Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 13 packages in 0.11 seconds

tailloss — by Isabella Gollini, 9 years ago

Estimate the Probability in the Upper Tail of the Aggregate Loss Distribution

Set of tools to estimate the probability in the upper tail of the aggregate loss distribution using different methods: Panjer recursion, Monte Carlo simulations, Markov bound, Cantelli bound, Moment bound, and Chernoff bound.

lvm4net — by Isabella Gollini, 5 years ago

Latent Variable Models for Networks

Latent variable models for network data using fast inferential procedures. For more information please visit: < http://igollini.github.io/lvm4net/>.

GWmodel — by Binbin Lu, 5 months ago

Geographically-Weighted Models

Techniques from a particular branch of spatial statistics,termed geographically-weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there are spatial regions where a suitably localised calibration provides a better description. 'GWmodel' includes functions to calibrate: GW summary statistics (Brunsdon et al., 2002), GW principal components analysis (Harris et al., 2011), GW discriminant analysis (Brunsdon et al., 2007) and various forms of GW regression (Brunsdon et al., 1996); some of which are provided in basic and robust (outlier resistant) forms.

vangogh — by Cheryl Isabella, 2 years ago

A Vincent Van Gogh Color Palette Generator

Palettes generated from Vincent van Gogh's paintings.

SECFISH — by Isabella Bitetto, 5 years ago

Disaggregate Variable Costs

These functions were developed within SECFISH project (Strengthening regional cooperation in the area of fisheries data collection-Socio-economic data collection for fisheries, aquaculture and the processing industry at EU level). They are aimed at identifying correlations between costs and transversal variables by metier using individual vessel data and for disaggregating variable costs from fleet segment to metier level.

Seurat — by Rahul Satija, a month ago

Tools for Single Cell Genomics

A toolkit for quality control, analysis, and exploration of single cell RNA sequencing data. 'Seurat' aims to enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data. See Satija R, Farrell J, Gennert D, et al (2015) , Macosko E, Basu A, Satija R, et al (2015) , Stuart T, Butler A, et al (2019) , and Hao, Hao, et al (2020) for more details.

effectsizescr — by Isabella Giammusso, 6 years ago

Indices for Single-Case Research

Parametric and nonparametric statistics for single-case design. Regarding nonparametric statistics, the index suggested by Parker, Vannest, Davis and Sauber (2011) was included. It combines both nonoverlap and trend to estimate the effect size of a treatment in a single case design.

MLDataR — by Gary Hutson, 2 years ago

Collection of Machine Learning Datasets for Supervised Machine Learning

Contains a collection of datasets for working with machine learning tasks. It will contain datasets for supervised machine learning Jiang (2020) and will include datasets for classification and regression. The aim of this package is to use data generated around health and other domains.

lenght — by Jacob Bradley, a year ago

Allow Misspellings of Length Function

Convenient aliases for common ways of misspelling the base R function length(). These include every permutation of the final three letters.

aMNLFA — by Veronica Cole, 2 years ago

Automated Moderated Nonlinear Factor Analysis Using 'M-plus'

Automated generation, running, and interpretation of moderated nonlinear factor analysis models for obtaining scores from observed variables, using the method described by Gottfredson and colleagues (2019) . This package creates M-plus input files which may be run iteratively to test two different types of covariate effects on items: (1) latent variable impact (both mean and variance); and (2) differential item functioning. After sequentially testing for all effects, it also creates a final model by including all significant effects after adjusting for multiple comparisons. Finally, the package creates a scoring model which uses the final values of parameter estimates to generate latent variable scores. \n\n This package generates TEMPLATES for M-plus inputs, which can and should be inspected, altered, and run by the user. In addition to being presented without warranty of any kind, the package is provided under the assumption that everyone who uses it is reading, interpreting, understanding, and altering every M-plus input and output file. There is no one right way to implement moderated nonlinear factor analysis, and this package exists solely to save users time as they generate M-plus syntax according to their own judgment.