Showing 17 of total 17 results (show query)
easystats
datawizard:Easy Data Wrangling and Statistical Transformations
A lightweight package to assist in key steps involved in any data analysis workflow: (1) wrangling the raw data to get it in the needed form, (2) applying preprocessing steps and statistical transformations, and (3) compute statistical summaries of data properties and distributions. It is also the data wrangling backend for packages in 'easystats' ecosystem. References: Patil et al. (2022) <doi:10.21105/joss.04684>.
Maintained by Etienne Bacher. Last updated 3 days ago.
datadplyrhacktoberfestjanitormanipulationreshapetidyrwrangling
223 stars 14.77 score 436 scripts 120 dependents
alexanderrobitzsch
sirt:Supplementary Item Response Theory Models
Supplementary functions for item response models aiming to complement existing R packages. The functionality includes among others multidimensional compensatory and noncompensatory IRT models (Reckase, 2009, <doi:10.1007/978-0-387-89976-3>), MCMC for hierarchical IRT models and testlet models (Fox, 2010, <doi:10.1007/978-1-4419-0742-4>), NOHARM (McDonald, 1982, <doi:10.1177/014662168200600402>), Rasch copula model (Braeken, 2011, <doi:10.1007/s11336-010-9190-4>; Schroeders, Robitzsch & Schipolowski, 2014, <doi:10.1111/jedm.12054>), faceted and hierarchical rater models (DeCarlo, Kim & Johnson, 2011, <doi:10.1111/j.1745-3984.2011.00143.x>), ordinal IRT model (ISOP; Scheiblechner, 1995, <doi:10.1007/BF02301417>), DETECT statistic (Stout, Habing, Douglas & Kim, 1996, <doi:10.1177/014662169602000403>), local structural equation modeling (LSEM; Hildebrandt, Luedtke, Robitzsch, Sommer & Wilhelm, 2016, <doi:10.1080/00273171.2016.1142856>).
Maintained by Alexander Robitzsch. Last updated 3 months ago.
item-response-theoryopenblascpp
23 stars 10.01 score 280 scripts 22 dependents
ekstroem
MESS:Miscellaneous Esoteric Statistical Scripts
A mixed collection of useful and semi-useful diverse statistical functions, some of which may even be referenced in The R Primer book. See Ekstrøm, C. T. (2016). The R Primer. 2nd edition. Chapman & Hall.
Maintained by Claus Thorn Ekstrøm. Last updated 1 months ago.
biostatisticspower-analysisstatistical-analysisstatistical-methodsstatistical-modelsopenblascpp
4 stars 7.69 score 328 scripts 13 dependents
svkucheryavski
mdatools:Multivariate Data Analysis for Chemometrics
Projection based methods for preprocessing, exploring and analysis of multivariate data used in chemometrics. S. Kucheryavskiy (2020) <doi:10.1016/j.chemolab.2020.103937>.
Maintained by Sergey Kucheryavskiy. Last updated 8 months ago.
36 stars 7.41 score 220 scripts 1 dependents
jakobbossek
ecr:Evolutionary Computation in R
Framework for building evolutionary algorithms for both single- and multi-objective continuous or discrete optimization problems. A set of predefined evolutionary building blocks and operators is included. Moreover, the user can easily set up custom objective functions, operators, building blocks and representations sticking to few conventions. The package allows both a black-box approach for standard tasks (plug-and-play style) and a much more flexible white-box approach where the evolutionary cycle is written by hand.
Maintained by Jakob Bossek. Last updated 2 years ago.
combinatorial-optimizationevolutionary-algorithmevolutionary-algorithmsevolutionary-strategygenetic-algorithm-frameworkmetaheuristicsmulti-objective-optimizationoptimizationoptimization-frameworkcpp
43 stars 7.36 score 89 scripts 2 dependents
wilkelab
sicegar:Analysis of Single-Cell Viral Growth Curves
Aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal, and double sigmoidal curves on to time vs intensity data. Then all the fits are used to make decision on which model best describes the data. This method was first developed in the context of single-cell viral growth analysis (for details, see Caglar et al. (2018) <doi:10.7717/peerj.4251>), and the package name stands for "SIngle CEll Growth Analysis in R".
Maintained by Claus O. Wilke. Last updated 4 years ago.
