dynamic joint-models longitudinal-data multivariate-analysis multivariate-data multivariate-longitudinal-data prediction rcpp regression-models statistics survival openblas cpp openmp

11.0 match 30 stars 8.93 score 146 scripts 1 dependents

coatless-rpkg

rgen:Random Sampling Distribution C++ Routines for Armadillo

Provides popular sampling distributions C++ routines based in armadillo through a header file approach.

Maintained by James Joseph Balamuta. Last updated 1 years ago.

armadillo random-distributions rcpp rcpparmadillo

14.5 match 4 stars 5.38 score 1 scripts 4 dependents

mlampros

elmNNRcpp:The Extreme Learning Machine Algorithm

Training and predict functions for Single Hidden-layer Feedforward Neural Networks (SLFN) using the Extreme Learning Machine (ELM) algorithm. The ELM algorithm differs from the traditional gradient-based algorithms for very short training times (it doesn't need any iterative tuning, this makes learning time very fast) and there is no need to set any other parameters like learning rate, momentum, epochs, etc. This is a reimplementation of the 'elmNN' package using 'RcppArmadillo' after the 'elmNN' package was archived. For more information, see "Extreme learning machine: Theory and applications" by Guang-Bin Huang, Qin-Yu Zhu, Chee-Kheong Siew (2006), Elsevier B.V, <doi:10.1016/j.neucom.2005.12.126>.

Maintained by Lampros Mouselimis. Last updated 2 years ago.

armadillo elm extreme-learning-machine rcpparmadillo openblas cpp openmp

11.0 match 14 stars 7.06 score 39 scripts 7 dependents

ypan1988

roptim:General Purpose Optimization in R using C++

Perform general purpose optimization in R using C++. A unified wrapper interface is provided to call C functions of the five optimization algorithms ('Nelder-Mead', 'BFGS', 'CG', 'L-BFGS-B' and 'SANN') underlying optim().

Maintained by Yi Pan. Last updated 3 years ago.

armadillo bfgs conjugate-gradient l-bfgs-b nelder-mead optim rcpp simulated-annealing openblas cpp

11.0 match 20 stars 6.93 score 15 scripts 12 dependents

tmsalab

cIRT:Choice Item Response Theory

Jointly model the accuracy of cognitive responses and item choices within a Bayesian hierarchical framework as described by Culpepper and Balamuta (2015) <doi:10.1007/s11336-015-9484-7>. In addition, the package contains the datasets used within the analysis of the paper.

Maintained by James Joseph Balamuta. Last updated 3 years ago.

armadillo bayesian choice cognitive-diagnostic-models gibbs-sampling item-response-theory rcpparmadillo openblas cpp openmp

11.0 match 4 stars 5.14 score 23 scripts

mlampros

ClusterR:Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and Affinity Propagation Clustering

Gaussian mixture models, k-means, mini-batch-kmeans, k-medoids and affinity propagation clustering with the option to plot, validate, predict (new data) and estimate the optimal number of clusters. The package takes advantage of 'RcppArmadillo' to speed up the computationally intensive parts of the functions. For more information, see (i) "Clustering in an Object-Oriented Environment" by Anja Struyf, Mia Hubert, Peter Rousseeuw (1997), Journal of Statistical Software, <doi:10.18637/jss.v001.i04>; (ii) "Web-scale k-means clustering" by D. Sculley (2010), ACM Digital Library, <doi:10.1145/1772690.1772862>; (iii) "Armadillo: a template-based C++ library for linear algebra" by Sanderson et al (2016), The Journal of Open Source Software, <doi:10.21105/joss.00026>; (iv) "Clustering by Passing Messages Between Data Points" by Brendan J. Frey and Delbert Dueck, Science 16 Feb 2007: Vol. 315, Issue 5814, pp. 972-976, <doi:10.1126/science.1136800>.

