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apache

arrow:Integration to 'Apache' 'Arrow'

'Apache' 'Arrow' <https://arrow.apache.org/> is a cross-language development platform for in-memory data. It specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware. This package provides an interface to the 'Arrow C++' library.

Maintained by Jonathan Keane. Last updated 2 months ago.

arrowcurlopensslcpp

15k stars 19.25 score 10k scripts 82 dependents

bioc

GEOquery:Get data from NCBI Gene Expression Omnibus (GEO)

The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor.

Maintained by Sean Davis. Last updated 5 months ago.

microarraydataimportonechanneltwochannelsagebioconductorbioinformaticsdata-sciencegenomicsncbi-geo

93 stars 13.48 score 4.1k scripts 45 dependents

rfastofficial

Rfast:A Collection of Efficient and Extremely Fast R Functions

A collection of fast (utility) functions for data analysis. Column and row wise means, medians, variances, minimums, maximums, many t, F and G-square tests, many regressions (normal, logistic, Poisson), are some of the many fast functions. References: a) Tsagris M., Papadakis M. (2018). Taking R to its limits: 70+ tips. PeerJ Preprints 6:e26605v1 <doi:10.7287/peerj.preprints.26605v1>. b) Tsagris M. and Papadakis M. (2018). Forward regression in R: from the extreme slow to the extreme fast. Journal of Data Science, 16(4): 771--780. <doi:10.6339/JDS.201810_16(4).00006>. c) Chatzipantsiou C., Dimitriadis M., Papadakis M. and Tsagris M. (2020). Extremely Efficient Permutation and Bootstrap Hypothesis Tests Using Hypothesis Tests Using R. Journal of Modern Applied Statistical Methods, 18(2), eP2898. <doi:10.48550/arXiv.1806.10947>. d) Tsagris M., Papadakis M., Alenazi A. and Alzeley O. (2024). Computationally Efficient Outlier Detection for High-Dimensional Data Using the MDP Algorithm. Computation, 12(9): 185. <doi:10.3390/computation12090185>. e) Tsagris M. and Papadakis M. (2025). Fast and light-weight energy statistics using the R package Rfast. <doi:10.48550/arXiv.2501.02849>.

Maintained by Manos Papadakis. Last updated 1 months ago.

openblascppopenmp

147 stars 12.67 score 1.2k scripts 174 dependents

melff

memisc:Management of Survey Data and Presentation of Analysis Results

An infrastructure for the management of survey data including value labels, definable missing values, recoding of variables, production of code books, and import of (subsets of) 'SPSS' and 'Stata' files is provided. Further, the package allows to produce tables and data frames of arbitrary descriptive statistics and (almost) publication-ready tables of regression model estimates, which can be exported to 'LaTeX' and HTML.

Maintained by Martin Elff. Last updated 25 days ago.

survey-data

46 stars 12.34 score 1.2k scripts 13 dependents

gpaux

Mediana:Clinical Trial Simulations

Provides a general framework for clinical trial simulations based on the Clinical Scenario Evaluation (CSE) approach. The package supports a broad class of data models (including clinical trials with continuous, binary, survival-type and count-type endpoints as well as multivariate outcomes that are based on combinations of different endpoints), analysis strategies and commonly used evaluation criteria.

Maintained by Gautier Paux. Last updated 4 years ago.

biostatisticsclinical-trial-simulationsclinical-trialssimulations

29 stars 6.53 score 39 scripts

jamovi

jmvcore:Dependencies for the 'jamovi' Framework

A framework for creating rich interactive analyses for the jamovi platform (see <https://www.jamovi.org> for more information).

Maintained by Jonathon Love. Last updated 7 months ago.

4 stars 6.51 score 20 scripts 8 dependents

jacobseedorff21

BranchGLM:Efficient Best Subset Selection for GLMs via Branch and Bound Algorithms

Performs efficient and scalable glm best subset selection using a novel implementation of a branch and bound algorithm. To speed up the model fitting process, a range of optimization methods are implemented in 'RcppArmadillo'. Parallel computation is available using 'OpenMP'.

