Package: countfitteR 1.4
Jaroslaw Chilimoniuk
countfitteR: Comprehensive Automatized Evaluation of Distribution Models for Count Data
A large number of measurements generate count data. This is a statistical data type that only assumes non-negative integer values and is generated by counting. Typically, counting data can be found in biomedical applications, such as the analysis of DNA double-strand breaks. The number of DNA double-strand breaks can be counted in individual cells using various bioanalytical methods. For diagnostic applications, it is relevant to record the distribution of the number data in order to determine their biomedical significance (Roediger, S. et al., 2018. Journal of Laboratory and Precision Medicine. <doi:10.21037/jlpm.2018.04.10>). The software offers functions for a comprehensive automated evaluation of distribution models of count data. In addition to programmatic interaction, a graphical user interface (web server) is included, which enables fast and interactive data-scientific analyses. The user is supported in selecting the most suitable counting distribution for his own data set.
Authors:
countfitteR_1.4.tar.gz
countfitteR_1.4.zip(r-4.5)countfitteR_1.4.zip(r-4.4)countfitteR_1.4.zip(r-4.3)
countfitteR_1.4.tgz(r-4.4-any)countfitteR_1.4.tgz(r-4.3-any)
countfitteR_1.4.tar.gz(r-4.5-noble)countfitteR_1.4.tar.gz(r-4.4-noble)
countfitteR_1.4.tgz(r-4.4-emscripten)countfitteR_1.4.tgz(r-4.3-emscripten)
countfitteR.pdf |countfitteR.html✨
countfitteR/json (API)
| # Install 'countfitteR' in R: |
| install.packages('countfitteR', repos = c('https://biogenies.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/biogenies/countfitter/issues
- case_study - Short version of the 'case_study_FITC'
- case_study_APC - Case study for APC dye
- case_study_FITC - Case study for FITC dye
- case_study_all - Case study with two fluorescent dyes
- sim_dat - Data created from simulation of NB Poiss
cancercancer-imaging-researchcount-datacount-distributionfoci
Last updated 1 years agofrom:3e52c5e123. Checks:OK: 3 NOTE: 4. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 13 2024 |
| R-4.5-win | NOTE | Nov 13 2024 |
| R-4.5-linux | NOTE | Nov 13 2024 |
| R-4.4-win | NOTE | Nov 13 2024 |
| R-4.4-mac | NOTE | Nov 13 2024 |
| R-4.3-win | OK | Nov 13 2024 |
| R-4.3-mac | OK | Nov 13 2024 |
Exports:compare_fitcountfitteR_guidecidedZINBdZIPfit_countsplot_fitcmpprocess_countsrZINBrZIPselect_modelsummary_fitlistvalidate_counts
Dependencies:base64encbslibcachemclicolorspacecommonmarkcrayondigestfansifarverfastmapfontawesomefsggplot2gluegtablehtmltoolshttpuvisobandjquerylibjsonlitelabelinglaterlatticelifecyclemagrittrMASSMatrixmemoisemgcvmimemunsellnlmepillarpkgconfigpromisespsclR6rappdirsRColorBrewerRcpprlangsassscalesshinysourcetoolstibbleutf8vctrsviridisLitewithrxtable
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| countfitteR - a framework for fitting count distributions in R | countfitteR-package countfitteR |
| Short version of the 'case_study_FITC' | case_study |
| Case study with two fluorescent dyes | case_study_all |
| Case study for APC dye | case_study_APC |
| Case study for FITC dye | case_study_FITC |
| Compare fits | compare_fit |
| countfitteR Graphical User Interface | countfitteR_gui |
| Make a decision based on the BIC value | decide |
| Fit counts to distributions | fit_counts |
| plot_fitcmp | plot_fitcmp |
| Process counts | process_counts |
| Select the most appropriate model | select_model |
| Data created from simulation of NB Poiss | sim_dat |
| Summary of estimates | summary_fitlist |
| Validate data | validate_counts |
| Zero-inflated negative binomial distrbution | dZINB rZINB ZINB zinb |
| Zero-inflated Poisson distrbution | dZIP rZIP ZIP zip |
