close

Showing 5 of total 5 results (show query)

andrisignorell

DescTools:Tools for Descriptive Statistics

A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.

Maintained by Andri Signorell. Last updated 5 days ago.

fortrancpp

86 stars 16.73 score 7.7k scripts 101 dependents

bluefoxr

COINr:Composite Indicator Construction and Analysis

A comprehensive high-level package, for composite indicator construction and analysis. It is a "development environment" for composite indicators and scoreboards, which includes utilities for construction (indicator selection, denomination, imputation, data treatment, normalisation, weighting and aggregation) and analysis (multivariate analysis, correlation plotting, short cuts for principal component analysis, global sensitivity analysis, and more). A composite indicator is completely encapsulated inside a single hierarchical list called a "coin". This allows a fast and efficient work flow, as well as making quick copies, testing methodological variations and making comparisons. It also includes many plotting options, both statistical (scatter plots, distribution plots) as well as for presenting results.

Maintained by William Becker. Last updated 2 months ago.

26 stars 8.94 score 73 scripts 1 dependents

jwb133

dejaVu:Multiple Imputation for Recurrent Events

Performs reference based multiple imputation of recurrent event data based on a negative binomial regression model, as described by Keene et al (2014) <doi:10.1002/pst.1624>.

Maintained by Jonathan Bartlett. Last updated 9 months ago.

4.68 score 24 scripts

bioc

ADImpute:Adaptive Dropout Imputer (ADImpute)

Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble.

Maintained by Ana Carolina Leote. Last updated 5 months ago.

geneexpressionnetworkpreprocessingsequencingsinglecelltranscriptomics

4.30 score 7 scripts

indenkun

MissMech:Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random

To test whether the missing data mechanism, in a set of incompletely observed data, is one of missing completely at random (MCAR). For detailed description see Jamshidian, M. Jalal, S., and Jansen, C. (2014). "MissMech: An R Package for Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random (MCAR)", Journal of Statistical Software, 56(6), 1-31. <https://www.jstatsoft.org/v56/i06/> <doi:10.18637/jss.v056.i06>.

Maintained by Mao Kobayashi. Last updated 1 years ago.

missing-data

3.54 score 54 scripts

: