vsp - Vintage Sparse PCA for Semi-Parametric Factor Analysis

Provides fast spectral estimation of latent factors in random dot product graphs using the vsp estimator. Under mild assumptions, the vsp estimator is consistent for (degree-corrected) stochastic blockmodels, (degree-corrected) mixed-membership stochastic blockmodels, and degree-corrected overlapping stochastic blockmodels.

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6.41 score 26 stars 22 scripts 563 downloads

fastRG - Sample Generalized Random Dot Product Graphs in Linear Time

Samples generalized random product graphs, a generalization of a broad class of network models. Given matrices X, S, and Y with with non-negative entries, samples a matrix with expectation X S Y^T and independent Poisson or Bernoulli entries using the fastRG algorithm of Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. The algorithm first samples the number of edges and then puts them down one-by-one. As a result it is O(m) where m is the number of edges, a dramatic improvement over element-wise algorithms that which require O(n^2) operations to sample a random graph, where n is the number of nodes.

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adjacency-matrixgraph-samplinglatent-factors

5.81 score 5 stars 26 scripts 243 downloads

fastadi - Self-Tuning Data Adaptive Matrix Imputation

Implements the AdaptiveImpute matrix completion algorithm of 'Intelligent Initialization and Adaptive Thresholding for Iterative Matrix Completion' <doi:10.1080/10618600.2018.1518238> as well as the specialized variant of 'Co-Factor Analysis of Citation Networks' <doi:10.1080/10618600.2024.2394464>. AdaptiveImpute is useful for embedding sparsely observed matrices, often out performs competing matrix completion algorithms, and self-tunes its hyperparameter, making usage easy.

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openblascppopenmp

3.95 score 9 stars 5 scripts 610 downloads

LRMF3 - Low Rank Matrix Factorization S3 Objects

Provides S3 classes to represent low rank matrix decompositions.

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matrix-factorizationsingular-value-decomposition

3.78 score 2 stars 2 dependents 6 scripts 217 downloads

gdim - Estimate Graph Dimension using Cross-Validated Eigenvalues

Cross-validated eigenvalues are estimated by splitting a graph into two parts, the training and the test graph. The training graph is used to estimate eigenvectors, and the test graph is used to evaluate the correlation between the training eigenvectors and the eigenvectors of the test graph. The correlations follow a simple central limit theorem that can be used to estimate graph dimension via hypothesis testing, see Chen et al. (2021) <doi:10.48550/arXiv:2108.03336> for details.

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3.54 score 7 stars 6 scripts 178 downloads

sparseLRMatrix - Represent and Use Sparse + Low Rank Matrices

Provides an S4 class for representing and interacting with sparse plus rank matrices. At the moment the implementation is quite spare, but the plan is eventually subclass Matrix objects.

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3.48 score 1 stars 2 dependents 2 scripts 146 downloads

invertiforms - Invertible Transforms for Matrices

Provides composable invertible transforms for (sparse) matrices.

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3.18 score 1 stars 1 dependents 4 scripts 242 downloads