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ropensci

taxize:Taxonomic Information from Around the Web

Interacts with a suite of web application programming interfaces (API) for taxonomic tasks, such as getting database specific taxonomic identifiers, verifying species names, getting taxonomic hierarchies, fetching downstream and upstream taxonomic names, getting taxonomic synonyms, converting scientific to common names and vice versa, and more. Some of the services supported include 'NCBI E-utilities' (<https://www.ncbi.nlm.nih.gov/books/NBK25501/>), 'Encyclopedia of Life' (<https://eol.org/docs/what-is-eol/data-services>), 'Global Biodiversity Information Facility' (<https://techdocs.gbif.org/en/openapi/>), and many more. Links to the API documentation for other supported services are available in the documentation for their respective functions in this package.

Maintained by Zachary Foster. Last updated 25 days ago.

taxonomybiologynomenclaturejsonapiwebapi-clientidentifiersspeciesnamesapi-wrapperbiodiversitydarwincoredatataxize

274 stars 13.63 score 1.6k scripts 23 dependents

muvisu

biplotEZ:EZ-to-Use Biplots

Provides users with an EZ-to-use platform for representing data with biplots. Currently principal component analysis (PCA), canonical variate analysis (CVA) and simple correspondence analysis (CA) biplots are included. This is accompanied by various formatting options for the samples and axes. Alpha-bags and concentration ellipses are included for visual enhancements and interpretation. For an extensive discussion on the topic, see Gower, J.C., Lubbe, S. and le Roux, N.J. (2011, ISBN: 978-0-470-01255-0) Understanding Biplots. Wiley: Chichester.

Maintained by Sugnet Lubbe. Last updated 20 days ago.

fortran

7 stars 8.39 score 30 scripts 1 dependents

ropensci

taxa:Classes for Storing and Manipulating Taxonomic Data

Provides classes for storing and manipulating taxonomic data. Most of the classes can be treated like base R vectors (e.g. can be used in tables as columns and can be named). Vectorized classes can store taxon names and authorities, taxon IDs from databases, taxon ranks, and other types of information. More complex classes are provided to store taxonomic trees and user-defined data associated with them.

Maintained by Zachary Foster. Last updated 1 years ago.

taxonomybiologyhierarchydata-cleaningtaxon

47 stars 6.79 score 217 scripts

cefet-rj-dal

daltoolbox:Leveraging Experiment Lines to Data Analytics

The natural increase in the complexity of current research experiments and data demands better tools to enhance productivity in Data Analytics. The package is a framework designed to address the modern challenges in data analytics workflows. The package is inspired by Experiment Line concepts. It aims to provide seamless support for users in developing their data mining workflows by offering a uniform data model and method API. It enables the integration of various data mining activities, including data preprocessing, classification, regression, clustering, and time series prediction. It also offers options for hyper-parameter tuning and supports integration with existing libraries and languages. Overall, the package provides researchers with a comprehensive set of functionalities for data science, promoting ease of use, extensibility, and integration with various tools and libraries. Information on Experiment Line is based on Ogasawara et al. (2009) <doi:10.1007/978-3-642-02279-1_20>.

Maintained by Eduardo Ogasawara. Last updated 2 months ago.

1 stars 6.65 score 536 scripts 4 dependents

ropensci

taxizedb:Tools for Working with 'Taxonomic' Databases

Tools for working with 'taxonomic' databases, including utilities for downloading databases, loading them into various 'SQL' databases, cleaning up files, and providing a 'SQL' connection that can be used to do 'SQL' queries directly or used in 'dplyr'.

Maintained by Tamás Stirling. Last updated 2 months ago.

itistaxizetaxonomic-databasestaxonomy

31 stars 5.86 score 86 scripts 1 dependents

bioc

rexposome:Exposome exploration and outcome data analysis

Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes.

Maintained by Xavier Escribà Montagut. Last updated 5 months ago.

softwarebiologicalquestioninfrastructuredataimportdatarepresentationbiomedicalinformaticsexperimentaldesignmultiplecomparisonclassificationclustering

5.70 score 28 scripts 1 dependents

loelschlaeger

RprobitB:Bayesian Probit Choice Modeling

Bayes estimation of probit choice models, both in the cross-sectional and panel setting. The package can analyze binary, multivariate, ordered, and ranked choices, as well as heterogeneity of choice behavior among deciders. The main functionality includes model fitting via Markov chain Monte Carlo m ethods, tools for convergence diagnostic, choice data simulation, in-sample and out-of-sample choice prediction, and model selection using information criteria and Bayes factors. The latent class model extension facilitates preference-based decider classification, where the number of latent classes can be inferred via the Dirichlet process or a weight-based updating heuristic. This allows for flexible modeling of choice behavior without the need to impose structural constraints. For a reference on the method see Oelschlaeger and Bauer (2021) <https://trid.trb.org/view/1759753>.

Maintained by Lennart Oelschläger. Last updated 6 months ago.

bayesdiscrete-choiceprobitopenblascppopenmp

4 stars 5.45 score 1 scripts

inbo

effectclass:Classification and Visualisation of Effects

Classify effects by comparing the confidence intervals with thresholds.