Maintained by Lampros Mouselimis. Last updated 9 months ago.

affinity-propagation cpp11 gmm kmeans kmedoids-clustering mini-batch-kmeans rcpparmadillo openblas cpp openmp

4.6 match 84 stars 11.04 score 640 scripts 24 dependents

collinerickson

GauPro:Gaussian Process Fitting

Fits a Gaussian process model to data. Gaussian processes are commonly used in computer experiments to fit an interpolating model. The model is stored as an 'R6' object and can be easily updated with new data. There are options to run in parallel, and 'Rcpp' has been used to speed up calculations. For more info about Gaussian process software, see Erickson et al. (2018) <doi:10.1016/j.ejor.2017.10.002>.

Maintained by Collin Erickson. Last updated 6 days ago.

openblas cpp openmp

5.2 match 16 stars 8.40 score 104 scripts 1 dependents

tmsalab

dina:Bayesian Estimation of DINA Model

Estimate the Deterministic Input, Noisy "And" Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) <doi:10.3102/1076998615595403>.

Maintained by James Joseph Balamuta. Last updated 5 years ago.

armadillo bayesian gibbs-sampler irt item-response-theory psychometrics rcpp rcpparmadillo openblas cpp

11.0 match 14 stars 3.85 score 3 scripts

tmsalab

iccbeta:Multilevel Model Intraclass Correlation for Slope Heterogeneity

A function and vignettes for computing an intraclass correlation described in Aguinis & Culpepper (2015) <doi:10.1177/1094428114563618>. This package quantifies the share of variance in a dependent variable that is attributed to group heterogeneity in slopes.

Maintained by Steven Andrew Culpepper. Last updated 5 years ago.

armadillo correlation intraclass-correlation rcpp rcpparmadillo openblas cpp

11.0 match 2 stars 3.00 score 5 scripts

tmsalab

fourPNO:Bayesian 4 Parameter Item Response Model

Estimate Barton & Lord's (1981) <doi:10.1002/j.2333-8504.1981.tb01255.x> four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2016) <doi:10.1007/s11336-015-9477-6>.

Maintained by Steven Andrew Culpepper. Last updated 5 years ago.

armadillo cognitive-diagnostic-models gibbs-sampler item-response-theory rcpp rcpparmadillo openblas cpp openmp

11.0 match 1 stars 2.70 score 5 scripts

tmsalab

rrum:Bayesian Estimation of the Reduced Reparameterized Unified Model with Gibbs Sampling

Implementation of Gibbs sampling algorithm for Bayesian Estimation of the Reduced Reparameterized Unified Model ('rrum'), described by Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.

Maintained by James Joseph Balamuta. Last updated 1 years ago.

armadillo cdm cognitive-diagnostic-models gibbs-sampling-algorithm psychometrics rcpparmadillo rrum openblas cpp openmp

11.0 match 2.70 score 3 scripts

eddelbuettel

naarma:Connect nanoarrow with (Rcpp)Armadillo

The nanoarrow package offers C-level functionality to work with Arrow object, along with a small amount of C++ integration. This package uses it to interact with Armadillo objects. Some auxiliary testing facility from the nanoarrow package is included here as well.

Maintained by Dirk Eddelbuettel. Last updated 3 months ago.

armadillo arrow cpp mlpack cpp

14.6 match 2.00 score 4 scripts

polkas

miceFast:Fast Imputations Using 'Rcpp' and 'Armadillo'

Fast imputations under the object-oriented programming paradigm. Moreover there are offered a few functions built to work with popular R packages such as 'data.table' or 'dplyr'. The biggest improvement in time performance could be achieve for a calculation where a grouping variable have to be used. A single evaluation of a quantitative model for the multiple imputations is another major enhancement. A new major improvement is one of the fastest predictive mean matching in the R world because of presorting and binary search.

