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bcallaway11

did:Treatment Effects with Multiple Periods and Groups

The standard Difference-in-Differences (DID) setup involves two periods and two groups -- a treated group and untreated group. Many applications of DID methods involve more than two periods and have individuals that are treated at different points in time. This package contains tools for computing average treatment effect parameters in Difference in Differences setups with more than two periods and with variation in treatment timing using the methods developed in Callaway and Sant'Anna (2021) <doi:10.1016/j.jeconom.2020.12.001>. The main parameters are group-time average treatment effects which are the average treatment effect for a particular group at a a particular time. These can be aggregated into a fewer number of treatment effect parameters, and the package deals with the cases where there is selective treatment timing, dynamic treatment effects, calendar time effects, or combinations of these. There are also functions for testing the Difference in Differences assumption, and plotting group-time average treatment effects.

Maintained by Brantly Callaway. Last updated 3 days ago.

329 stars 12.09 score 696 scripts 3 dependents

trevorhastie

gam:Generalized Additive Models

Functions for fitting and working with generalized additive models, as described in chapter 7 of "Statistical Models in S" (Chambers and Hastie (eds), 1991), and "Generalized Additive Models" (Hastie and Tibshirani, 1990).

Maintained by Trevor Hastie. Last updated 7 months ago.

fortranopenblas

4 stars 9.50 score 2.2k scripts 61 dependents

oscarperpinan

rasterVis:Visualization Methods for Raster Data

Methods for enhanced visualization and interaction with raster data. It implements visualization methods for quantitative data and categorical data, both for univariate and multivariate rasters. It also provides methods to display spatiotemporal rasters, and vector fields. See the website for examples.

Maintained by Oscar Perpinan Lamigueiro. Last updated 1 years ago.

2 stars 8.06 score 4.2k scripts 13 dependents

cran

sna:Tools for Social Network Analysis

A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.

Maintained by Carter T. Butts. Last updated 7 months ago.

8 stars 6.78 score 94 dependents

bioc

plotGrouper:Shiny app GUI wrapper for ggplot with built-in statistical analysis

A shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed).

Maintained by John D. Gagnon. Last updated 5 months ago.

immunooncologyflowcytometrygraphandnetworkstatisticalmethoddataimportguimultiplecomparisonbioconductorggplot2plottingshiny

6 stars 4.78 score 10 scripts

osmandag

onewaytests:One-Way Tests in Independent Groups Designs

Performs one-way tests in independent groups designs including homoscedastic and heteroscedastic tests. These are one-way analysis of variance (ANOVA), Welch's heteroscedastic F test, Welch's heteroscedastic F test with trimmed means and Winsorized variances, Brown-Forsythe test, Alexander-Govern test, James second order test, Kruskal-Wallis test, Scott-Smith test, Box F test, Johansen F test, Generalized tests equivalent to Parametric Bootstrap and Fiducial tests, Alvandi's F test, Alvandi's generalized p-value, approximate F test, B square test, Cochran test, Weerahandi's generalized F test, modified Brown-Forsythe test, adjusted Welch's heteroscedastic F test, Welch-Aspin test, Permutation F test. The package performs pairwise comparisons and graphical approaches. Also, the package includes Student's t test, Welch's t test and Mann-Whitney U test for two samples. Moreover, it assesses variance homogeneity and normality of data in each group via tests and plots (Dag et al., 2018, <https://journal.r-project.org/archive/2018/RJ-2018-022/RJ-2018-022.pdf>).

Maintained by Osman Dag. Last updated 1 years ago.

4.17 score 211 scripts 2 dependents

cran

metRology:Support for Metrological Applications

Provides classes and calculation and plotting functions for metrology applications, including measurement uncertainty estimation and inter-laboratory metrology comparison studies.

Maintained by Stephen L R Ellison. Last updated 2 months ago.

5 stars 3.41 score 7 dependents

dkimstatlab

GSD:Graph Signal Decomposition

Graph signals residing on the vertices of a graph have recently gained prominence in research in various fields. Many methodologies have been proposed to analyze graph signals by adapting classical signal processing tools. Recently, several notable graph signal decomposition methods have been proposed, which include graph Fourier decomposition based on graph Fourier transform, graph empirical mode decomposition, and statistical graph empirical mode decomposition. This package efficiently implements multiscale analysis applicable to various fields, and offers an effective tool for visualizing and decomposing graph signals. For the detailed methodology, see Ortega et al. (2018) <doi:10.1109/JPROC.2018.2820126>, Shuman et al. (2013) <doi:10.1109/MSP.2012.2235192>, Tremblay et al. (2014) <https://www.eurasip.org/Proceedings/Eusipco/Eusipco2014/HTML/papers/1569922141.pdf>, and Cho et al. (2024) "Statistical graph empirical mode decomposition by graph denoising and boundary treatment".

Maintained by Donghoh Kim. Last updated 1 years ago.

2.70 score

gsantafe

bde:Bounded Density Estimation

A collection of S4 classes which implements different methods to estimate and deal with densities in bounded domains. That is, densities defined within the interval [lower.limit, upper.limit], where lower.limit and upper.limit are values that can be set by the user.

Maintained by Guzman Santafe. Last updated 3 years ago.

2.37 score 26 scripts 3 dependents

luigiaugugliaro

sglasso:Lasso Method for RCON(V,E) Models

RCON(V, E) models are a kind of restriction of the Gaussian Graphical Models defined by a set of equality constraints on the entries of the concentration matrix. 'sglasso' package implements the structured graphical lasso (sglasso) estimator proposed in Abbruzzo et al. (2014) for the weighted l1-penalized RCON(V, E) model. Two cyclic coordinate algorithms are implemented to compute the sglasso estimator, i.e. a cyclic coordinate minimization (CCM) and a cyclic coordinate descent (CCD) algorithm.

Maintained by Luigi Augugliaro. Last updated 1 years ago.

fortranopenblas

1.04 score 11 scripts

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