Package: SEMdeep 1.1.1

SEMdeep: Structural Equation Modeling with Deep Neural Network and Machine Learning Algorithms

Training and validation of a custom (or data-driven) Structural Equation Models using Deep Neural Networks or Machine Learning algorithms, which extend the fitting procedures of the 'SEMgraph' R package <doi:10.32614/CRAN.package.SEMgraph>.

Authors:Mario Grassi [aut], Barbara Tarantino [cre]

SEMdeep_1.1.1.tar.gz
SEMdeep_1.1.1.zip(r-4.7-any)SEMdeep_1.1.1.zip(r-4.6-any)SEMdeep_1.1.1.zip(r-4.5-any)
SEMdeep_1.1.1.tgz(r-4.6-any)SEMdeep_1.1.1.tgz(r-4.5-any)
SEMdeep_1.1.1.tar.gz(r-4.7-any)SEMdeep_1.1.1.tar.gz(r-4.6-any)
SEMdeep_1.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SEMdeep/json (API)

# Install 'SEMdeep' in R:

install.packages('SEMdeep', repos = c('https://barbaratarantino.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/barbaratarantino/semdeep/issues

On CRAN:

4.26 score 12 stars 233 downloads 16 exports 134 dependencies

Last updated from:f344438cf1. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	201
source / vignettes	OK	222
linux-release	OK	195
macos-release	OK	235
macos-oldrel	OK	252
windows-devel	OK	133
windows-release	OK	144
windows-oldrel	OK	156
wasm-release	OK	132

Exports:classificationReport crossValidation getConnectionWeight getGradientWeight getLOCO getShapleyR2 getSignificanceTest getVariableImportance mapGraph nplot predict.DNN predict.ML predict.SEM SEMdnn SEMml trainingReport

Dependencies:AnnotationDbi askpass aspect BH Biobase BiocBaseUtils BiocGenerics BiocManager Biostrings bit bit64 bitops blob boot cachem callr cherry class cli codetools coro corpcor cpp11 crayon curl dagitty data.table DBI desc digest dir.expiry doFuture dplyr e1071 farver fastmap filelock flip foreach future future.apply gdata generics ggm ggplot2 glasso glmnet globals glue graph graphite gtable gtools hms hommel httr igraph IRanges isoband iterators jsonlite KEGGREST kernelshap labeling lattice lavaan lifecycle listenv lpSolve magrittr MASS Matrix memoise mgcv mime mnormt mvtnorm NeuralNetTools nlme nnet numDeriv openssl otel parabar parallelly pbapply pbivnorm pillar pkgconfig plyr png prettyunits processx progress protoclust proxy ps purrr quadprog R6 ranger rappdirs RBGL RColorBrewer Rcpp RcppEigen reshape2 Rgraphviz rlang rpart RSQLite S4Vectors S7 safetensors scales SEMgraph Seqinfo shape someMTP stringi stringr survival sys tibble tidyr tidyselect torch utf8 V8 vctrs viridisLite withr xgboost XVector

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Prediction evaluation report of a classification model	classificationReport
Cross-validation of linear SEM, ML or DNN training models	crossValidation
Connection Weight method for neural network variable importance	getConnectionWeight
Gradient Weight method for neural network variable importance	getGradientWeight
Compute variable importance using LOCO values	getLOCO
Compute variable importance using Shapley (R2) values	getShapleyR2
Test for the significance of neural network input nodes	getSignificanceTest
Variable importance for Machine Learning models	getVariableImportance
Map additional variables (nodes) to a graph object	mapGraph
Create a plot for a neural network model	nplot
SEM-based out-of-sample prediction using DNN	predict.DNN
SEM-based out-of-sample prediction using nodewise ML	predict.ML
SEM-based out-of-sample prediction using layer-wise ordering	predict.SEM
SEM train with Deep Neural Netwok (DNN) models	SEMdnn
Nodewise SEM train using Machine Learning (ML)	SEMml
Training history evaluation of a DNN model	trainingReport