Maintained by Maciej Nasinski. Last updated 1 months ago.

cpp fast fast-imputations grouping imputation imputations matrix mro multiple-imputation rcpp rcpparmadillo vif weighting openblas cpp openmp

3.0 match 20 stars 5.94 score 29 scripts

cran

Glarmadillo:Solve the Graphical Lasso Problem with 'Armadillo'

Efficiently implements the Graphical Lasso algorithm, utilizing the 'Armadillo' 'C++' library for rapid computation. This algorithm introduces an L1 penalty to derive sparse inverse covariance matrices from observations of multivariate normal distributions. Features include the generation of random and structured sparse covariance matrices, beneficial for simulations, statistical method testing, and educational purposes in graphical modeling. A unique function for regularization parameter selection based on predefined sparsity levels is also offered, catering to users with specific sparsity requirements in their models. The methodology for sparse inverse covariance estimation implemented in this package is based on the work of Friedman, Hastie, and Tibshirani (2008) <doi:10.1093/biostatistics/kxm045>.

Maintained by Alessandro Meng. Last updated 1 years ago.

openblas cpp

5.4 match 2.70 score

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

2.5 match 31 stars 5.57 score 12 scripts

kharchenkolab

pagoda2:Single Cell Analysis and Differential Expression

Analyzing and interactively exploring large-scale single-cell RNA-seq datasets. 'pagoda2' primarily performs normalization and differential gene expression analysis, with an interactive application for exploring single-cell RNA-seq datasets. It performs basic tasks such as cell size normalization, gene variance normalization, and can be used to identify subpopulations and run differential expression within individual samples. 'pagoda2' was written to rapidly process modern large-scale scRNAseq datasets of approximately 1e6 cells. The companion web application allows users to explore which gene expression patterns form the different subpopulations within your data. The package also serves as the primary method for preprocessing data for conos, <https://github.com/kharchenkolab/conos>. This package interacts with data available through the 'p2data' package, which is available in a 'drat' repository. To access this data package, see the instructions at <https://github.com/kharchenkolab/pagoda2>. The size of the 'p2data' package is approximately 6 MB.

Maintained by Evan Biederstedt. Last updated 1 years ago.

scrna-seq single-cell single-cell-rna-seq transcriptomics openblas cpp openmp

1.6 match 222 stars 8.00 score 282 scripts

resourcecode-project

resourcecode:Access to the 'RESOURCECODE' Hindcast Database

Utility functions to download data from the 'RESOURCECODE' hindcast database of sea-states, time series of sea-state parameters and time series of 1D and 2D wave spectra. See <https://resourcecode.ifremer.fr> for more details about the available data. Also provides facilities to plot and analyse downloaded data, such as computing the sea-state parameters from both the 1D and 2D surface elevation variance spectral density.

Maintained by Nicolas Raillard. Last updated 2 months ago.

openblas cpp

1.7 match 1 stars 4.65 score 4 scripts

alexeckert

parallelDist:Parallel Distance Matrix Computation using Multiple Threads

A fast parallelized alternative to R's native 'dist' function to calculate distance matrices for continuous, binary, and multi-dimensional input matrices, which supports a broad variety of 41 predefined distance functions from the 'stats', 'proxy' and 'dtw' R packages, as well as user- defined functions written in C++. For ease of use, the 'parDist' function extends the signature of the 'dist' function and uses the same parameter naming conventions as distance methods of existing R packages. The package is mainly implemented in C++ and leverages the 'RcppParallel' package to parallelize the distance computations with the help of the 'TinyThread' library. Furthermore, the 'Armadillo' linear algebra library is used for optimized matrix operations during distance calculations. The curiously recurring template pattern (CRTP) technique is applied to avoid virtual functions, which improves the Dynamic Time Warping calculations while the implementation stays flexible enough to support different DTW step patterns and normalization methods.

