Showing 5 of total 5 results (show query)
trilnick
bunchr:Analyze Bunching in a Kink or Notch Setting
View and analyze data where bunching is expected. Estimate counter- factual distributions. For earnings data, estimate the compensated elasticity of earnings w.r.t. the net-of-tax rate.
Maintained by Itai Trilnick. Last updated 1 years ago.
bunchingelasticitykinksnotchtax-rate
25.9 match 7 stars 5.02 score 15 scripts
mavpanos
bunching:Estimate Bunching
Implementation of the bunching estimator for kinks and notches. Allows for flexible estimation of counterfactual (e.g. controlling for round number bunching, accounting for other bunching masses within bunching window, fixing bunching point to be minimum, maximum or median value in its bin, etc.). It produces publication-ready plots in the style followed since Chetty et al. (2011) <doi:10.1093/qje/qjr013>, with lots of functionality to set plot options.
Maintained by Panos Mavrokonstantis. Last updated 2 years ago.
11.7 match 5 stars 4.70 score 5 scripts
gjmvanboxtel
gsignal:Signal Processing
R implementation of the 'Octave' package 'signal', containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.
Maintained by Geert van Boxtel. Last updated 2 months ago.
1.9 match 24 stars 10.03 score 133 scripts 34 dependents
dipterix
ravetools:Signal and Image Processing Toolbox for Analyzing Intracranial Electroencephalography Data
Implemented fast and memory-efficient Notch-filter, Welch-periodogram, discrete wavelet spectrogram for minutes of high-resolution signals, fast 3D convolution, image registration, 3D mesh manipulation; providing fundamental toolbox for intracranial Electroencephalography (iEEG) pipelines. Documentation and examples about 'RAVE' project are provided at <https://rave.wiki>, and the paper by John F. Magnotti, Zhengjia Wang, Michael S. Beauchamp (2020) <doi:10.1016/j.neuroimage.2020.117341>; see 'citation("ravetools")' for details.
Maintained by Zhengjia Wang. Last updated 7 days ago.
2.5 match 3 stars 5.13 score 20 scripts 1 dependents
bioc
vidger:Create rapid visualizations of RNAseq data in R
The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR.
Maintained by Brandon Monier. Last updated 5 months ago.
immunooncologyvisualizationrnaseqdifferentialexpressiongeneexpressiondata-mungingdifferential-expressiongene-expressionrna-seq-analysis
1.5 match 19 stars 6.61 score 27 scripts