close

Showing 17 of total 17 results (show query)

harrelfe

Hmisc:Harrell Miscellaneous

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to LaTeX and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.

Maintained by Frank E Harrell Jr. Last updated 9 hours ago.

fortran

58.7 match 210 stars 17.61 score 17k scripts 750 dependents

corradolanera

depigner:A Utility Package to Help you Deal with "Pignas"

Pigna [_pìn'n'a_] is the Italian word for pine cone. In jargon, it is used to identify a task which is boring, banal, annoying, painful, frustrating and maybe even with a not so beautiful or rewarding result, just like the obstinate act of trying to challenge yourself in extracting pine nuts from a pine cone, provided that, in the end, you will find at least one inside it. Here you can find a backpack of functions to be used to solve small everyday problems of coding or analyzing (clinical) data, which would be normally solved using quick-and-dirty patches. You will be able to convert 'Hmisc' and 'rms' summary()es into data.frames ready to be rendered by 'pander' and 'knitr'. You can access easy-to-use wrappers to activate essential but useful progress bars (from 'progress') into your loops or functionals. Easy setup and control Telegram's bots (from 'telegram.bot') to send messages or to divert error messages to a Telegram's chat. You also have some utilities helping you in the development of packages, like the activation of the same user interface of 'usethis' into your package, or call polite functions to ask a user to install other packages. Finally, you find a set of thematic sets of packages you may use to set up new environments quickly, installing them in a single call.

Maintained by Corrado Lanera. Last updated 2 years ago.

hmiscpignermstelegram

13.3 match 3 stars 4.14 score 93 scripts

spgarbet

tangram:The Grammar of Tables

Provides an extensible formula system to quickly and easily create production quality tables. The processing steps are a formula parser, statistical content generation from data as defined by formula, followed by rendering into a table. Each step of the processing is separate and user definable thus creating a set of composable building blocks for highly customizable table generation. A user is not limited by any of the choices of the package creator other than the formula grammar. For example, one could chose to add a different S3 rendering function and output a format not provided in the default package, or possibly one would rather have Gini coefficients for their statistical content in a resulting table. Routines to achieve New England Journal of Medicine style, Lancet style and Hmisc::summaryM() statistics are provided. The package contains rendering for HTML5, Rmarkdown and an indexing format for use in tracing and tracking are provided.

Maintained by Shawn Garbett. Last updated 2 years ago.

9.2 match 68 stars 5.93 score 62 scripts

tidyverse

ggplot2:Create Elegant Data Visualisations Using the Grammar of Graphics

A system for 'declaratively' creating graphics, based on "The Grammar of Graphics". You provide the data, tell 'ggplot2' how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

Maintained by Thomas Lin Pedersen. Last updated 9 days ago.

data-visualisationvisualisation

1.9 match 6.6k stars 25.10 score 645k scripts 7.5k dependents

gforge

Gmisc:Descriptive Statistics, Transition Plots, and More

Tools for making the descriptive "Table 1" used in medical articles, a transition plot for showing changes between categories (also known as a Sankey diagram), flow charts by extending the grid package, a method for variable selection based on the SVD, Bézier lines with arrows complementing the ones in the 'grid' package, and more.

Maintained by Max Gordon. Last updated 2 years ago.

cpp

3.6 match 50 stars 10.40 score 233 scripts 2 dependents

njtierney

naniar:Data Structures, Summaries, and Visualisations for Missing Data

Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of 'ggplot2' and tidy data. The work is fully discussed at Tierney & Cook (2023) <doi:10.18637/jss.v105.i07>.

Maintained by Nicholas Tierney. Last updated 4 days ago.

data-visualisationggplot2missing-datamissingnesstidy-data

1.5 match 657 stars 15.63 score 5.1k scripts 9 dependents

juba

questionr:Functions to Make Surveys Processing Easier

Set of functions to make the processing and analysis of surveys easier : interactive shiny apps and addins for data recoding, contingency tables, dataset metadata handling, and several convenience functions.

Maintained by Julien Barnier. Last updated 1 days ago.

1.7 match 83 stars 12.62 score 1.1k scripts 19 dependents

lionel-

ggstance:Horizontal 'ggplot2' Components

A 'ggplot2' extension that provides flipped components: horizontal versions of 'Stats' and 'Geoms', and vertical versions of 'Positions'. This package is now superseded by 'ggplot2' itself which now has full native support for horizontal layouts. It remains available for backward compatibility.

