Package: NNbenchmark 3.2.1

Patrice Kiener

NNbenchmark: Datasets and Functions to Benchmark Neural Network Packages

Datasets and functions to benchmark (convergence, speed, ease of use) R packages dedicated to regression with neural networks (no classification in this version). The templates for the tested packages are available in the R, R Markdown and HTML formats at <https://github.com/pkR-pkR/NNbenchmarkTemplates> and <https://theairbend3r.github.io/NNbenchmarkWeb/index.html>. The submitted article to the R-Journal can be read at <https://www.inmodelia.com/gsoc2020.html>.

Authors:Patrice Kiener [aut, cre], Christophe Dutang [aut], Salsabila Mahdi [aut], Akshaj Verma [aut], Yifu Yan [ctb]

# Install 'NNbenchmark' in R:

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

Datasets:

On CRAN:

Last updated 10 months agofrom:504d48eaea. Checks:OK: 7. Indexed: yes.

Help page	Topics
Package NNbenchmark	NNbenchmark-package NNbenchmark
Create or Append a data.frame to a csv File	add2csv
Dataset bWoodN1	bWoodN1
Concatenates List and Vectors into a List	cc
Detach the Loaded Packages and the ZZ object	clearNN detachNN
Create a timer object. Get the data frame of a timer object	createTimer getTimer
Calculate the RMSE, MSE, MAE, and WAE Rounded to 4 digits	funMAE funMSE funRMSE funWAE
Concatenates head() and tail() in vector, list, matrix, data.frame, array	ht ht9
Dataset mDette	mDette
Dataset mFriedman	mFriedman
Dataset mIshigami	mIshigami
Dataset mRef153	mRef153
Big Datasets in One list (2020)	NNbigdatasets
All Datasets in One List	NNdatasets
A Summary (in data.frame format) of NNdatasets	NNdataSummary
Summarize Calculations of RMSE, MSE, MAE, and WAE	NNsummary
Generic Functions for Training and Predicting	NNtrainPredict trainPredict_1data trainPredict_1mth1data trainPredict_1pkg
Create a Plot, Add Lines or Points Depending The Context	lipoNN plotNN
Prepare a Dataset For All Possible Formats	prepareZZ
A R6 Class to represent a timer.	timeR
Collect the difftime between two events	timediff timestart
Dataset uDmod1	uDmod1
Dataset uDmod2	uDmod2
Dataset uDreyfus1	uDreyfus1
Dataset uDreyfus2	uDreyfus2
Dataset uGauss1	uGauss1
Dataset uGauss2	uGauss2
Dataset uGauss3	uGauss3
Dataset uNeuroOne	uNeuroOne