Maintained by Thierry Onkelinx. Last updated 10 months ago.

effect-sizefan-chart

6 stars 5.30 score 37 scripts 1 dependents

bioc

CMA:Synthesis of microarray-based classification

This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment.

Maintained by Roman Hornung. Last updated 5 months ago.

classificationdecisiontree

5.09 score 61 scripts

bioc

scoreInvHap:Get inversion status in predefined regions

scoreInvHap can get the samples' inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for 21 inversions.

Maintained by Dolors Pelegri-Siso. Last updated 5 months ago.

snpgeneticsgenomicvariation

4.34 score 11 scripts

jaroslav-kuchar

rCBA:CBA Classifier for R

Provides implementations of a classifier based on the "Classification Based on Associations" (CBA). It can be used for building classification models from association rules. Rules are pruned in the order of precedence given by the sort criteria and a default rule is added. The final classifier labels provided instances. CBA was originally proposed by Liu, B. Hsu, W. and Ma, Y. Integrating Classification and Association Rule Mining. Proceedings KDD-98, New York, 27-31 August. AAAI. pp80-86 (1998, ISBN:1-57735-070-7).

Maintained by Jaroslav Kuchar. Last updated 6 years ago.

openjdk

7 stars 4.14 score 39 scripts

hoksanyip

SVMMaj:Implementation of the SVM-Maj Algorithm

Implements the SVM-Maj algorithm to train data with support vector machine <doi:10.1007/s11634-008-0020-9>. This algorithm uses two efficient updates, one for linear kernel and one for the nonlinear kernel.

Maintained by Hoksan Yip. Last updated 4 months ago.

1 stars 3.36 score 23 scripts

bioc

epigenomix:Epigenetic and gene transcription data normalization and integration with mixture models

A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types.

Maintained by Hans-Ulrich Klein. Last updated 5 months ago.

chipseqgeneexpressiondifferentialexpressionclassification

3.30 score 1 scripts

fabriciomlopes

BASiNET:Classification of RNA Sequences using Complex Network Theory

It makes the creation of networks from sequences of RNA, with this is done the abstraction of characteristics of these networks with a methodology of threshold for the purpose of making a classification between the classes of the sequences. There are four data present in the 'BASiNET' package, "sequences", "sequences2", "sequences-predict" and "sequences2-predict" with 11, 10, 11 and 11 sequences respectively. These sequences were taken from the data set used in the article (LI, Aimin; ZHANG, Junying; ZHOU, Zhongyin, 2014) <doi:10.1186/1471-2105-15-311>, these sequences are used to run examples. The BASiNET was published on Nucleic Acids Research, (ITO, Eric; KATAHIRA, Isaque; VICENTE, Fábio; PEREIRA, Felipe; LOPES, Fabrício, 2018) <doi:10.1093/nar/gky462>.

Maintained by Fabricio Martins Lopes. Last updated 3 years ago.

softwarebiologicalquestiongenepredictionopenjdk

2.48 score 7 scripts

mpbohorquezc

SpatFD:Functional Geostatistics: Univariate and Multivariate Functional Spatial Prediction

Performance of functional kriging, cokriging, optimal sampling and simulation for spatial prediction of functional data. The framework of spatial prediction, optimal sampling and simulation are extended from scalar to functional data. 'SpatFD' is based on the Karhunen-Loève expansion that allows to represent the observed functions in terms of its empirical functional principal components. Based on this approach, the functional auto-covariances and cross-covariances required for spatial functional predictions and optimal sampling, are completely determined by the sum of the spatial auto-covariances and cross-covariances of the respective score components. The package provides new classes of data and functions for modeling spatial dependence structure among curves. The spatial prediction of curves at unsampled locations can be carried out using two types of predictors, and both of them report, the respective variances of the prediction error. In addition, there is a function for the determination of spatial locations sampling configuration that ensures minimum variance of spatial functional prediction. There are also two functions for plotting predicted curves at each location and mapping the surface at each time point, respectively. References Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s10260-015-0340-9>, Bohorquez, M., Giraldo, R., and Mateu, J. (2016) <doi:10.1007/s00477-016-1266-y>, Bohorquez M., Giraldo R. and Mateu J. (2021) <doi:10.1002/9781119387916>.

Maintained by Martha Patricia Bohorquez Castañeda. Last updated 9 months ago.

1.70 score 7 scripts

apedrods

MAINT.Data:Model and Analyse Interval Data

Implements methodologies for modelling interval data by Normal and Skew-Normal distributions, considering appropriate parameterizations of the variance-covariance matrix that takes into account the intrinsic nature of interval data, and lead to four different possible configuration structures. The Skew-Normal parameters can be estimated by maximum likelihood, while Normal parameters may be estimated by maximum likelihood or robust trimmed maximum likelihood methods.

Maintained by Pedro Duarte Silva. Last updated 2 years ago.

openblascpp

1.15 score 14 scripts

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