Maintained by Lionel Henry. Last updated 10 months ago.

1.8 match 201 stars 10.95 score 1.0k scripts 5 dependents

andrewhooker

PopED:Population (and Individual) Optimal Experimental Design

Optimal experimental designs for both population and individual studies based on nonlinear mixed-effect models. Often this is based on a computation of the Fisher Information Matrix. This package was developed for pharmacometric problems, and examples and predefined models are available for these types of systems. The methods are described in Nyberg et al. (2012) <doi:10.1016/j.cmpb.2012.05.005>, and Foracchia et al. (2004) <doi:10.1016/S0169-2607(03)00073-7>.

Maintained by Andrew C. Hooker. Last updated 5 months ago.

nlmeoptimal-designpharmacodynamicspharmacokineticspharmacometricspkpdpopulationpopulation-model

1.7 match 33 stars 9.58 score 300 scripts 1 dependents

animint

animint2:Animated Interactive Grammar of Graphics

Functions are provided for defining animated, interactive data visualizations in R code, and rendering on a web page. The 2018 Journal of Computational and Graphical Statistics paper, <doi:10.1080/10618600.2018.1513367> describes the concepts implemented.

Maintained by Toby Hocking. Last updated 27 days ago.

1.7 match 64 stars 8.87 score 173 scripts

henrikbengtsson

PSCBS:Analysis of Parent-Specific DNA Copy Numbers

Segmentation of allele-specific DNA copy number data and detection of regions with abnormal copy number within each parental chromosome. Both tumor-normal paired and tumor-only analyses are supported.

Maintained by Henrik Bengtsson. Last updated 1 years ago.

acghcopynumbervariantssnpmicroarrayonechanneltwochannelgenetics

1.5 match 7 stars 7.63 score 34 scripts 9 dependents

rmheiberger

HH:Statistical Analysis and Data Display: Heiberger and Holland

Support software for Statistical Analysis and Data Display (Second Edition, Springer, ISBN 978-1-4939-2121-8, 2015) and (First Edition, Springer, ISBN 0-387-40270-5, 2004) by Richard M. Heiberger and Burt Holland. This contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The second edition includes redesigned graphics and additional chapters. The authors emphasize how to construct and interpret graphs, discuss principles of graphical design, and show how accompanying traditional tabular results are used to confirm the visual impressions derived directly from the graphs. Many of the graphical formats are novel and appear here for the first time in print. All chapters have exercises. All functions introduced in the book are in the package. R code for all examples, both graphs and tables, in the book is included in the scripts directory of the package.

Maintained by Richard M. Heiberger. Last updated 1 months ago.

1.7 match 3 stars 6.42 score 752 scripts 5 dependents

tirgit

missCompare:Intuitive Missing Data Imputation Framework

Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. These include simpler methods, such as mean and median imputation and random replacement, but also include more sophisticated algorithms already implemented in popular R packages, such as 'mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; 'mice', described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; 'missForest', described by Stekhoven and Buhlmann (2012) <doi:10.1093/bioinformatics/btr597>; 'missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and 'pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. The central assumption behind 'missCompare' is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. 'missCompare' takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. 'missCompare' will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance.

Maintained by Tibor V. Varga. Last updated 4 years ago.

comparisoncomparison-benchmarksimputationimputation-algorithmimputation-methodsimputationskolmogorov-smirnovmissingmissing-datamissing-data-imputationmissing-status-checkmissing-valuesmissingnesspost-imputation-diagnosticsrmse

1.7 match 39 stars 5.89 score 40 scripts

nutterb

labelVector:Label Attributes for Atomic Vectors

Labels are a common construct in statistical software providing a human readable description of a variable. While variable names are succinct, quick to type, and follow a language's naming conventions, labels may be more illustrative and may use plain text and spaces. R does not provide native support for labels. Some packages, however, have made this feature available. Most notably, the 'Hmisc' package provides labelling methods for a number of different object. Due to design decisions, these methods are not all exported, and so are unavailable for use in package development. The 'labelVector' package supports labels for atomic vectors in a light-weight design that is suitable for use in other packages.

Maintained by Benjamin Nutter. Last updated 3 years ago.

