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tidyverse
dplyr:A Grammar of Data Manipulation
A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
Maintained by Hadley Wickham. Last updated 26 days ago.
4.8k stars 24.68 score 659k scripts 7.8k dependents
gergness
srvyr:'dplyr'-Like Syntax for Summary Statistics of Survey Data
Use piping, verbs like 'group_by' and 'summarize', and other 'dplyr' inspired syntactic style when calculating summary statistics on survey data using functions from the 'survey' package.
Maintained by Greg Freedman Ellis. Last updated 2 months ago.
215 stars 13.88 score 1.8k scripts 15 dependents
statisfactions
simpr:Flexible 'Tidyverse'-Friendly Simulations
A general, 'tidyverse'-friendly framework for simulation studies, design analysis, and power analysis. Specify data generation, define varying parameters, generate data, fit models, and tidy model results in a single pipeline, without needing loops or custom functions.
Maintained by Ethan Brown. Last updated 9 months ago.
43 stars 6.89 score 30 scripts
bioc
RNAdecay:Maximum Likelihood Decay Modeling of RNA Degradation Data
RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions.
Maintained by Reed Sorenson. Last updated 5 months ago.
immunooncologysoftwaregeneexpressiongeneregulationdifferentialexpressiontranscriptiontranscriptomicstimecourseregressionrnaseqnormalizationworkflowstep
4.18 score 2 scripts