Last data update: 2014.03.03

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gamsel : Fit Regularization Path for Generalized Additive Models

Package: gamsel
Type: Package
Title: Fit Regularization Path for Generalized Additive Models
Version: 1.7-3
Date: 2015-06-18
Author: Alexandra Chouldechova and Trevor Hastie
Maintainer: Trevor Hastie <>
Description: Using overlap grouped lasso penalties, gamsel selects whether a term in a gam is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families.
License: GPL-2
Depends: mda, foreach
NeedsCompilation: yes
Packaged: 2015-06-19 05:44:32 UTC; hastie
Repository: CRAN
Date/Publication: 2015-06-19 08:45:13

● Data Source: CranContrib
● 0 images, 11 functions, 0 datasets
● Reverse Depends: 0

gdm : Functions for Generalized Dissimilarity Modeling

Package: gdm
Type: Package
Title: Functions for Generalized Dissimilarity Modeling
Version: 1.2.3
Date: 2016-04-15
Author: Glenn Manion, Matthew Lisk, Simon Ferrier, Diego Nieto-Lugilde, Matthew C. Fitzpatrick
Maintainer: Matthew C. Fitzpatrick <>
Description: A toolkit with functions to fit, plot, and summarize Generalized Dissimilarity Models.
License: GPL-2
Depends: R (>= 2.15.2), raster, foreach, doParallel, parallel
LinkingTo: Rcpp
Imports: Rcpp (>= 0.10.4), reshape2, vegan
Suggests: R.rsp
VignetteBuilder: R.rsp
NeedsCompilation: yes
Packaged: 2016-04-15 19:36:47 UTC; mlisk
Repository: CRAN
Date/Publication: 2016-04-16 04:54:18

● Data Source: CranContrib
● 0 images, 14 functions, 3 datasets
● Reverse Depends: 0

localgauss : Estimating Local Gaussian Parameters

Package: localgauss
Type: Package
Title: Estimating Local Gaussian Parameters
Version: 0.35
Date: 2016-12-01
Author: Tore Selland Kleppe <>
Maintainer: Tore Selland Kleppe <>
Depends: ggplot2, MASS, foreach, matrixStats
Description: Computational routines for estimating and visualizing local Gaussian parameters. Local Gaussian parameters are useful for characterizing and testing for non-linear dependence within bivariate data. See e.g. Tjostheim and Hufthammer, Local Gaussian correlation: A new measure of dependence, Journal of Econometrics, 2013, Volume 172 (1), pages 33-48.
License: GPL-2
LazyLoad: yes
NeedsCompilation: yes
Packaged: 2016-01-12 09:31:53 UTC; Tore
Repository: CRAN
Date/Publication: 2016-01-12 12:13:16

● Data Source: CranContrib
● 0 images, 4 functions, 0 datasets
● Reverse Depends: 0

missForest : Nonparametric Missing Value Imputation using Random Forest

Package: missForest
Type: Package
Title: Nonparametric Missing Value Imputation using Random Forest
Version: 1.4
Date: 2013-12-31
Author: Daniel J. Stekhoven <>
Maintainer: Daniel J. Stekhoven <>
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)
Packaged: 2013-12-31 14:28:06 UTC; DSQuantik
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-12-31 16:17:04

● Data Source: CranContrib
● 0 images, 6 functions, 0 datasets
Reverse Depends: 1

multicon : Multivariate Constructs

Package: multicon
Type: Package
Title: Multivariate Constructs
Version: 1.6
Date: 2015-1-28
Author: Ryne A. Sherman
Maintainer: Ryne A. Sherman <>
Description: Includes functions designed to examine relationships among multivariate constructs (e.g., personality). This includes functions for profile (within-person) analysis, dealing with large numbers of analyses, lens model analyses, and structural summary methods for data with circumplex structure. The package also includes functions for graphically comparing and displaying group means.
License: GPL-2
Depends: R (>= 3.0.0), psych, abind, foreach
Imports: mvtnorm, sciplot,
NeedsCompilation: no
Packaged: 2015-02-01 19:22:39 UTC; davidserfass
Repository: CRAN
Date/Publication: 2015-02-02 01:38:16

● Data Source: CranContrib
● 0 images, 56 functions, 18 datasets
● Reverse Depends: 0