2.0 match 4.21 score 18 scripts 6 dependents

rmheiberger

microplot:Microplots (Sparklines) in 'LaTeX', 'Word', 'HTML', 'Excel'

The microplot function writes a set of R graphics files to be used as microplots (sparklines) in tables in either 'LaTeX', 'HTML', 'Word', or 'Excel' files. For 'LaTeX', we provide methods for the Hmisc::latex() generic function to construct 'latex' tabular environments which include the graphs. These can be used directly with the operating system 'pdflatex' or 'latex' command, or by using one of 'Sweave', 'knitr', 'rmarkdown', or 'Emacs org-mode' as an intermediary. For 'MS Word', the msWord() function uses the 'flextable' package to construct 'Word' tables which include the graphs. There are several distinct approaches for constructing HTML files. The simplest is to use the msWord() function with argument filetype="html". Alternatively, use either 'Emacs org-mode' or the htmlTable::htmlTable() function to construct an 'HTML' file containing tables which include the graphs. See the documentation for our as.htmlimg() function. For 'Excel' use on 'Windows', the file examples/irisExcel.xls includes 'VBA' code which brings the individual panels into individual cells in the spreadsheet. Examples in the examples and demo subdirectories are shown with 'lattice' graphics, 'ggplot2' graphics, and 'base' graphics. Examples for 'LaTeX' include 'Sweave' (both 'LaTeX'-style and 'Noweb'-style), 'knitr', 'emacs org-mode', and 'rmarkdown' input files and their 'pdf' output files. Examples for 'HTML' include 'org-mode' and 'Rmd' input files and their webarchive 'HTML' output files. In addition, the as.orgtable() function can display a data.frame in an 'org-mode' document. The examples for 'MS Word' (with either filetype="docx" or filetype="html") work with all operating systems. The package does not require the installation of 'LaTeX' or 'MS Word' to be able to write '.tex' or '.docx' files.

Maintained by Richard M. Heiberger. Last updated 3 years ago.

2.1 match 1 stars 2.56 score 36 scripts

landroni

RcmdrPlugin.Export:Export R Output to LaTeX or HTML

Export Rcmdr output to LaTeX or HTML code. The plug-in was originally intended to facilitate exporting Rcmdr output to formats other than ASCII text and to provide R novices with an easy-to-use, easy-to-access reference on exporting R objects to formats suited for printed output. The package documentation contains several pointers on creating reports, either by using conventional word processors or LaTeX/LyX.

Maintained by Liviu Andronic. Last updated 9 years ago.

1.8 match 2.70 score 3 scripts

ajiangsfu

csmpv:Biomarker Confirmation, Selection, Modelling, Prediction, and Validation

There are diverse purposes such as biomarker confirmation, novel biomarker discovery, constructing predictive models, model-based prediction, and validation. It handles binary, continuous, and time-to-event outcomes at the sample or patient level. - Biomarker confirmation utilizes established functions like glm() from 'stats', coxph() from 'survival', surv_fit(), and ggsurvplot() from 'survminer'. - Biomarker discovery and variable selection are facilitated by three LASSO-related functions LASSO2(), LASSO_plus(), and LASSO2plus(), leveraging the 'glmnet' R package with additional steps. - Eight versatile modeling functions are offered, each designed for predictive models across various outcomes and data types. 1) LASSO2(), LASSO_plus(), LASSO2plus(), and LASSO2_reg() perform variable selection using LASSO methods and construct predictive models based on selected variables. 2) XGBtraining() employs 'XGBoost' for model building and is the only function not involving variable selection. 3) Functions like LASSO2_XGBtraining(), LASSOplus_XGBtraining(), and LASSO2plus_XGBtraining() combine LASSO-related variable selection with 'XGBoost' for model construction. - All models support prediction and validation, requiring a testing dataset comparable to the training dataset. Additionally, the package introduces XGpred() for risk prediction based on survival data, with the XGpred_predict() function available for predicting risk groups in new datasets. The methodology is based on our new algorithms and various references: - Hastie et al. (1992, ISBN 0 534 16765-9), - Therneau et al. (2000, ISBN 0-387-98784-3), - Kassambara et al. (2021) <https://CRAN.R-project.org/package=survminer>, - Friedman et al. (2010) <doi:10.18637/jss.v033.i01>, - Simon et al. (2011) <doi:10.18637/jss.v039.i05>, - Harrell (2023) <https://CRAN.R-project.org/package=rms>, - Harrell (2023) <https://CRAN.R-project.org/package=Hmisc>, - Chen and Guestrin (2016) <arXiv:1603.02754>, - Aoki et al. (2023) <doi:10.1200/JCO.23.01115>.

Maintained by Aixiang Jiang. Last updated 1 years ago.

0.5 match 3.70 score 1 scripts

: