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momix
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public |
No Summary
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2023-06-16 |
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r-ictest
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public |
For different linear dimension reduction methods like principal components analysis (PCA), independent components analysis (ICA) and supervised linear dimension reduction tests and estimates for the number of interesting components (ICs) are provided.
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2023-06-16 |
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r-tsbss
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public |
Different estimates are provided to solve the blind source separation problem for multivariate time series with stochastic volatility (Matilainen, Nordhausen and Oja (2015) <doi:10.1016/j.spl.2015.04.033>; Matilainen, Miettinen, Nordhausen, Oja and Taskinen (2017) <doi:10.17713/ajs.v46i3-4.671>) and supervised dimension reduction problem for multivariate time series (Matilainen, Croux, Nordhausen and Oja (2017) <doi:10.1016/j.ecosta.2017.04.002>). Different functions based on AMUSE and SOBI are also provided for estimating the dimension of the white noise subspace.
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2023-06-16 |
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r-tensorbss
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public |
Contains several utility functions for manipulating tensor-valued data (centering, multiplication from a single mode etc.) and the implementations of the following blind source separation methods for tensor-valued data: 'tPCA', 'tFOBI', 'tJADE', k-tJADE', 'tgFOBI', 'tgJADE', 'tSOBI', 'tNSS.SD', 'tNSS.JD', 'tNSS.TD.JD' and 'tPP'.
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2023-06-16 |
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r-mofa
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public |
No Summary
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2023-06-16 |
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r-spatiotemporal
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public |
Utilities that estimate, predict and cross-validate the spatio-temporal model developed for MESA Air.
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2023-06-16 |
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r-intnmf
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public |
Carries out integrative clustering analysis using multiple types of genomic dataset using integrative Non-negative Matrix factorization.
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2023-06-16 |
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r-rgcca
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public |
Multiblock data analysis concerns the analysis of several sets of variables (blocks) observed on the same group of individuals. The main aims of the RGCCA package are: (i) to study the relationships between blocks and (ii) to identify subsets of variables of each block which are active in their relationships with the other blocks.
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2023-06-16 |
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scikit-fusion
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public |
No Summary
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2023-06-16 |
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r-intersim
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public |
Generates three inter-related genomic datasets : methylation, gene expression and protein expression.
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2023-06-16 |
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r-multiway
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public |
Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
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2023-06-16 |
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r-cmls
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public |
Solves multivariate least squares (MLS) problems subject to constraints on the coefficients, e.g., non-negativity, orthogonality, equality, inequality, monotonicity, unimodality, smoothness, etc. Includes flexible functions for solving MLS problems subject to user-specified equality and/or inequality constraints, as well as a wrapper function that implements 24 common constraint options. Also does k-fold or generalized cross-validation to tune constraint options for MLS problems. See ten Berge (1993, ISBN:9789066950832) for an overview of MLS problems, and see Goldfarb and Idnani (1983) <doi:10.1007/BF02591962> for a discussion of the underlying quadratic programming algorithm.
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2023-06-16 |
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funcsfa
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public |
No Summary
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2023-06-16 |
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r-jive
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public |
Performs the JIVE decomposition on a list of data sets when the data share a dimension, returning low-rank matrices that capture the joint and individual structure of the data [O'Connell, MJ and Lock, EF (2016) <doi:10.1093/bioinformatics/btw324>]. It provides two methods of rank selection when the rank is unknown, a permutation test and a BIC selection algorithm. Also included in the package are three plotting functions for visualizing the variance attributed to each data source: a bar plot that shows the percentages of the variability attributable to joint and individual structure, a heatmap that shows the structure of the variability, and principal component plots.
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2023-06-16 |
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r-msfa
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public |
Fits the Multi-Study Factor Analysis model via the ECM algorithm and via a Bayesian approach.
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2023-06-16 |
