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

Found 78 packages in 0.02 seconds

rWCVP — by Matilda Brown, a year ago

Generating Summaries, Reports and Plots from the World Checklist of Vascular Plants

A companion to the World Checklist of Vascular Plants (WCVP). It includes functions to generate maps and species lists, as well as match names to the WCVP. For more details and to cite the package, see: Brown M.J.M., Walker B.E., Black N., Govaerts R., Ondo I., Turner R., Nic Lughadha E. (in press). "rWCVP: A companion R package to the World Checklist of Vascular Plants". New Phytologist.

NHSRplotthedots — by Christopher Reading, 2 years ago

Draw XmR Charts for NHSE/I 'Making Data Count' Programme

Provides tools for drawing Statistical Process Control (SPC) charts. This package supports the NHSE/I programme 'Making Data Count', and allows users to draw XmR charts, use change points and apply rules with summary indicators for when rules are breached.

OmicKriging — by Hae Kyung Im, 8 years ago

Poly-Omic Prediction of Complex TRaits

It provides functions to generate a correlation matrix from a genetic dataset and to use this matrix to predict the phenotype of an individual by using the phenotypes of the remaining individuals through kriging. Kriging is a geostatistical method for optimal prediction or best unbiased linear prediction. It consists of predicting the value of a variable at an unobserved location as a weighted sum of the variable at observed locations. Intuitively, it works as a reverse linear regression: instead of computing correlation (univariate regression coefficients are simply scaled correlation) between a dependent variable Y and independent variables X, it uses known correlation between X and Y to predict Y.

metapower — by Jason Griffin, 3 years ago

Power Analysis for Meta-Analysis

A simple and effective tool for computing and visualizing statistical power for meta-analysis, including power analysis of main effects (Jackson & Turner, 2017), test of homogeneity (Pigott, 2012), subgroup analysis, and categorical moderator analysis (Hedges & Pigott, 2004).

spatstat.data — by Adrian Baddeley, 2 months ago

Datasets for 'spatstat' Family

Contains all the datasets for the 'spatstat' family of packages.

TDAstats — by Raoul Wadhwa, 4 years ago

Pipeline for Topological Data Analysis

A comprehensive toolset for any useR conducting topological data analysis, specifically via the calculation of persistent homology in a Vietoris-Rips complex. The tools this package currently provides can be conveniently split into three main sections: (1) calculating persistent homology; (2) conducting statistical inference on persistent homology calculations; (3) visualizing persistent homology and statistical inference. The published form of TDAstats can be found in Wadhwa et al. (2018) . For a general background on computing persistent homology for topological data analysis, see Otter et al. (2017) . To learn more about how the permutation test is used for nonparametric statistical inference in topological data analysis, read Robinson & Turner (2017) . To learn more about how TDAstats calculates persistent homology, you can visit the GitHub repository for Ripser, the software that works behind the scenes at < https://github.com/Ripser/ripser>. This package has been published as Wadhwa et al. (2018) .

EMAS — by Xiuquan Nie, 2 years ago

Epigenome-Wide Mediation Analysis Study

DNA methylation is essential for human, and environment can change the DNA methylation and affect body status. Epigenome-Wide Mediation Analysis Study (EMAS) can find potential mediator CpG sites between exposure (x) and outcome (y) in epigenome-wide. For more information on the methods we used, please see the following references: Tingley, D. (2014) , Turner, S. D. (2018) , Rosseel, D. (2012) .

WhatIf — by Soubhik Barari, 3 years ago

Software for Evaluating Counterfactuals

Inferences about counterfactuals are essential for prediction, answering what if questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based largely on speculation hidden in convenient modeling assumptions that few would be willing to defend. Unfortunately, standard statistical approaches assume the veracity of the model rather than revealing the degree of model-dependence, which makes this problem hard to detect. WhatIf offers easy-to-apply methods to evaluate counterfactuals that do not require sensitivity testing over specified classes of models. If an analysis fails the tests offered here, then we know that substantive inferences will be sensitive to at least some modeling choices that are not based on empirical evidence, no matter what method of inference one chooses to use. WhatIf implements the methods for evaluating counterfactuals discussed in Gary King and Langche Zeng, 2006, "The Dangers of Extreme Counterfactuals," Political Analysis 14 (2) ; and Gary King and Langche Zeng, 2007, "When Can History Be Our Guide? The Pitfalls of Counterfactual Inference," International Studies Quarterly 51 (March) .

dataCompareR — by Sarah Johnston, 2 years ago

Compare Two Data Frames and Summarise the Difference

Easy comparison of two tabular data objects in R. Specifically designed to show differences between two sets of data in a useful way that should make it easier to understand the differences, and if necessary, help you work out how to remedy them. Aims to offer a more useful output than all.equal() when your two data sets do not match, but isn't intended to replace all.equal() as a way to test for equality.

TmCalculator — by Junhui Li, 2 years ago

Melting Temperature of Nucleic Acid Sequences

This tool is extended from methods in Bio.SeqUtils.MeltingTemp of python. The melting temperature of nucleic acid sequences can be calculated in three method, the Wallace rule (Thein & Wallace (1986) ), empirical formulas based on G and C content (Marmur J. (1962) , Schildkraut C. (2010) , Wetmur J G (1991) , Untergasser,A. (2012) , von Ahsen N (2001) ) and nearest neighbor thermodynamics (Breslauer K J (1986) , Sugimoto N (1996) , Allawi H (1998) , SantaLucia J (2004) , Freier S (1986) , Xia T (1998) , Chen JL (2012) , Bommarito S (2000) , Turner D H (2010) , Sugimoto N (1995) , Allawi H T (1997) , Santalucia N (2005) ), and it can also be corrected with salt ions and chemical compound (SantaLucia J (1996) , SantaLucia J(1998) , Owczarzy R (2004) , Owczarzy R (2008) ).