Showing 15 of total 15 results (show query)
lrberge
fixest:Fast Fixed-Effects Estimations
Fast and user-friendly estimation of econometric models with multiple fixed-effects. Includes ordinary least squares (OLS), generalized linear models (GLM) and the negative binomial. The core of the package is based on optimized parallel C++ code, scaling especially well for large data sets. The method to obtain the fixed-effects coefficients is based on Berge (2018) <https://github.com/lrberge/fixest/blob/master/_DOCS/FENmlm_paper.pdf>. Further provides tools to export and view the results of several estimations with intuitive design to cluster the standard-errors.
Maintained by Laurent Berge. Last updated 7 months ago.
124.1 match 387 stars 14.69 score 3.8k scripts 25 dependents
tidymodels
broom:Convert Statistical Objects into Tidy Tibbles
Summarizes key information about statistical objects in tidy tibbles. This makes it easy to report results, create plots and consistently work with large numbers of models at once. Broom provides three verbs that each provide different types of information about a model. tidy() summarizes information about model components such as coefficients of a regression. glance() reports information about an entire model, such as goodness of fit measures like AIC and BIC. augment() adds information about individual observations to a dataset, such as fitted values or influence measures.
Maintained by Simon Couch. Last updated 4 months ago.
5.5 match 1.5k stars 21.56 score 37k scripts 1.4k dependents
s3alfisc
summclust:Module to Compute Influence and Leverage Statistics for Regression Models with Clustered Errors
Module to compute cluster specific information for regression models with clustered errors, including leverage and influence statistics. Models of type 'lm' and 'fixest'(from the 'stats' and 'fixest' packages) are supported. 'summclust' implements similar features as the user-written 'summclust.ado' Stata module (MacKinnon, Nielsen & Webb, 2022; <arXiv:2205.03288v1>).
Maintained by Alexander Fischer. Last updated 2 years ago.
clustered-standard-errorsfixestlinear-regressionrobust-inference
16.4 match 6 stars 6.16 score 53 scripts 3 dependentss3alfisc
fwildclusterboot:Fast Wild Cluster Bootstrap Inference for Linear Models
Implementation of fast algorithms for wild cluster bootstrap inference developed in 'Roodman et al' (2019, 'STATA' Journal, <doi:10.1177/1536867X19830877>) and 'MacKinnon et al' (2022), which makes it feasible to quickly calculate bootstrap test statistics based on a large number of bootstrap draws even for large samples. Multiple bootstrap types as described in 'MacKinnon, Nielsen & Webb' (2022) are supported. Further, 'multiway' clustering, regression weights, bootstrap weights, fixed effects and 'subcluster' bootstrapping are supported. Further, both restricted ('WCR') and unrestricted ('WCU') bootstrap are supported. Methods are provided for a variety of fitted models, including 'lm()', 'feols()' (from package 'fixest') and 'felm()' (from package 'lfe'). Additionally implements a 'heteroskedasticity-robust' ('HC1') wild bootstrap. Last, the package provides an R binding to 'WildBootTests.jl', which provides additional speed gains and functionality, including the 'WRE' bootstrap for instrumental variable models (based on models of type 'ivreg()' from package 'ivreg') and hypotheses with q > 1.
Maintained by Alexander Fischer. Last updated 2 years ago.
clustered-standard-errorslinear-regression-modelswild-bootstrapwild-cluster-bootstrapopenblascppopenmp
6.9 match 24 stars 6.67 score 109 scripts 2 dependentss3alfisc
wildrwolf:Fast Computation of Romano-Wolf Corrected p-Values for Linear Regression Models
Fast Routines to Compute Romano-Wolf corrected p-Values (Romano and Wolf (2005a) <DOI:10.1198/016214504000000539>, Romano and Wolf (2005b) <DOI:10.1111/j.1468-0262.2005.00615.x>) for objects of type 'fixest' and 'fixest_multi' from the 'fixest' package via a wild (cluster) bootstrap.
Maintained by Alexander Fischer. Last updated 1 years ago.
fixestmultiple-comparisonsromano-wolfwild-bootstrapwild-cluster-bootstrap
11.8 match 7 stars 3.59 score 37 scripts 1 dependents
grantmcdermott
ggfixest:Dedicated 'ggplot2' Methods for 'fixest' Objects
Provides 'ggplot2' equivalents of fixest::coefplot() and fixest::iplot(), for producing nice coefficient plots and interaction plots. Enables some additional functionality and convenience features, including grouped multi-'fixest' object faceting and programmatic updates to existing plots (e.g., themes and aesthetics).
