Lucy D'Agostino McGowan

Lucy D'Agostino McGowan

9 packages on CRAN

1 packages on GitHub

tidycode

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Analyze lines of R code using tidy principles. This allows you to input lines of R code and output a data frame with one row per function included. Additionally, it facilitates code classification via included lexicons.

tipr

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The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, a plausible residual effect size for an unmeasured continuous or binary confounder, and a realistic mean difference or prevalence difference for this hypothetical confounder. Building on the methods put forth by Lin, Psaty, & Kronmal (1998) <doi:10.2307/2533848>, we can use these quantities to assess how an unmeasured confounder may tip our result to insignificance, rendering the study inconclusive.

cowsay

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Allows printing of character strings as messages/warnings/etc. with ASCII animals, including cats, cows, frogs, chickens, ghosts, and more.

datasauRus

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The Datasaurus Dozen is a set of datasets with the same summary statistics. They retain the same summary statistics despite having radically different distributions. The datasets represent a larger and quirkier object lesson that is typically taught via Anscombe's Quartet (available in the 'datasets' package). Anscombe's Quartet contains four very different distributions with the same summary statistics and as such highlights the value of visualisation in understanding data, over and above summary statistics. As well as being an engaging variant on the Quartet, the data is generated in a novel way. The simulated annealing process used to derive datasets from the original Datasaurus is detailed in "Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing" <doi:10.1145/3025453.3025912>.

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Manage Google Drive files from R.

emo

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Makes it easy to insert 'emoji' based on either their name or a descriptive keyword.

matahari

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Conveniently log everything you type into the R console. Logs are are stored as tidy data frames which can then be analyzed using 'tidyverse' style tools.

rdhs

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Provides a client for (1) querying the DHS API for survey indicators and metadata (<https://api.dhsprogram.com/#/index.html>), (2) identifying surveys and datasets for analysis, (3) downloading survey datasets from the DHS website, (4) loading datasets and associate metadata into R, and (5) extracting variables and combining datasets for pooled analysis.

RJafroc

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Implements software for assessing medical imaging systems, radiologists or computer aided detection algorithms. Models of observer performance are implemented, including the binormal model (BM), the contaminated binormal model (CBM), the correlated contaminated binormal model (CORCBM), and the radiological search model (RSM). The software and applications are described in a book - Chakraborty DP: Observer Performance Methods for Diagnostic Imaging - Foundations, Modeling, and Applications with R-Based Examples. Taylor-Francis LLC; 2017 - and its vignettes <https://dpc10ster.github.io/RJafroc/>. Observer performance data collection paradigms are the receiver operating characteristic (ROC) and its location specific extensions, primarily free-response ROC (FROC) and the location ROC (LROC). ROC data consists of single ratings per images. A rating is the perceived confidence level that the image is that of a diseased patient. FROC data consists of a variable number (including zero) of mark-rating pairs per image, where a mark is the location of a clinically reportable suspicious region and the rating is the corresponding confidence level that it is a real lesion. LROC data consists of a rating and a forced localization of the most suspicious region on every image. RJafroc supersedes the Windows version of JAFROC software V4.2.1, <http://www.devchakraborty.com>:. Package functions are organized as follows. Data file related function names are preceded by Df, curve fitting functions by Fit, included data sets by dataset, plotting functions by Plot, significance testing functions by St, sample size related functions by Ss, data simulation functions by Simulate and utility functions by Util. Implemented are figures of merit (FOMs) for quantifying performance, functions for visualizing empirical operating characteristics: e.g., ROC, FROC, alternative FROC (AFROC) and weighted AFROC (wAFROC) curves. Four maximum likelihood curve-fitting algorithms are implemented: the binormal model (BM), the contaminated binormal model (CBM), the correlated contaminated binormal model (CORCBM) and the radiological search model (RSM). Unlike the binormal model, CBM, CORCBM and RSM predict "proper" ROC curves that do not cross the chance diagonal. RSM fitting additionally yields measures of search and lesion-classification performances. Search performance is the ability to find lesions while avoiding finding non-lesions. Lesion-classification performance is the ability to correctly classify found lesions from found non-lesions. For fully crossed study designs significance testing of reader-averaged FOM differences between modalities is implemented via both Dorfman-Berbaum-Metz and the Obuchowski-Rockette methods, including Hillis' extensions. Also implemented are single treatment analyses, which allow comparison of performance of a group of radiologists to a specified value, or comparison to CAD to a group of radiologists interpreting the same cases. Crossed-modality analysis is implemented wherein there are two crossed treatment factors and the desire is to determined performance in each treatment factor averaged over all levels of the other factor. Sample size estimation tools are provided for ROC and FROC studies; these use estimates of the relevant variances from a pilot study to predict required numbers of readers and cases in a pivotal study to achieve a desired power. Utility and data file manipulation functions allow data to be read in any of the currently used input formats, including Excel, and the results of the analysis can be viewed in text or Excel output files. The methods are illustrated with several included datasets from the author's international collaborations. This version corrects a few bugs noticed by users and extends the Excel file input format for greater flexibility in handling non-crossed datasets and the sample size routines have been rewritten for ease of use.

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Supports the input and reproducible analysis of RTI MicroPEM output files.