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pascoalf

ulrb:Unsupervised Learning Based Definition of Microbial Rare Biosphere

A tool to define rare biosphere. 'ulrb' solves the problem of the definition of rarity by replacing arbitrary thresholds with an unsupervised machine learning algorithm (partitioning around medoids, or k-medoids). This algorithm works for any type of microbiome data, provided there is a species abundance table. For validation of this method to different species abundance tables see Pascoal et al, 2024 (in peer-review). This method also works for non-microbiome data.

Maintained by Francisco Pascoal. Last updated 8 days ago.

3 stars 5.78 score 9 scripts

rikenbit

WormTensor:A Clustering Method for Time-Series Whole-Brain Activity Data of 'C. elegans'

A toolkit to detect clusters from distance matrices. The distance matrices are assumed to be calculated between the cells of multiple animals ('Caenorhabditis elegans') from input time-series matrices. Some functions for generating distance matrices, performing clustering, evaluating the clustering, and visualizing the results of clustering and evaluation are available. We're also providing the download function to retrieve the calculated distance matrices from 'figshare' <https://figshare.com>.

Maintained by Kentaro Yamamoto. Last updated 8 months ago.

1 stars 4.18 score 3 scripts

tsukubai

hclusteasy:Determining Hierarchical Clustering Easily

Facilitates hierarchical clustering analysis with functions to read data in 'txt', 'xlsx', and 'xls' formats, apply normalization techniques to the dataset, perform hierarchical clustering and construct scatter plot from principal component analysis to evaluate the groups obtained.

Maintained by Henrique Andrade. Last updated 10 months ago.

3.00 score 1 scripts

vmoprojs

clusEvol:A Procedure for Cluster Evolution Analytics

Cluster Evolution Analytics allows us to use exploratory what if questions in the sense that the present information of an object is plugged-in a dataset in a previous time frame so that we can explore its evolution (and of its neighbors) to the present. See the URL for the papers associated with this package, as for instance, Morales-Oñate and Morales-Oñate (2024) <https://mpra.ub.uni-muenchen.de/120220>.

Maintained by Víctor Morales-Oñate. Last updated 8 months ago.

2.70 score 1 scripts

a-dudek-ue

mdsOpt:Searching for Optimal MDS Procedure for Metric and Interval-Valued Data

Selecting the optimal multidimensional scaling (MDS) procedure for metric data via metric MDS (ratio, interval, mspline) and nonmetric MDS (ordinal). Selecting the optimal multidimensional scaling (MDS) procedure for interval-valued data via metric MDS (ratio, interval, mspline).Selecting the optimal multidimensional scaling procedure for interval-valued data by varying all combinations of normalization and optimization methods.Selecting the optimal MDS procedure for statistical data referring to the evaluation of tourist attractiveness of Lower Silesian counties. (Borg, I., Groenen, P.J.F., Mair, P. (2013) <doi:10.1007/978-3-642-31848-1>, Walesiak, M. (2016) <doi:10.15611/ekt.2016.2.01>, Walesiak, M. (2017) <doi:10.15611/ekt.2017.3.01>).

Maintained by Andrzej Dudek. Last updated 1 years ago.

2.28 score 19 scripts

a-dudek-ue

symbolicDA:Analysis of Symbolic Data

Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation. (Milligan, G.W., Cooper, M.C. (1985) <doi:10.1007/BF02294245>, Breiman, L. (1996), <doi:10.1007/BF00058655>, Hubert, L., Arabie, P. (1985), <doi:10.1007%2FBF01908075>, Ichino, M., & Yaguchi, H. (1994), <doi:10.1109/21.286391>, Rand, W.M. (1971) <doi:10.1080/01621459.1971.10482356>, Calinski, T., Harabasz, J. (1974) <doi:10.1080/03610927408827101>, Breckenridge, J.N. (2000) <doi:10.1207/S15327906MBR3502_5>, Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) <doi:10.1016/j.csda.2006.04.003>, Walesiak, M., Dudek, A. (2008) <doi:10.1007/978-3-540-78246-9_11>, Dudek, A. (2007), <doi:10.1007/978-3-540-70981-7_4>).

Maintained by Andrzej Dudek. Last updated 2 years ago.

2.08 score 40 scripts 1 dependents

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immunaut:Machine Learning Immunogenicity and Vaccine Response Analysis

Used for analyzing immune responses and predicting vaccine efficacy using machine learning and advanced data processing techniques. 'Immunaut' integrates both unsupervised and supervised learning methods, managing outliers and capturing immune response variability. It performs multiple rounds of predictive model testing to identify robust immunogenicity signatures that can predict vaccine responsiveness. The platform is designed to handle high-dimensional immune data, enabling researchers to uncover immune predictors and refine personalized vaccination strategies across diverse populations.

Maintained by Ivan Tomic. Last updated 5 months ago.

1.70 score

a-dudek-ue

warehouseTools:Heuristics for Solving the Traveling Salesman Problem in Warehouse Layouts

Heuristic methods to solve the routing problems in a warehouse management. Package includes several heuristics such as the Midpoint, Return, S-Shape and Semi-Optimal Heuristics for designation of the picker’s route in order picking. The heuristics aim to provide the acceptable travel distances while considering warehouse layout constraints such as aisles and shelves. It also includes implementation of the COPRAS (COmplex PRoportional ASsessment) method for supporting selection of locations to be visited by the picker in shared storage systems. The package is designed to facilitate more efficient warehouse routing and logistics operations. see: Bartholdi, J. J., Hackman, S. T. (2019). "WAREHOUSE & DISTRIBUTION SCIENCE. Release 0.98.1." The Supply Chain & Logistics Institute. H. Milton Stewart School of Industrial and Systems Engineering. Georgia Institute of Technology. <https://www.warehouse-science.com/book/editions/wh-sci-0.98.1.pdf>.

Maintained by Andrzej Dudek. Last updated 4 months ago.

1.00 score

cran

comato:Analysis of Concept Maps and Concept Landscapes

Provides methods for the import/export and automated analysis of concept maps and concept landscapes (sets of concept maps).

Maintained by Andreas Muehling. Last updated 7 years ago.

1.00 score

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