Provides generalized linear models for panel data, including pooled, fixed-effects, and random-effects estimators for continuous, binary, count, ordinal, positive (gamma), and bounded (beta) outcomes, with the fixed-effects ordered logit of Baetschmann, Staub and Winkelmann (2015) doi:10.1111/rssa.12090 and beta regression (Ferrari and Cribari-Neto 2004) doi:10.1080/0266476042000214501 . Also offers correlated random effects (Mundlak 1978) doi:10.2307/1913646 , dynamic models with the Wooldridge (2005) doi:10.1002/jae.770 initial conditions solution, observation weights, average partial effects, regression-spline curves and linearity tests, difference-in-differences and event studies, and two-part models for costs (Duan et al. 1983) doi:10.1080/07350015.1983.10509330 , including correlated random intercepts (Van Ourti 2004) doi:10.1002/hec.920 . Estimators and tests follow standard panel-data references, including Hausman (1978) doi:10.2307/1913827 , Chamberlain (1980) doi:10.2307/2297110 , Allison and Waterman (2002) doi:10.1111/1467-9531.00117 , and Croissant and Millo (2008) doi:10.18637/jss.v027.i02 . Core numerical routines are implemented using 'Rcpp', 'RcppArmadillo', and 'RcppParallel'.
Threads
The within and random-effects estimators for Gaussian, binomial and
Poisson models compute per-group quantities in parallel with
RcppParallel. By default they use all available cores. Set
options(panglm.threads = k) to use at most k threads; the
environment variable RCPP_PARALLEL_NUM_THREADS is also honoured. When
OMP_THREAD_LIMIT is set, it caps the number of threads, and under
R CMD check (when _R_CHECK_LIMIT_CORES_ is set) at most 2 threads
are used. Matrix algebra done in R goes through the BLAS library R is
linked to, whose threads are controlled outside panglm (for example with
OMP_NUM_THREADS, OPENBLAS_NUM_THREADS or MKL_NUM_THREADS).
Author
Maintainer: Imad El Badisy elbadisyimad@gmail.com