mboost statistics

Tool stats & trends

Looking to identify usage trends or leading experts?

Protocols

mboost specifications

Information


Unique identifier OMICS_15707
Name mboost
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 2.0
Computer skills Advanced
Version 2.9-1
Stability Stable
Requirements
methods, stats, RColorBrewer, parallel, graphics, Matrix, splines, utils, grDevices, lattice, MASS, fields, survival, quadprog, R(≥3.2.0), nnet, randomforest, nnls, mlbench, stabs(≥0.5-0), partykit(≥1.2-1), TH.data, BayesX, gbm, rpart(≥4.0-3), testthat(≥0.10.0), kangar00
Maintained Yes

Versioning


No version available

Documentation


Maintainer


  • person_outline Benjamin Hofner

Publication for mboost

mboost citations

 (30)
library_books

Seasonal asthma in Melbourne, Australia, and some observations on the occurrence of thunderstorm asthma and its predictability

2018
PLoS One
PMCID: 5896915
PMID: 29649224
DOI: 10.1371/journal.pone.0194929

[…] tistical computing environment was used for all analyses []. The R package mgcv was used for fitting the GAMs with penalised regression splines []. Gradient boosting was performed using the R package mboost []. […]

library_books

Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound

2017
PMCID: 5577694
PMID: 28854966
DOI: 10.1186/s40001-017-0270-0

[…] erage R 2 statistic on validation datasets.Calculations were carried out using the R system for statistical computing (version 3.0.1; R Core Team, Vienna, Austria, 2013). Particularly, the R packages mboost (version 2.2-3), randomForest (version 4.6-7) and glmnet (version 1.9-5) were used to fit boosting, random forest and lasso models. […]

library_books

An Update on Statistical Boosting in Biomedicine

2017
PMCID: 5558647
PMID: 28831290
DOI: 10.1155/2017/6083072

[…] tistate models for patients exposed to competing risks (e.g., adverse events, recovery, death, or relapse). The approach is implemented in the gamboostMSM package [], relying on the infrastructure of mboost. Möst and Hothorn [] focused on boosting the patient-specific survivor function based on conditional transformation models [] incorporating inverse probability of censoring weights [].When stat […]

library_books

A Multicriteria Approach to Find Predictive and Sparse Models with Stable Feature Selection for High Dimensional Data

2017
PMCID: 5556617
PMID: 28835769
DOI: 10.1155/2017/7907163

[…] s [] is used to roll out the experiments on a high performance computing cluster. For the classification and filter methods, we additionally rely on the R packages fmrmr [], kernlab [], LiblineaR [], mboost [], ranger [], and ROCR []. […]

library_books

Prediction of aboveground grassland biomass on the Loess Plateau, China, using a random forest algorithm

2017
Sci Rep
PMCID: 5537351
PMID: 28761059
DOI: 10.1038/s41598-017-07197-6

[…] Error statistics were calculated for the predicted grassland AGB, and the residuals of the RF model were compared with the predictions obtained using other machine learning models (bagging, mboost, and SVM). The error statistics included the ME, MAE and RMSE, and their formulas are as follows:2ME=1N∑i=1N(Y−X) 3MAE=1N∑i=1N|Y−X| 4RMSE=1N∑i=1N(Y−X)2In addition, R and λ were used to measure […]

library_books

Probing for Sparse and Fast Variable Selection with Model Based Boosting

2017
PMCID: 5555005
PMID: 28831289
DOI: 10.1155/2017/1421409

[…] ple their position in the vector in each run to allow for varying correlation patterns among the informative variables. For variable selection with cross-validation, 25-fold bootstrap (the default in mboost) is used to determine the final number of iterations. Different configurations of stability selection were tested to investigate whether and, if so, to what extent these settings affect the sel […]

Citations

Looking to check out a full list of citations?

mboost institution(s)
Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany; Seminar für Statistik, ETH Zürich, Zürich, Switzerland
mboost funding source(s)
This work was supported by Deutsche Forschungsgemeinschaft (DFG) under grant HO3242/1-1.

mboost reviews

star_border star_border star_border star_border star_border
star star star star star

Be the first to review mboost