9 stars 6.57 score 41 scripts
divdyn
divDyn:Diversity Dynamics using Fossil Sampling Data
Functions to describe sampling and diversity dynamics of fossil occurrence datasets (e.g. from the Paleobiology Database). The package includes methods to calculate range- and occurrence-based metrics of taxonomic richness, extinction and origination rates, along with traditional sampling measures. A powerful subsampling tool is also included that implements frequently used sampling standardization methods in a multiple bin-framework. The plotting of time series and the occurrence data can be simplified by the functions incorporated in the package, as well as other calculations, such as environmental affinities and extinction selectivity testing. Details can be found in: Kocsis, A.T.; Reddin, C.J.; Alroy, J. and Kiessling, W. (2019) <doi:10.1101/423780>.
Maintained by Adam T. Kocsis. Last updated 4 months ago.
diversityextinctionfossil-dataoccurrencesoriginationpaleobiologycpp
11 stars 6.48 score 137 scripts
babaknaimi
elsa:Entropy-Based Local Indicator of Spatial Association
A framework that provides the methods for quantifying entropy-based local indicator of spatial association (ELSA) that can be used for both continuous and categorical data. In addition, this package offers other methods to measure local indicators of spatial associations (LISA). Furthermore, global spatial structure can be measured using a variogram-like diagram, called entrogram. For more information, please check that paper: Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019) <doi:10.1016/j.spasta.2018.10.001>.
Maintained by Babak Naimi. Last updated 1 years ago.
14 stars 5.23 score 24 scripts
azure
AzureVision:Interface to Azure Computer Vision Services
An interface to 'Azure Computer Vision' <https://docs.microsoft.com/azure/cognitive-services/Computer-vision/Home> and 'Azure Custom Vision' <https://docs.microsoft.com/azure/cognitive-services/custom-vision-service/home>, building on the low-level functionality provided by the 'AzureCognitive' package. These services allow users to leverage the cloud to carry out visual recognition tasks using advanced image processing models, without needing powerful hardware of their own. Part of the 'AzureR' family of packages.
Maintained by Hong Ooi. Last updated 4 years ago.
azure-cognitive-servicesazure-sdk-rcomputer-visioncustom-vision
5 stars 5.00 score 8 scripts
maxwestphal
cases:Stratified Evaluation of Subgroup Classification Accuracy
Enables simultaneous statistical inference for the accuracy of multiple classifiers in multiple subgroups (strata). For instance, allows to perform multiple comparisons in diagnostic accuracy studies with co-primary endpoints sensitivity and specificity (Westphal M, Zapf A. Statistical inference for diagnostic test accuracy studies with multiple comparisons. Statistical Methods in Medical Research. 2024;0(0). <doi:10.1177/09622802241236933>).
Maintained by Max Westphal. Last updated 3 months ago.
1 stars 4.59 score 13 scripts
dominiquemaucieri
quadcleanR:Cleanup and Visualization of Quadrat Data
A tool that can be customized to aid in the clean up of ecological data collected using quadrats and can crop quadrats to ensure comparability between quadrats collected under different methodologies.
Maintained by Dominique Maucieri. Last updated 2 years ago.
4.45 score 14 scripts
a2-ai
scicalc:Scientific Calculations for Quantitative Clinical Pharmacology and Pharmacometrics Analysis
Utility functions helpful for reproducible scientific calculations.
Maintained by Matthew Smith. Last updated 2 months ago.
1 stars 4.04 score 4 scripts
alexchristensen
latentFactoR:Data Simulation Based on Latent Factors
Generates data based on latent factor models. Data can be continuous, polytomous, dichotomous, or mixed. Skews, cross-loadings, wording effects, population errors, and local dependencies can be added. All parameters can be manipulated. Data categorization is based on Garrido, Abad, and Ponsoda (2011) <doi:10.1177/0013164410389489>.
Maintained by Alexander Christensen. Last updated 8 months ago.
3 stars 3.88 score 2 scripts
insileco
inSilecoMisc:inSileco Miscellaneous Functions
A set of miscellaneous R functions written by our inSileco group.
Maintained by Kevin Cazelles. Last updated 3 years ago.
4 stars 3.30 score 4 scripts
eisuke-inoue
nricens:NRI for Risk Prediction Models with Time to Event and Binary Response Data
Calculating the net reclassification improvement (NRI) for risk prediction models with time to event and binary data.
Maintained by Eisuke Inoue. Last updated 7 years ago.
2.85 score 70 scripts
