CRAN/E | familiar

familiar

End-to-End Automated Machine Learning and Model Evaluation

Installation

About

Single unified interface for end-to-end modelling of regression, categorical and time-to-event (survival) outcomes. Models created using familiar are self-containing, and their use does not require additional information such as baseline survival, feature clustering, or feature transformation and normalisation parameters. Model performance, calibration, risk group stratification, (permutation) variable importance, individual conditional expectation, partial dependence, and more, are assessed automatically as part of the evaluation process and exported in tabular format and plotted, and may also be computed manually using export and plot functions. Where possible, metrics and values obtained during the evaluation process come with confidence intervals.

Citation familiar citation info
github.com/alexzwanenburg/familiar
Bug report File report

Key Metrics

Version 1.4.1
R ≥ 4.0.0
Published 2022-12-17 287 days ago
Needs compilation? no
License EUPL
CRAN checks familiar results

Downloads

Last 24 hours 0 -100%
Last 7 days 58 -38%
Last 30 days 297 -15%
Last 90 days 891 -15%
Last 365 days 4.255 +61%

Maintainer

Maintainer

Alex Zwanenburg

alexander.zwanenburg@nct-dresden.de

Authors

Alex Zwanenburg

aut / cre

Steffen Löck

aut

Stefan Leger

ctb

Iram Shahzadi

ctb

Asier Rabasco Meneghetti

ctb

Sebastian Starke

ctb

Technische Universität Dresden

cph

German Cancer Research Center

cph

(DKFZ)

Material

NEWS
Reference manual
Package source

Vignettes

Evaluation and explanation
Feature selection methods
Introducing familiar
Learning algorithms and hyperparameter optimisation
Performance metrics
Using familiar prospectively

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

familiar archive

Depends

R ≥ 4.0.0

Imports

data.table
methods
rlang ≥ 0.3.4
rstream
survival

Suggests

BART
callr ≥ 3.4.3
cluster
CORElearn
coro
dynamicTreeCut
e1071 ≥ 1.7.5
Ecdat
fastcluster
fastglm
ggplot2 ≥ 3.0.0
glmnet
gtable
harmonicmeanp
isotree ≥0.3.0
knitr
labeling
laGP
MASS
maxstat
mboost ≥2.9.0
microbenchmark
nnet
partykit
praznik
proxy
qvalue
randomForestSRC
ranger
rmarkdown
scales
testthat ≥3.0.0
xml2
VGAM
xgboost