Maintained by Jacob Seedorff. Last updated 6 months ago.

generalized-linear-modelsregressionstatisticssubset-selectionvariable-selectionopenblascppopenmp

7 stars 6.20 score 30 scripts

frictionlessdata

tableschema.r:Table Schema 'Frictionless Data'

Allows to work with 'Table Schema' (<https://specs.frictionlessdata.io/table-schema/>). 'Table Schema' is well suited for use cases around handling and validating tabular data in text formats such as 'csv', but its utility extends well beyond this core usage, towards a range of applications where data benefits from a portable schema format. The 'tableschema.r' package can load and validate any table schema descriptor, allow the creation and modification of descriptors, expose methods for reading and streaming data that conforms to a 'Table Schema' via the 'Tabular Data Resource' abstraction.

Maintained by Kleanthis Koupidis. Last updated 2 years ago.

25 stars 5.70 score 101 scripts

stencila

stencilaschema:Bindings for Stencila Schema

Provides R bindings for the Stencila Schema <https://schema.stenci.la>. This package is primarily aimed at R developers wanting to programmatically generate, or modify, executable documents.

Maintained by Nokome Bentley. Last updated 3 years ago.

json-schemapythonrustschema-orgsemantictypescriptvocabulary

17 stars 4.93 score 2 scripts

lgnbhl

xlcharts:Create Native 'Excel' Charts and Work with Microsoft 'Excel' Files

An R interface to the 'OpenPyXL' 'Python' library to create native 'Excel' charts and work with Microsoft 'Excel' files.

Maintained by Felix Luginbuhl. Last updated 25 days ago.

excel

12 stars 4.48 score 4 scripts

schweflo

pandocfilters:Pandoc Filters for R

The document converter 'pandoc' <https://pandoc.org/> is widely used in the R community. One feature of 'pandoc' is that it can produce and consume JSON-formatted abstract syntax trees (AST). This allows to transform a given source document into JSON-formatted AST, alter it by so called filters and pass the altered JSON-formatted AST back to 'pandoc'. This package provides functions which allow to write such filters in native R code. Although this package is inspired by the Python package 'pandocfilters' <https://github.com/jgm/pandocfilters/>, it provides additional convenience functions which make it simple to use the 'pandocfilters' package as a report generator. Since 'pandocfilters' inherits most of it's functionality from 'pandoc' it can create documents in many formats (for more information see <https://pandoc.org/>) but is also bound to the same limitations as 'pandoc'.

Maintained by Florian Schwendinger. Last updated 3 years ago.

2.80 score 63 scripts

igrave

ladder.api:Google Slides API client and tools

Create, read and modify Slides presentations with full REST API functionality.

Maintained by Isaac Gravestock. Last updated 8 months ago.

slides

2.40 score

cran

rhosp:Side Effect Risks in Hospital : Simulation and Estimation

Evaluating risk (that a patient arises a side effect) during hospitalization is the main purpose of this package. Several methods (Parametric, non parametric and De Vielder estimation) to estimate the risk constant (R) are implemented in this package. There are also functions to simulate the different models of this issue in order to quantify the previous estimators. It is necessary to read at least the first six pages of the report to understand the topic.

Maintained by Christophe Dutang. Last updated 7 years ago.

2.00 score

certara-jcraig

Certara.RDarwin:Interface for 'pyDarwin' Machine Learning Pharmacometric Model Development

Utilities that support the usage of 'pyDarwin' (<https://certara.github.io/pyDarwin/>) for ease of setup and execution of a machine learning based pharmacometric model search with Certara's Non-Linear Mixed Effects (NLME) modeling engine.

Maintained by James Craig. Last updated 1 months ago.

1.70 score

osmandag

GMDH2:Binary Classification via GMDH-Type Neural Network Algorithms

Performs binary classification via Group Method of Data Handling (GMDH) - type neural network algorithms. There exist two main algorithms available in GMDH() and dceGMDH() functions. GMDH() performs classification via GMDH algorithm for a binary response and returns important variables. dceGMDH() performs classification via diverse classifiers ensemble based on GMDH (dce-GMDH) algorithm. Also, the package produces a well-formatted table of descriptives for a binary response. Moreover, it produces confusion matrix, its related statistics and scatter plot (2D and 3D) with classification labels of binary classes to assess the prediction performance. All 'GMDH2' functions are designed for a binary response (Dag et al., 2019, <https://download.atlantis-press.com/article/125911202.pdf>).

Maintained by Osman Dag. Last updated 2 years ago.

1 stars 1.28 score 19 scripts

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