Showing 8 of total 8 results (show query)
data-cleaning
validate:Data Validation Infrastructure
Declare data validation rules and data quality indicators; confront data with them and analyze or visualize the results. The package supports rules that are per-field, in-record, cross-record or cross-dataset. Rules can be automatically analyzed for rule type and connectivity. Supports checks implied by an SDMX DSD file as well. See also Van der Loo and De Jonge (2018) <doi:10.1002/9781118897126>, Chapter 6 and the JSS paper (2021) <doi:10.18637/jss.v097.i10>.
Maintained by Mark van der Loo. Last updated 28 days ago.
419 stars 12.39 score 448 scripts 8 dependents
laresbernardo
lares:Analytics & Machine Learning Sidekick
Auxiliary package for better/faster analytics, visualization, data mining, and machine learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling, Marketing Mix Modeling (Robyn), Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust results, without the need of repetitive coding or advanced R programming skills.
Maintained by Bernardo Lares. Last updated 1 months ago.
analyticsapiautomationautomldata-sciencedescriptive-statisticsh2omachine-learningmarketingmmmpredictive-modelingpuzzlerlanguagerobynvisualization
233 stars 9.92 score 185 scripts 1 dependents
jpgattuso
seacarb:Seawater Carbonate Chemistry
Calculates parameters of the seawater carbonate system and assists the design of ocean acidification perturbation experiments.
Maintained by Jean-Pierre Gattuso. Last updated 1 years ago.
8 stars 8.19 score 350 scripts 5 dependents
r-quantities
errors:Uncertainty Propagation for R Vectors
Support for measurement errors in R vectors, matrices and arrays: automatic uncertainty propagation and reporting. Documentation about 'errors' is provided in the paper by Ucar, Pebesma & Azcorra (2018, <doi:10.32614/RJ-2018-075>), included in this package as a vignette; see 'citation("errors")' for details.
Maintained by Iñaki Ucar. Last updated 2 months ago.
49 stars 8.18 score 86 scripts 4 dependents
bioc
QTLExperiment:S4 classes for QTL summary statistics and metadata
QLTExperiment defines an S4 class for storing and manipulating summary statistics from QTL mapping experiments in one or more states. It is based on the 'SummarizedExperiment' class and contains functions for creating, merging, and subsetting objects. 'QTLExperiment' also stores experiment metadata and has checks in place to ensure that transformations apply correctly.
Maintained by Amelia Dunstone. Last updated 13 days ago.
functionalgenomicsdataimportdatarepresentationinfrastructuresequencingsnpsoftware
2 stars 5.32 score 14 scripts 1 dependentsbioc
rsbml:R support for SBML, using libsbml
Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models.
Maintained by Michael Lawrence. Last updated 1 months ago.
graphandnetworkpathwaysnetworklibsbmlcpp
4.71 score 19 scripts 1 dependents
bergsmat
nonmemica:Create and Evaluate NONMEM Models in a Project Context
Systematically creates and modifies NONMEM(R) control streams. Harvests NONMEM output, builds run logs, creates derivative data, generates diagnostics. NONMEM (ICON Development Solutions <https://www.iconplc.com/>) is software for nonlinear mixed effects modeling. See 'package?nonmemica'.
Maintained by Tim Bergsma. Last updated 3 months ago.
4 stars 4.58 score 45 scripts
