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fwild

lsa:Latent Semantic Analysis

The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome.

Maintained by Fridolin Wild. Last updated 3 years ago.

6.25 score 1.2k scripts 23 dependents

stephenturner

Tmisc:Turner Miscellaneous

Miscellaneous utility functions for data manipulation, data tidying, and working with gene expression data and biological sequence data.

Maintained by Stephen Turner. Last updated 11 months ago.

2 stars 5.44 score 174 scripts 1 dependents

kylebaron

mrgsim.sa:Sensitivity Analysis with 'mrgsolve'

Perform sensitivity analysis on ordinary differential equation based models, including ad-hoc graphical analyses based on structured sequences of parameters as well as local sensitivity analysis. Functions are provided for creating inputs, simulating scenarios and plotting outputs.

Maintained by Kyle Baron. Last updated 1 years ago.

6 stars 5.05 score 37 scripts

changwn

RobMixReg:Robust Mixture Regression

Finite mixture models are a popular technique for modelling unobserved heterogeneity or to approximate general distribution functions in a semi-parametric way. They are used in a lot of different areas such as astronomy, biology, economics, marketing or medicine. This package is the implementation of popular robust mixture regression methods based on different algorithms including: fleximix, finite mixture models and latent class regression; CTLERob, component-wise adaptive trimming likelihood estimation; mixbi, bi-square estimation; mixL, Laplacian distribution; mixt, t-distribution; TLE, trimmed likelihood estimation. The implemented algorithms includes: CTLERob stands for Component-wise adaptive Trimming Likelihood Estimation based mixture regression; mixbi stands for mixture regression based on bi-square estimation; mixLstands for mixture regression based on Laplacian distribution; TLE stands for Trimmed Likelihood Estimation based mixture regression. For more detail of the algorithms, please refer to below references. Reference: Chun Yu, Weixin Yao, Kun Chen (2017) <doi:10.1002/cjs.11310>. NeyKov N, Filzmoser P, Dimova R et al. (2007) <doi:10.1016/j.csda.2006.12.024>. Bai X, Yao W. Boyer JE (2012) <doi:10.1016/j.csda.2012.01.016>. Wennan Chang, Xinyu Zhou, Yong Zang, Chi Zhang, Sha Cao (2020) <arXiv:2005.11599>.

Maintained by Wennan Chang. Last updated 20 days ago.

2.70 score 7 scripts

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