R-universe search: exports:scaling

Showing 9 of total 9 results (show query)

alexkz

kernlab:Kernel-Based Machine Learning Lab

Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.

Maintained by Alexandros Karatzoglou. Last updated 8 months ago.

openblas cpp

21 stars 12.26 score 7.8k scripts 487 dependents

idem-lab

conmat:Builds Contact Matrices using GAMs and Population Data

Builds contact matrices using GAMs and population data. This package incorporates data that is copyright Commonwealth of Australia (Australian Electoral Commission and Australian Bureau of Statistics) 2020.

Maintained by Nicholas Tierney. Last updated 20 days ago.

contact-matrices infectious-diseases population-data public-health

19 stars 7.21 score 47 scripts

tkcaccia

KODAMA:Knowledge Discovery by Accuracy Maximization

An unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. Based on Cacciatore S, Tenori L, Luchinat C, Bennett PR, MacIntyre DA. (2017) Bioinformatics <doi:10.1093/bioinformatics/btw705> and Cacciatore S, Luchinat C, Tenori L. (2014) Proc Natl Acad Sci USA <doi:10.1073/pnas.1220873111>.

Maintained by Stefano Cacciatore. Last updated 14 days ago.

openblas cpp

1 stars 7.00 score 63 scripts 1 dependents

amsantac

TOC:Total Operating Characteristic Curve and ROC Curve

Construction of the Total Operating Characteristic (TOC) Curve and the Receiver (aka Relative) Operating Characteristic (ROC) Curve for spatial and non-spatial data. The TOC method is a modification of the ROC method which measures the ability of an index variable to diagnose either presence or absence of a characteristic. The diagnosis depends on whether the value of an index variable is above a threshold. Each threshold generates a two-by-two contingency table, which contains four entries: hits (H), misses (M), false alarms (FA), and correct rejections (CR). While ROC shows for each threshold only two ratios, H/(H + M) and FA/(FA + CR), TOC reveals the size of every entry in the contingency table for each threshold (Pontius Jr., R.G., Si, K. 2014. <doi:10.1080/13658816.2013.862623>).

Maintained by Ali Santacruz. Last updated 1 years ago.

4 stars 4.48 score 15 scripts

emanuelhuber

RConics:Computations on Conics

Solve some conic related problems (intersection of conics with lines and conics, arc length of an ellipse, polar lines, etc.).

Maintained by Emanuel Huber. Last updated 1 months ago.

conics ellipse elliptic geometry intersection

4.20 score 53 scripts 2 dependents

ilaga

networkscaleup:Network Scale-Up Models for Aggregated Relational Data

Provides a variety of Network Scale-up Models for researchers to analyze Aggregated Relational Data, mostly through the use of Stan. In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2021) <arXiv:2109.10204>, Zheng, T., Salganik, M. J., and Gelman, A. (2006) <doi:10.1198/016214505000001168>, Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., and Bernard, H. R. (1998) <doi:10.1016/S0378-8733(96)00305-X>, and Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and Johnsen, E. C. (1998) <doi:10.1177/0193841X9802200205>.

Maintained by Ian Laga. Last updated 1 years ago.

cpp

4 stars 3.60 score 4 scripts

liuy12

SCdeconR:Deconvolution of Bulk RNA-Seq Data using Single-Cell RNA-Seq Data as Reference

Streamlined workflow from deconvolution of bulk RNA-seq data to downstream differential expression and gene-set enrichment analysis. Provide various visualization functions.

Maintained by Yuanhang Liu. Last updated 10 months ago.

bulk-rna-seq-deconvolution deconvolution differential-expression ffpe geneset-enrichment-analysis scdeconr single-cell

4 stars 3.60 score 4 scripts

olgnaydn

compindexR:Calculates Composite Index

It uses the first-order sensitivity index to measure whether the weights assigned by the creator of the composite indicator match the actual importance of the variables. Moreover, the variance inflation factor is used to reduce the set of correlated variables. In the case of a discrepancy between the importance and the assigned weight, the script determines weights that allow adjustment of the weights to the intended impact of variables. If the optimised weights are unable to reflect the desired importance, the highly correlated variables are reduced, taking into account variance inflation factor. The final outcome of the script is the calculated value of the composite indicator based on optimal weights and a reduced set of variables, and the linear ordering of the analysed objects.

Maintained by Olgun Aydin. Last updated 2 years ago.

2 stars 3.00 score 1 scripts

gormleyi

MetabolAnalyze:Probabilistic Latent Variable Models for Metabolomic Data

Fits probabilistic principal components analysis, probabilistic principal components and covariates analysis and mixtures of probabilistic principal components models to metabolomic spectral data.

Maintained by Claire Gormley. Last updated 6 years ago.

1 stars 2.69 score 18 scripts 1 dependents