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chkiefer
lavacreg:Latent Variable Count Regression Models
Estimation of a multi-group count regression models (i.e., Poisson, negative binomial) with latent covariates. This packages provides two extensions compared to ordinary count regression models based on a generalized linear model: First, measurement models for the predictors can be specified allowing to account for measurement error. Second, the count regression can be simultaneously estimated in multiple groups with stochastic group weights. The marginal maximum likelihood estimation is described in Kiefer & Mayer (2020) <doi:10.1080/00273171.2020.1751027>.
Maintained by Christoph Kiefer. Last updated 24 days ago.
count-regressionlatent-covariatesstructural-equation-modelingopenblascppopenmp
31.9 match 3 stars 4.78 score 5 scripts
blunde1
agtboost:Adaptive and Automatic Gradient Boosting Computations
Fast and automatic gradient tree boosting designed to avoid manual tuning and cross-validation by utilizing an information theoretic approach. This makes the algorithm adaptive to the dataset at hand; it is completely automatic, and with minimal worries of overfitting. Consequently, the speed-ups relative to state-of-the-art implementations can be in the thousands while mathematical and technical knowledge required on the user are minimized.
Maintained by Berent Ånund Strømnes Lunde. Last updated 3 years ago.
3.6 match 1.72 score 52 scripts