Showing 7 of total 7 results (show query)
bioc
GNOSIS:Genomics explorer using statistical and survival analysis in R
GNOSIS incorporates a range of R packages enabling users to efficiently explore and visualise clinical and genomic data obtained from cBioPortal. GNOSIS uses an intuitive GUI and multiple tab panels supporting a range of functionalities. These include data upload and initial exploration, data recoding and subsetting, multiple visualisations, survival analysis, statistical analysis and mutation analysis, in addition to facilitating reproducible research.
Maintained by Lydia King. Last updated 5 months ago.
5 stars 4.70 score 2 scripts
giocomai
castarter:Content Analysis Starter Toolkit
Consistent approaches for basic web scraping, text mining and word frequency analysis of textual datasets.
Maintained by Giorgio Comai. Last updated 22 hours ago.
3 stars 4.59 score 2 scripts
certara-jcraig
Certara.VPCResults:Generate Visual Predictive Checks (VPC) Using 'shiny'
Utilize the 'shiny' interface to parameterize a Visual Predictive Check (VPC), including selecting from different binning or binless methods and performing stratification, censoring, and prediction correction. Generate the underlying 'tidyvpc' and 'ggplot2' code directly from the user interface and download R or Rmd scripts to reproduce the VPCs in R.
Maintained by James Craig. Last updated 4 months ago.
2.11 score 13 scriptscertara-jcraig
Certara.RsNLME.ModelExecutor:Execute Pharmacometric Models Using 'shiny'
Execute Nonlinear Mixed Effects (NLME) models for pharmacometrics using a 'shiny' interface. Specify engine parameters and select from different run options, including simple estimation, stepwise covariate search, bootstrapping, simulation, visual predictive check, and more. Models are executed using the 'Certara.RsNLME' package.
Maintained by James Craig. Last updated 11 days ago.
2.00 score 3 scriptscertara-jcraig
Certara.ModelResults:Generate Diagnostics for Pharmacometric Models Using 'shiny'
Utilize the 'shiny' interface to generate Goodness of Fit (GOF) plots and tables for Non-Linear Mixed Effects (NLME / NONMEM) pharmacometric models. From the interface, users can customize model diagnostics and generate the underlying R code to reproduce the diagnostic plots and tables outside of the 'shiny' session. Model diagnostics can be included in a 'rmarkdown' document and rendered to desired output format.
Maintained by James Craig. Last updated 27 days ago.
1.70 scorecertara-jcraig
Certara.DarwinReporter:Data Visualization Utilities for 'pyDarwin' Machine Learning Pharmacometric Model Development
Utilize the 'shiny' interface for visualizing results from a 'pyDarwin' (<https://certara.github.io/pyDarwin/>) machine learning pharmacometric model search. It generates Goodness-of-Fit plots and summary tables for selected models, allowing users to customize diagnostic outputs within the interface. The underlying R code for generating plots and tables can be extracted for use outside the interactive session. Model diagnostics can also be incorporated into an R Markdown document and rendered in various output formats.
Maintained by James Craig. Last updated 23 days ago.
1.70 scorecertara-jcraig
Certara.RsNLME.ModelBuilder:Pharmacometric Model Building Using 'shiny'
Develop Nonlinear Mixed Effects (NLME) models for pharmacometrics using a 'shiny' interface. The Pharmacometric Modeling Language (PML) code updates in real time given changes to user inputs. Models can be executed using the 'Certara.RsNLME' package. Additional support to generate the underlying 'Certara.RsNLME' code to recreate the corresponding model in R is provided in the user interface.
Maintained by James Craig. Last updated 3 months ago.
1.70 score 8 scripts