alexpghayes r-universe repositoryhttps://alexpghayes.r-universe.devPackage updated in alexpghayescranlike-server 0.11.18https://github.com/alexpghayes.png?size=400alexpghayes r-universe repositoryhttps://alexpghayes.r-universe.devThu, 30 Jun 2022 13:41:13 GMT[rohelab] fastRG 0.3.1.9000alexpghayes@gmail.com (Alex Hayes)Samples generalized random product graph, 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.https://github.com/r-universe/rohelab/actions/runs/2764649528Thu, 30 Jun 2022 13:41:13 GMTfastRG0.3.1.9000successhttps://rohelab.r-universe.devhttps://github.com/rohelab/fastrg[alexpghayes] distributions3 0.1.2.9000alexpghayes@gmail.com (Alex Hayes)Tools to create and manipulate probability distributions
using S3. Generics random(), pdf(), cdf() and quantile() provide
replacements for base R's r/d/p/q 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.https://github.com/r-universe/alexpghayes/actions/runs/2709471745Tue, 21 Jun 2022 19:59:40 GMTdistributions30.1.2.9000successhttps://alexpghayes.r-universe.devhttps://github.com/alexpghayes/distributions3intro-to-hypothesis-testing.Rmdintro-to-hypothesis-testing.htmlIntro to hypothesis testing2019-05-09 18:27:472022-02-22 17:09:49one-sample-sign-tests.Rmdone-sample-sign-tests.htmlOne sample sign tests2019-05-14 19:37:292022-02-22 17:09:49one-sample-t-test.Rmdone-sample-t-test.htmlOne sample T-tests2019-05-14 15:51:052022-02-22 17:09:49one-sample-z-test.Rmdone-sample-z-test.htmlOne sample Z-tests2019-05-09 18:27:472022-02-22 17:09:49one-sample-z-test-for-proportion.Rmdone-sample-z-test-for-proportion.htmlOne sample Z-tests for a proportion2019-05-14 15:51:052022-02-22 17:09:49one-sample-t-confidence-interval.Rmdone-sample-t-confidence-interval.htmlone-sample-t-confidence-interval2019-05-14 15:51:052022-02-22 17:09:49paired-tests.Rmdpaired-tests.htmlPaired tests2019-05-14 19:37:292022-02-22 17:09:49poisson.Rmdpoisson.htmlThe Poisson distribution: From basic probability theory to regression models2022-02-22 16:59:532022-02-22 17:09:49two-sample-z-test.Rmdtwo-sample-z-test.htmlTwo sample Z-tests2019-05-14 15:51:052022-02-22 17:09:49one-sample-z-confidence-interval.Rmdone-sample-z-confidence-interval.htmlZ confidence interval for a mean2019-05-09 18:27:472022-02-22 17:09:49[rohelab] vsp 0.1.0.9000alexpghayes@gmail.com (Alex Hayes)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.https://github.com/r-universe/rohelab/actions/runs/2859049307Sun, 20 Feb 2022 23:44:36 GMTvsp0.1.0.9000successhttps://rohelab.r-universe.devhttps://github.com/rohelab/vspbff.Rmdbff.htmlbff2020-01-31 19:08:372022-02-09 08:54:25[rohelab] invertiforms 0.1.0.9000alexpghayes@gmail.com (Alex Hayes)Provides composable invertible transforms for
(sparse) matrices.https://github.com/r-universe/rohelab/actions/runs/2858860927Wed, 16 Feb 2022 19:37:48 GMTinvertiforms0.1.0.9000successhttps://rohelab.r-universe.devhttps://github.com/rohelab/invertiforms[rohelab] fastadi 0.1.0.9000alexpghayes@gmail.com (Alex Hayes)Implements the AdaptiveImpute matrix completion
algorithm of 'Intelligent Initialization and Adaptive Thresholding for
Iterative Matrix Completion',
<https://amstat.tandfonline.com/doi/abs/10.1080/10618600.2018.1518238>.
AdaptiveImpute is useful for embedding sparsely observed matrices,
often out performs competing matrix completion algorithms, and
self-tunes its hyperparameter, making usage easy.https://github.com/r-universe/rohelab/actions/runs/2858978347Wed, 16 Feb 2022 19:36:37 GMTfastadi0.1.0.9000successhttps://rohelab.r-universe.devhttps://github.com/rohelab/fastadi[rohelab] LRMF3 0.1.0.9000alexpghayes@gmail.com (Alex Hayes)Provides S3 classes to represent low rank matrix
decompositions.https://github.com/r-universe/rohelab/actions/runs/2858920011Wed, 09 Feb 2022 19:26:08 GMTLRMF30.1.0.9000failurehttps://rohelab.r-universe.devhttps://github.com/rohelab/lrmf3[rohelab] sparseLRMatrix 0.1.0.9000alexpghayes@gmail.com (Alex Hayes)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.https://github.com/r-universe/rohelab/actions/runs/2858920066Tue, 02 Mar 2021 18:50:58 GMTsparseLRMatrix0.1.0.9000successhttps://rohelab.r-universe.devhttps://github.com/rohelab/sparselrmatrix[alexpghayes] modeltests 0.1.4alexpghayes@gmail.com (Alex Hayes)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.https://github.com/r-universe/alexpghayes/actions/runs/2865442793Wed, 13 Jan 2021 16:50:52 GMTmodeltests0.1.4successhttps://alexpghayes.r-universe.devhttps://github.com/alexpghayes/modeltests