The ukFaculty dataset | A first attempt | Attempting to fit with ERGM
Package: lolog 1.3.2
Ian E. Fellows
lolog: Latent Order Logistic Graph Models
Estimation of Latent Order Logistic (LOLOG) Models for Networks. LOLOGs are a flexible and fully general class of statistical graph models. This package provides functions for performing MOM, GMM and variational inference. Visual diagnostics and goodness of fit metrics are provided. See Fellows (2018) <doi:10.48550/arXiv.1804.04583> for a detailed description of the methods.
Authors:
lolog_1.3.2.tar.gz
lolog_1.3.2.zip(r-4.7-x86_64)lolog_1.3.2.zip(r-4.7-arm64)lolog_1.3.2.zip(r-4.6-x86_64)lolog_1.3.2.zip(r-4.6-arm64)lolog_1.3.2.zip(r-4.5-x86_64)
lolog_1.3.2.tgz(r-4.6-x86_64)lolog_1.3.2.tgz(r-4.6-arm64)lolog_1.3.2.tgz(r-4.5-x86_64)lolog_1.3.2.tgz(r-4.5-arm64)
lolog_1.3.2.tar.gz(r-4.7-arm64)lolog_1.3.2.tar.gz(r-4.7-x86_64)lolog_1.3.2.tar.gz(r-4.6-arm64)lolog_1.3.2.tar.gz(r-4.6-x86_64)
lolog_1.3.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
lolog/json (API)
| # Install 'lolog' in R: |
| install.packages('lolog', repos = c('https://statnet.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/statnet/lolog/issues
Datasets:
Last updated from:87aa3b4d61. Checks:13 WARNING, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | WARNING | 225 | ||
| linux-devel-x86_64 | WARNING | 235 | ||
| source / vignettes | OK | 1825 | ||
| linux-release-arm64 | WARNING | 258 | ||
| linux-release-x86_64 | WARNING | 255 | ||
| macos-release-arm64 | WARNING | 234 | ||
| macos-release-x86_64 | WARNING | 418 | ||
| macos-oldrel-arm64 | WARNING | 223 | ||
| macos-oldrel-x86_64 | WARNING | 420 | ||
| windows-devel-arm64 | WARNING | 348 | ||
| windows-devel-x86_64 | WARNING | 304 | ||
| windows-release-arm64 | WARNING | 358 | ||
| windows-release-x86_64 | WARNING | 245 | ||
| windows-oldrel-x86_64 | WARNING | 264 | ||
| wasm-release | OK | 141 |
Exports:_lolog_initStats_rcpp_module_boot_lologas.BinaryNetas.BinaryNet.defaultas.networkas.network.Rcpp_DirectedNetas.network.Rcpp_UndirectedNetcalculateStatisticscoef.lologcreateCppModelcreateLatentOrderLikelihoodDirectedLatentOrderLikelihoodDirectedModelDirectedNetgofitgofit.lologinitLologStatisticsinlineLologPluginlologlologPackageSkeletonlologVariationalplot.gofitplot.lologGmmplot.Rcpp_DirectedNetplot.Rcpp_UndirectedNetprint.gofitprint.lologprint.lologVariationalFitregisterDirectedStatisticregisterUndirectedStatisticrunLologCppTestssimulate.lologsummary.lologUndirectedLatentOrderLikelihoodUndirectedModelUndirectedNet
Dependencies:BHclicodacpp11farverggplot2gluegtableigraphintergraphisobandlabelinglatticelifecyclemagrittrMatrixnetworkpillarpkgconfigplyrR6RColorBrewerRcppreshape2rlangS7scalesstatnet.commonstringistringrtibbleutf8vctrsviridisLitewithr
An Example Analysis Using LOLOG
Last update: 2024-01-03
Started: 2018-04-10
An Introduction to Latent Order Logistic (LOLOG) Network Models
Introduction to LOLOG | Getting Started | Statistical Network Modeling: The lolog Command and lolog Object | An Erdos-Renyi Model | A Triangle Model | The Effect of Wealth | Does Large Wealth Inequality Matter? | Reciprocity | Modeling Larger Networks | Specifying Vertex Inclusion Order | Model Terms Available for LOLOG Estimation and Simulation | Terms Provided with lolog | Coding New Terms | Network Simulation and Computation | Working with BinaryNet Objects | Order Dependent Terms | Examining the Quality of Model Fit - GOF