Maintained by Alexander Eckert. Last updated 3 years ago.

data-science distance-computations matrices openblas cpp

0.5 match 51 stars 9.92 score 432 scripts 14 dependents

koheiw

proxyC:Computes Proximity in Large Sparse Matrices

Computes proximity between rows or columns of large matrices efficiently in C++. Functions are optimised for large sparse matrices using the Armadillo and Intel TBB libraries. Among various built-in similarity/distance measures, computation of correlation, cosine similarity and Euclidean distance is particularly fast.

Maintained by Kohei Watanabe. Last updated 5 months ago.

data-science distance-measures similarity-measures openblas onetbb cpp

0.5 match 29 stars 8.88 score 23 scripts 32 dependents

duckmayr

RcppDist:'Rcpp' Integration of Additional Probability Distributions

The 'Rcpp' package provides a C++ library to make it easier to use C++ with R. R and 'Rcpp' provide functions for a variety of statistical distributions. Several R packages make functions available to R for additional statistical distributions. However, to access these functions from C++ code, a costly call to the R functions must be made. 'RcppDist' provides a header-only C++ library with functions for additional statistical distributions that can be called from C++ when writing code using 'Rcpp' or 'RcppArmadillo'. Functions are available that return a 'NumericVector' as well as doubles, and for multivariate or matrix distributions, 'Armadillo' vectors and matrices. 'RcppDist' provides functions for the following distributions: the four parameter beta distribution; the Laplace distribution; the location-scale t distribution; the truncated normal distribution; the truncated t distribution; a truncated location-scale t distribution; the triangle distribution; the multivariate normal distribution*; the multivariate t distribution*; the Wishart distribution*; and the inverse Wishart distribution*. Distributions marked with an asterisk rely on 'RcppArmadillo'.

Maintained by JB Duck-Mayr. Last updated 6 years ago.

openblas cpp

0.5 match 6 stars 7.50 score 81 scripts 33 dependents

eddelbuettel

RInside:C++ Classes to Embed R in C++ (and C) Applications

C++ classes to embed R in C++ (and C) applications A C++ class providing the R interpreter is offered by this package making it easier to have "R inside" your C++ application. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the nine subdirectories of the examples/ directory of the installed package: standard, 'mpi' (for parallel computing), 'qt' (showing how to embed 'RInside' inside a Qt GUI application), 'wt' (showing how to build a "web-application" using the Wt toolkit), 'armadillo' (for 'RInside' use with 'RcppArmadillo'), 'eigen' (for 'RInside' use with 'RcppEigen'), and 'c_interface' for a basic C interface and 'Ruby' illustration. The examples use 'GNUmakefile(s)' with GNU extensions, so a GNU make is required (and will use the 'GNUmakefile' automatically). 'Doxygen'-generated documentation of the C++ classes is available at the 'RInside' website as well.

Maintained by Dirk Eddelbuettel. Last updated 5 months ago.

c-plus-plus embedded cpp

0.5 match 136 stars 7.17 score 17 scripts 1 dependents

ghislainv

jSDM:Joint Species Distribution Models

Fits joint species distribution models ('jSDM') in a hierarchical Bayesian framework (Warton and al. 2015 <doi:10.1016/j.tree.2015.09.007>). The Gibbs sampler is written in 'C++'. It uses 'Rcpp', 'Armadillo' and 'GSL' to maximize computation efficiency.

Maintained by Ghislain Vieilledent. Last updated 2 years ago.

gsl openblas cpp

0.5 match 11 stars 5.87 score 68 scripts

cufesam

fasterElasticNet:An Amazing Fast Way to Fit Elastic Net

Fit Elastic Net, Lasso, and Ridge regression and do cross-validation in a fast way. We build the algorithm based on Least Angle Regression by Bradley Efron, Trevor Hastie, Iain Johnstone, etc. (2004)(<doi:10.1214/009053604000000067 >) and some algorithms like Givens rotation and Forward/Back Substitution. In this way, many matrices to be computed are retained as triangular matrices which can eventually speed up the computation. The fitting algorithm for Elastic Net is written in C++ using Armadillo linear algebra library.

