Package: missForest
Type: Package
Title: Nonparametric Missing Value Imputation using Random Forest
Version: 1.4
Date: 2013-12-31
Author: Daniel J. Stekhoven <stekhoven@stat.math.ethz.ch>
Maintainer: Daniel J. Stekhoven <stekhoven@stat.math.ethz.ch>
Depends: randomForest, foreach, itertools
Description: The function 'missForest' in this package is used to
impute missing values particularly in the case of mixed-type
data. It uses a random forest trained on the observed values of
a data matrix to predict the missing values. It can be used to
impute continuous and/or categorical data including complex
interactions and non-linear relations. It yields an out-of-bag
(OOB) imputation error estimate without the need of a test set
or elaborate cross-validation. It can be run in parallel to
save computation time.
License: GPL (>= 2)
URL: http://www.r-project.org
Packaged: 2013-12-31 14:28:06 UTC; DSQuantik
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-12-31 16:17:04
Package: MAVTgsa
Type: Package
Title: Three methods to identify differentially expressed gene sets,
ordinary least square test, Multivariate Analysis Of Variance
test with n contrasts and Random forest.
Version: 1.3
Date: 2014-05-27
Author: Chih-Yi Chien, Chen-An Tsai, Ching-Wei Chang, and James J. Chen
Maintainer: Chih-Yi Chien <92354503@nccu.edu.tw>
Depends: R (>= 2.13.2), corpcor, foreach, multcomp, randomForest, MASS
Description: This package is a gene set analysis function for one-sided test (OLS), two-sided test (multivariate analysis of variance).
If the experimental conditions are equal to 2, the p-value for Hotelling's t^2 test is calculated.
If the experimental conditions are great than 2, the p-value for Wilks' Lambda is determined and post-hoc test is reported too.
Three multiple comparison procedures, Dunnett, Tukey, and sequential pairwise comparison, are implemented.
The program computes the p-values and FDR (false discovery rate) q-values for all gene sets.
The p-values for individual genes in a significant gene set are also listed.
MAVTgsa generates two visualization output: a p-value plot of gene sets (GSA plot) and a GST-plot of the empirical distribution function of the ranked test statistics of a given gene set.
A Random Forests-based procedure is to identify gene sets that can accurately predict samples from different experimental conditions or are associated with the continuous phenotypes.
License: GPL-2
LazyData: Yes
Repository: CRAN
Packaged: 2014-06-30 03:41:28 UTC; pelly
NeedsCompilation: no
Date/Publication: 2014-07-02 13:48:35
Package: MixRF
Title: A Random-Forest-Based Approach for Imputing Clustered Incomplete
Data
Version: 1.0
Date: 2016-04-05
Author: Jiebiao Wang and Lin S. Chen
Maintainer: Jiebiao Wang <randel.wang@gmail.com>
Description: It offers random-forest-based functions to impute clustered
incomplete data. The package is tailored for but not limited to imputing
multitissue expression data, in which a gene's expression is measured on the
collected tissues of an individual but missing on the uncollected tissues.
License: GPL
Depends: doParallel, randomForest, lme4, foreach
URL: https://github.com/randel/MixRF
BugReports: https://github.com/randel/MixRF/issues
RoxygenNote: 5.0.1
NeedsCompilation: no
Packaged: 2016-04-06 02:05:06 UTC; JWang
Repository: CRAN
Date/Publication: 2016-04-06 09:43:04
Package: ModelMap
Type: Package
Title: Modeling and Map Production using Random Forest and Stochastic
Gradient Boosting
Version: 3.3.2
Date: 2016-02-25
Depends: R (>= 2.13.0), randomForest, raster, rgdal
Suggests: party, quantregForest, gbm
Imports:
graphics,grDevices,stats,utils,mgcv,corrplot,fields,HandTill2001,PresenceAbsence
Author: Elizabeth Freeman, Tracey Frescino
Maintainer: Elizabeth Freeman <eafreeman@fs.fed.us>
Description: Creates sophisticated models of training data and validates the models with an independent test set, cross validation, or in the case of Random Forest Models, with Out Of Bag (OOB) predictions on the training data. Create graphs and tables of the model validation results. Applies these models to GIS .img files of predictors to create detailed prediction surfaces. Handles large predictor files for map making, by reading in the .img files in chunks, and output to the .txt file the prediction for each data chunk, before reading the next chunk of data.
License: Unlimited
NeedsCompilation: no
Packaged: 2016-02-25 20:21:48 UTC; eafreeman
Repository: CRAN
Date/Publication: 2016-02-25 23:54:07
Package: RFgroove
Type: Package
Title: Importance Measure and Selection for Groups of Variables with
Random Forests
Version: 1.1
Date: 2016-03-16
Author: Baptiste Gregorutti
Maintainer: Baptiste Gregorutti <baptiste.gregorutti@safety-line.fr>
Description: Variable selection tools for groups of variables and functional data based on a new grouped variable importance with random forests.
License: GPL (>= 2.0)
Depends: randomForest, wmtsa, fda
Packaged: 2016-03-17 09:22:35 UTC; bapt
NeedsCompilation: yes
Repository: CRAN
Date/Publication: 2016-03-17 13:20:10
Package: AUCRF
Type: Package
Title: Variable Selection with Random Forest and the Area Under the
Curve
Version: 1.1
Date: 2012-03-19
Author: Victor Urrea, M.Luz Calle
Maintainer: Victor Urrea <victor.urrea@uvic.cat>
Depends: R (>= 2.11.0), randomForest
Description: Variable selection using Random Forest based on optimizing
the area-under-the ROC curve (AUC) of the Random Forest.
License: GPL (>= 2)
LazyLoad: yes
Packaged: 2012-03-19 08:32:38 UTC; victor
Repository: CRAN
Date/Publication: 2012-03-19 11:12:24
Package: D2C
Type: Package
Title: Predicting Causal Direction from Dependency Features
Version: 1.2.1
Date: 2015-01-14
Author: Gianluca Bontempi, Catharina Olsen, Maxime Flauder
Maintainer: Catharina Olsen <colsen@ulb.ac.be>
Description: The relationship between statistical dependency and causality lies
at the heart of all statistical approaches to causal inference. The D2C
package implements a supervised machine learning approach to infer the
existence of a directed causal link between two variables in multivariate
settings with n>2 variables. The approach relies on the asymmetry of some
conditional (in)dependence relations between the members of the Markov
blankets of two variables causally connected. The D2C algorithm predicts
the existence of a direct causal link between two variables in a
multivariate setting by (i) creating a set of of features of the
relationship based on asymmetric descriptors of the multivariate dependency
and (ii) using a classifier to learn a mapping between the features and the
presence of a causal link
License: Artistic-2.0
Depends: R (>= 2.10.0), randomForest
Imports: gRbase, lazy, RBGL, MASS, corpcor, methods, Rgraphviz, foreach
LazyData: true
Packaged: 2015-01-20 15:26:57 UTC; bontempi
Suggests: knitr
VignetteBuilder: knitr
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2015-01-21 00:23:55