Last update: 2020-12-16
Started: 2018-03-21
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| indexing | [ [,Rcpp_DirectedNet,ANY,ANY,ANY-method [,Rcpp_DirectedNet-method [,Rcpp_UndirectedNet,ANY,ANY,ANY-method [,Rcpp_UndirectedNet-method [<- [<-,Rcpp_DirectedNet,ANY,ANY,ANY-method [<-,Rcpp_DirectedNet-method [<-,Rcpp_UndirectedNet,ANY,ANY,ANY-method [<-,Rcpp_UndirectedNet-method \S4method{[<-}{Rcpp_DirectedNet,ANY,ANY,ANY} \S4method{[<-}{Rcpp_UndirectedNet,ANY,ANY,ANY} \S4method{[}{Rcpp_DirectedNet,ANY,ANY,ANY} \S4method{[}{Rcpp_UndirectedNet,ANY,ANY,ANY} |
| Convert to either an UndirectedNet or DirectedNet object | as.BinaryNet |
| Convert to either an UndirectedNet or DirectedNet object | as.BinaryNet.default |
| Network conversion | as.network |
| Convert a DirectedNet to a network object | as.network.Rcpp_DirectedNet |
| Convert a UndirectedNet to a network object | as.network.Rcpp_UndirectedNet |
| BinaryNet | BinaryNet DirectedNet Rcpp_DirectedNet-class Rcpp_UndirectedNet-class UndirectedNet |
| Calculate network statistics from a formula | calculateStatistics |
| Internal Symbols | call-symbols initLologStatistics runLologCppTests _lolog_initStats _rcpp_module_boot_lolog |
| Extracts estimated model coefficients. | coef.lolog |
| Creates a model | createCppModel |
| Creates a probability model for a latent ordered network model | createLatentOrderLikelihood |
| Conduct goodness of fit diagnostics | gofit |
| Goodness of Fit Diagnostics for a LOLOG fit | gofit.lolog |
| An lolog plug-in for easy C++ prototyping and access | inlineLologPlugin |
| LatentOrderLikelihood | DirectedLatentOrderLikelihood LatentOrderLikelihood Rcpp_DirectedLatentOrderLikelihood-class Rcpp_UndirectedLatentOrderLikelihood-class UndirectedLatentOrderLikelihood |
| Collaboration Relationships Among Partners at a New England Law Firm | lazega |
| Fits a LOLOG model via Monte Carlo Generalized Method of Moments | lolog |
| LOLOG Model Terms | lolog-terms |
| Models | DirectedModel LologModels Rcpp_DirectedModel-class Rcpp_UndirectedModel-class UndirectedModel |
| Create a skeleton for a package extending lolog | lologPackageSkeleton |
| Fits a latent ordered network model using Monte Carlo variational inference | lologVariational |
| Plots a gofit object | plot.gofit |
| Conduct Monte Carlo diagnostics on a lolog model fit | plot.lologGmm |
| plot an DirectedNet object | plot.Rcpp_DirectedNet |
| Plot an UndirectedNet object | plot.Rcpp_UndirectedNet |
| prints a gofit object | print.gofit |
| Print a `lolog` object | print.lolog |
| Print of a lologVariationalFit object | print.lologVariationalFit |
| Register Statistics | registerDirectedOffset registerDirectedStatistic registerUndirectedOffset registerUndirectedStatistic |
| Generates BinaryNetworks from a fit lolog object | simulate.lolog |
| Summary of a `lolog` object | summary.lolog |
| Friendship network of a UK university faculty | ukFaculty |