DNMF : Discriminant Non-Negative Matrix Factorization

Package: DNMF
Version: 1.3
Date: 2015-06-09
Title: Discriminant Non-Negative Matrix Factorization
Authors@R: c(person("Zhilong", "Jia", role =c("aut", "cre"),
person("Xiang", "Zhang", role = "aut", email=""))
Description: Discriminant Non-Negative Matrix Factorization aims to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner. It refers to three article, Zafeiriou, Stefanos, et al. "Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification." Neural Networks, IEEE Transactions on 17.3 (2006): 683-695. Kim, Bo-Kyeong, and Soo-Young Lee. "Spectral Feature Extraction Using dNMF for Emotion Recognition in Vowel Sounds." Neural Information Processing. Springer Berlin Heidelberg, 2013. and Lee, Soo-Young, Hyun-Ah Song, and Shun-ichi Amari. "A new discriminant NMF algorithm and its application to the extraction of subtle emotional differences in speech." Cognitive neurodynamics 6.6 (2012): 525-535.
Depends: foreach
Imports: Matrix, gplots, parallel, doParallel
License: GPL (>= 2)
LazyData: true
NeedsCompilation: no
Packaged: 2015-06-09 15:05:15 UTC; zjia
Author: Zhilong Jia [aut, cre],
Xiang Zhang [aut]
Maintainer: Zhilong Jia <>
Repository: CRAN
Date/Publication: 2015-06-09 21:29:09

● Data Source: CranContrib
● 0 images, 4 functions, 0 datasets
● Reverse Depends: 0

MAVTgsa : Three methods to identify differentially expressed gene sets, ordinary least square test, Multivariate Analysis Of Variance test with n contrasts and Random forest.

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 <>
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

● Data Source: CranContrib
1 images, 12 functions, 0 datasets
● Reverse Depends: 0

MetaQC : MetaQC: Objective Quality Control and Inclusion/Exclusion Criteria for Genomic Meta-Analysis

Package: MetaQC
Type: Package
Title: MetaQC: Objective Quality Control and Inclusion/Exclusion
Criteria for Genomic Meta-Analysis
Version: 0.1.13
Author: Don Kang <> and George Tseng
Maintainer: Don Kang <>
Description: MetaQC implements our proposed quantitative quality
control measures: (1) internal homogeneity of co-expression
structure among studies (internal quality control; IQC); (2)
external consistency of co-expression structure correlating
with pathway database (external quality control; EQC); (3)
accuracy of differentially expressed gene detection (accuracy
quality control; AQCg) or pathway identification (AQCp); (4)
consistency of differential expression ranking in genes
(consistency quality control; CQCg) or pathways (CQCp). (See
the reference for detailed explanation.) For each quality
control index, the p-values from statistical hypothesis testing
are minus log transformed and PCA biplots were applied to
assist visualization and decision. Results generate systematic
suggestions to exclude problematic studies in microarray
meta-analysis and potentially can be extended to GWAS or other
types of genomic meta-analysis. The identified problematic
studies can be scrutinized to identify technical and biological
causes (e.g. sample size, platform, tissue collection,
preprocessing etc) of their bad quality or irreproducibility
for final inclusion/exclusion decision.
Depends: R (>= 2.10.0), proto, foreach, iterators
Suggests: doMC, doSNOW, FactoMineR, matrixStats, gdata, gtools,
License: GPL-2
LazyLoad: yes
Collate: MetaQC.R requireAll.R functions.R runQC.R cleanup.R
Date: 2012-12-21
Packaged: 2012-12-21 22:10:50 UTC; ddkang
Repository: CRAN
Date/Publication: 2012-12-22 07:39:42

● Data Source: CranContrib
● Cran Task View: MetaAnalysis
● 0 images, 8 functions, 0 datasets
● Reverse Depends: 0

MixRF : A Random-Forest-Based Approach for Imputing Clustered Incomplete Data

Package: MixRF
Title: A Random-Forest-Based Approach for Imputing Clustered Incomplete
Version: 1.0
Date: 2016-04-05
Author: Jiebiao Wang and Lin S. Chen
Maintainer: Jiebiao Wang <>
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
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

● Data Source: CranContrib
● 0 images, 4 functions, 0 datasets
● Reverse Depends: 0

MonoInc : Monotonic Increasing

Package: MonoInc
Type: Package
Title: Monotonic Increasing
Version: 1.1
Date: 2016-05-19
Author: Melyssa Minto, Michele Josey, and ClarLynda Williams-DeVane
Maintainer: Michele Josey <>
Description: Various imputation methods are utilized in this package, where one can flag and impute non-monotonic data that is outside of a prespecified range.
License: GPL-3
Encoding: UTF-8
Depends: compare, doParallel, foreach, iterators, parallel
Imports: sitar
NeedsCompilation: no
Packaged: 2016-05-20 16:57:57 UTC; michelejosey
Repository: CRAN
Date/Publication: 2016-05-20 22:36:53

● Data Source: CranContrib
5 images, 5 functions, 4 datasets
● Reverse Depends: 0