distributions3 - Probability Distributions as S3 Objects
Tools to create and manipulate probability distributions using S3. Generics pdf(), cdf(), quantile(), and random() provide replacements for base R's d/p/q/r style functions. Functions and arguments have been named carefully to minimize confusion for students in intro stats courses. The documentation for each distribution contains detailed mathematical notes.
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11.83 score 107 stars 12 dependents 126 scripts 5.3k downloadsmodeltests - Testing Infrastructure for Broom Model Generics
Provides a number of testthat tests that can be used to verify that tidy(), glance() and augment() methods meet consistent specifications. This allows methods for the same generic to be spread across multiple packages, since all of those packages can make the same guarantees to users about returned objects.
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7.81 score 6 stars 554 scripts 10k downloadsvsp - 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 downloadsfastRG - 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 174 downloadsfastadi - 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 downloadsLRMF3 - 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 downloadssparseLRMatrix - 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 downloadsinvertiforms - 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