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data.tree:General Purpose Hierarchical Data Structure

Create tree structures from hierarchical data, and traverse the tree in various orders. Aggregate, cumulate, print, plot, convert to and from data.frame and more. Useful for decision trees, machine learning, finance, conversion from and to JSON, and many other applications.

Maintained by Christoph Glur. Last updated 6 months ago.

209 stars 12.84 score 1.1k scripts 88 dependents

hasinurkhan

DNAseqtest:Generating and Testing DNA Sequences

Generates DNA sequences based on Markov model techniques for matched sequences. This can be generalized to several sequences. The sequences (taxa) are then arranged in an evolutionary tree (phylogenetic tree) depicting how taxa diverge from their common ancestors. This gives the tests and estimation methods for the parameters of different models. Standard phylogenetic methods assume stationarity, homogeneity and reversibility for the Markov processes, and often impose further restrictions on the parameters.

Maintained by Hasinur Rahaman Khan. Last updated 9 years ago.

1.15 score 14 scripts

behnam-yousefi

mnda:Multiplex Network Differential Analysis (MNDA)

Interactions between different biological entities are crucial for the function of biological systems. In such networks, nodes represent biological elements, such as genes, proteins and microbes, and their interactions can be defined by edges, which can be either binary or weighted. The dysregulation of these networks can be associated with different clinical conditions such as diseases and response to treatments. However, such variations often occur locally and do not concern the whole network. To capture local variations of such networks, we propose multiplex network differential analysis (MNDA). MNDA allows to quantify the variations in the local neighborhood of each node (e.g. gene) between the two given clinical states, and to test for statistical significance of such variation. Yousefi et al. (2023) <doi:10.1101/2023.01.22.525058>.

Maintained by Behnam Yousefi. Last updated 2 years ago.

1.04 score 11 scripts

cran

IALS:Iterative Alternating Least Square Estimation for Large-Dimensional Matrix Factor Model

The matrix factor model has drawn growing attention for its advantage in achieving two-directional dimension reduction simultaneously for matrix-structured observations. In contrast to the Principal Component Analysis (PCA)-based methods, we propose a simple Iterative Alternating Least Squares (IALS) algorithm for matrix factor model, see the details in He et al. (2023) <arXiv:2301.00360>.

Maintained by Ran Zhao. Last updated 1 years ago.

1.00 score

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