Maintained by Grant McDermott. Last updated 2 months ago.
5.6 match 49 stars 7.01 score 28 scripts
leifeld
texreg:Conversion of R Regression Output to LaTeX or HTML Tables
Converts coefficients, standard errors, significance stars, and goodness-of-fit statistics of statistical models into LaTeX tables or HTML tables/MS Word documents or to nicely formatted screen output for the R console for easy model comparison. A list of several models can be combined in a single table. The output is highly customizable. New model types can be easily implemented. Details can be found in Leifeld (2013), JStatSoft <doi:10.18637/jss.v055.i08>.)
Maintained by Philip Leifeld. Last updated 2 months ago.
html-tableslatexlatex-tablesregressionreportingtabletexreg
1.9 match 113 stars 14.09 score 1.8k scripts 67 dependents
michaeltopper1
panelsummary:Create Publication-Ready Regression Tables with Panels
Create an automated regression table that is well-suited for models that are estimated with multiple dependent variables. 'panelsummary' extends 'modelsummary' (Arel-Bundock, V. (2022) <doi:10.18637/jss.v103.i01>) by allowing regression tables to be split into multiple sections with a simple function call. Utilize familiar arguments such as fmt, estimate, statistic, vcov, conf_level, stars, coef_map, coef_omit, coef_rename, gof_map, and gof_omit from 'modelsummary' to clean the table, and additionally, add a row for the mean of the dependent variable without external manipulation.
Maintained by Michael Topper. Last updated 2 years ago.
4.8 match 2 stars 5.39 score 81 scriptsgrantmcdermott
lfe2fixest:Converts `lfe::felm()` To The `fixest::feols()` Equivalents
What the package does (one paragraph).
Maintained by Grant McDermott. Last updated 3 years ago.
4.6 match 5 stars 3.40 score 1 scripts
kylebutts
did2s:Two-Stage Difference-in-Differences Following Gardner (2021)
Estimates Two-way Fixed Effects difference-in-differences/event-study models using the approach proposed by Gardner (2021) <doi:10.48550/arXiv.2207.05943>. To avoid the problems caused by OLS estimation of the Two-way Fixed Effects model, this function first estimates the fixed effects and covariates using untreated observations and then in a second stage, estimates the treatment effects.
Maintained by Kyle Butts. Last updated 9 hours ago.
1.7 match 97 stars 7.89 score 134 scripts
moritzpschwarz
getspanel:General-to-Specific Modelling of Panel Data
Uses several types of indicator saturation and automated General-to-Specific (GETS) modelling from the 'gets' package and applies it to panel data. This allows the detection of structural breaks in panel data, operationalising a reverse causal approach of causal inference, see Pretis and Schwarz (2022) <doi:10.2139/ssrn.4022745>.
Maintained by Moritz Schwarz. Last updated 12 months ago.
1.8 match 10 stars 5.38 score 24 scripts
repboxr
repboxReg:Repbox module for analysing regressions
Repbox module for analysing regressions
Maintained by Sebastian Kranz. Last updated 30 days ago.
1.9 match 3.71 score 6 scripts 2 dependentskylebutts
fwlplot:Scatter Plot After Residualizing Using 'fixest' Package
Creates a scatter plot after residualizing using a set of covariates. The residuals are calculated using the 'fixest' package which allows very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell theorem is given in Basu (2023) <doi:10.48550/arXiv.2307.00369>.
Maintained by Kyle Butts. Last updated 9 months ago.
3.4 match 1.70 scores3alfisc
wildwyoung:Westfall-Young adjusted p-values for objects linear models via a wild bootstrap
Implements Westfall-Young corrected p-values for objects of type 'fixest' and 'fixest_multi' via a wild (cluster) bootstrap.
Maintained by Alexander Fischer. Last updated 2 years ago.
0.5 match 1.70 score 5 scripts
cran
effClust:Calculate Effective Number of Clusters for a Linear Model
Calculates the (approximate) effective number of clusters for a regression model, as described in Carter, Schnepel, and Steigerwald (2017) <doi:10.1162/REST_a_00639>. The effective number of clusters is a statistic to assess the reliability of asymptotic inference when sampling or treatment assignment is clustered. Methods are implemented for stats::lm(), plm::plm(), and fixest::feols(). There is also a formula method.
Maintained by Joe Ritter. Last updated 1 years ago.
0.5 match 1.00 score