Maintained by Linyu Zuo. Last updated 6 years ago.

elastic-net openblas cpp openmp

0.5 match 8 stars 3.60 score 4 scripts

yannrichet-asnr

rlibkriging:Kriging Models using the 'libKriging' Library

Interface to 'libKriging' 'C++' library <https://github.com/libKriging> that should provide most standard Kriging / Gaussian process regression features (like in 'DiceKriging', 'kergp' or 'RobustGaSP' packages). 'libKriging' relies on Armadillo linear algebra library (Apache 2 license) by Conrad Sanderson, 'lbfgsb_cpp' is a 'C++' port around by Pascal Have of 'lbfgsb' library (BSD-3 license) by Ciyou Zhu, Richard Byrd, Jorge Nocedal and Jose Luis Morales used for hyperparameters optimization.

Maintained by Yann Richet. Last updated 2 months ago.

openblas arpack cpp

0.5 match 3.40 score 126 scripts

hypertidy

controlledburn:Rasterize Index

Rasterize without materializing any pixel values. Rasterization of polygons starts with classifying pixels by polygon, and in terms of scanline algorithms this is natively stored very efficiently as an index of start and stops of edges by scanline. We produce these intermediate structures, so they can be used as an efficient format of polygon rasterization, or for the complement of this, data extraction from materialized rasters. This package was derived from 'fasterize', removing Armadillo and the raster package.

Maintained by Michael Sumner. Last updated 1 years ago.

compression lazy polygons raster cpp

0.5 match 11 stars 3.04 score 2 scripts

cran

cPCG:Efficient and Customized Preconditioned Conjugate Gradient Method for Solving System of Linear Equations

Solves system of linear equations using (preconditioned) conjugate gradient algorithm, with improved efficiency using Armadillo templated 'C++' linear algebra library, and flexibility for user-specified preconditioning method. Please check <https://github.com/styvon/cPCG> for latest updates.

Maintained by Yongwen Zhuang. Last updated 6 years ago.

openblas cpp

0.5 match 2.28 score 19 scripts

activeanalytics

bigReg:Generalized Linear Models (GLM) for Large Data Sets

Allows the user to carry out GLM on very large data sets. Data can be created using the data_frame() function and appended to the object with object$append(data); data_frame and data_matrix objects are available that allow the user to store large data on disk. The data is stored as doubles in binary format and any character columns are transformed to factors and then stored as numeric (binary) data while a look-up table is stored in a separate .meta_data file in the same folder. The data is stored in blocks and GLM regression algorithm is modified and carries out a MapReduce- like algorithm to fit the model. The functions bglm(), and summary() and bglm_predict() are available for creating and post-processing of models. The library requires Armadillo installed on your system. It probably won't function on windows since multi-core processing is done using mclapply() which forks R on Unix/Linux type operating systems.

Maintained by Chibisi Chima-Okereke. Last updated 9 years ago.

big-data data-frame generalized-linear-models openblas cpp

0.5 match 1 stars 2.00 score 3 scripts

cran

fastliu:Fast Functions for Liu Regression with Regularization Parameter and Statistics

Efficient computation of the Liu regression coefficient paths, Liu-related statistics and information criteria for a grid of the regularization parameter. The computations are based on the 'C++' library 'Armadillo' through the 'R' package 'Rcpp'.

Maintained by Murat Genç. Last updated 1 years ago.

openblas cpp openmp

0.5 match 1.00 score

davidealtomare

FBFsearch:Algorithm for Searching the Space of Gaussian Directed Acyclic Graph Models Through Moment Fractional Bayes Factors

We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. The algorithm proposed makes use of moment fractional Bayes factors (MFBF) and thus it is suitable for learning sparse graph. The algorithm is implemented by using Armadillo: an open-source C++ linear algebra library.

Maintained by Davide Altomare. Last updated 3 years ago.

openblas cpp

0.5 match 1.00 score 3 scripts