Package: alpaca 0.3.4

Amrei Stammann

alpaca: Fit GLM's with High-Dimensional k-Way Fixed Effects

Provides a routine to partial out factors with many levels during the optimization of the log-likelihood function of the corresponding generalized linear model (glm). The package is based on the algorithm described in Stammann (2018) <arxiv:1707.01815> and is restricted to glm's that are based on maximum likelihood estimation and nonlinear. It also offers an efficient algorithm to recover estimates of the fixed effects in a post-estimation routine and includes robust and multi-way clustered standard errors. Further the package provides analytical bias corrections for binary choice models derived by Fernandez-Val and Weidner (2016) <doi:10.1016/j.jeconom.2015.12.014> and Hinz, Stammann, and Wanner (2020) <arxiv:2004.12655>.

Authors:Amrei Stammann [aut, cre], Daniel Czarnowske [aut]

alpaca_0.3.4.tar.gz
alpaca_0.3.4.tar.gz(r-4.5-noble)alpaca_0.3.4.tar.gz(r-4.4-noble)
alpaca_0.3.4.tgz(r-4.4-emscripten)alpaca_0.3.4.tgz(r-4.3-emscripten)
alpaca.pdf |alpaca.html
alpaca/json (API)
NEWS

# Install 'alpaca' in R:
install.packages('alpaca', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/amrei-stammann/alpaca/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

openblascpp

3.54 score 106 scripts 1.6k downloads 8 exports 5 dependencies

Last updated 2 years agofrom:4fc8079b7c. Checks:OK: 1 NOTE: 1. Indexed: no.

TargetResultDate
Doc / VignettesOKNov 29 2024
R-4.5-linux-x86_64NOTENov 29 2024

Exports:biasCorrfeglmfeglm.controlfeglm.nbfeglmControlgetAPEsgetFEssimGLM

Dependencies:data.tableFormulaMASSRcppRcppArmadillo

Estimating the intensive and extensive margin of trade

Rendered fromtrade.Rmdusingknitr::rmarkdownon Nov 29 2024.

Last update: 2022-08-10
Started: 2020-01-12

How to use alpaca

Rendered fromhowto.Rmdusingknitr::rmarkdownon Nov 29 2024.

Last update: 2022-08-10
Started: 2019-05-14