Showing 5 of total 5 results (show query)
audreyqyfu
MRPC:PC Algorithm with the Principle of Mendelian Randomization
A PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also contains functions to simulate data under a certain topology, to visualize a graph in different ways, and to compare graphs and quantify the differences. See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>,Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>.
Maintained by Audrey Fu. Last updated 3 years ago.
8 stars 4.68 score 20 scripts
ptarroso
phylin:Spatial Interpolation of Genetic Data
The spatial interpolation of genetic distances between samples is based on a modified kriging method that accepts a genetic distance matrix and generates a map of probability of lineage presence. This package also offers tools to generate a map of potential contact zones between groups with user-defined thresholds in the tree to account for old and recent divergence. Additionally, it has functions for IDW interpolation using genetic data and midpoints.
Maintained by Pedro Tarroso. Last updated 5 years ago.
2.99 score 49 scripts
cran
multiway:Component Models for Multi-Way Data
Fits multi-way component models via alternating least squares algorithms with optional constraints. Fit models include N-way Canonical Polyadic Decomposition, Individual Differences Scaling, Multiway Covariates Regression, Parallel Factor Analysis (1 and 2), Simultaneous Component Analysis, and Tucker Factor Analysis.
Maintained by Nathaniel E. Helwig. Last updated 6 years ago.
3 stars 2.73 score 6 dependents
timothyliu-datascience
NMFN:Non-Negative Matrix Factorization
Non-negative Matrix Factorization.
Maintained by Suhai (Timothy) Liu. Last updated 3 years ago.
1 stars 1.59 score 13 scripts 1 dependentscran
cml:Conditional Manifold Learning
Finds a low-dimensional embedding of high-dimensional data, conditioning on available manifold information. The current version supports conditional MDS (based on either conditional SMACOF in Bui (2021) <arXiv:2111.13646> or closed-form solution in Bui (2022) <doi:10.1016/j.patrec.2022.11.007>) and conditional ISOMAP in Bui (2021) <arXiv:2111.13646>.
Maintained by Anh Tuan Bui. Last updated 2 years ago.
1.30 score