R-universe search: topic:quantile-regression

Showing 6 of total 6 results (show query)

centerforassessment

SGP:Student Growth Percentiles & Percentile Growth Trajectories

An analytic framework for the calculation of norm- and criterion-referenced academic growth estimates using large scale, longitudinal education assessment data as developed in Betebenner (2009) <doi:10.1111/j.1745-3992.2009.00161.x>.

Maintained by Damian W. Betebenner. Last updated 15 days ago.

percentile-growth-projections quantile-regression sgp sgp-analyses student-growth-percentiles student-growth-projections

20 stars 9.69 score 88 scripts

wwiecek

baggr:Bayesian Aggregate Treatment Effects

Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) <doi:10.1257/app.20170299>.

Maintained by Witold Wiecek. Last updated 9 days ago.

bayesian-statistics meta-analysis quantile-regression stan treatment-effects cpp

49 stars 7.24 score 88 scripts

cy-dev

hqreg:Regularization Paths for Lasso or Elastic-Net Penalized Huber Loss Regression and Quantile Regression

Offers efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss. Reference: Congrui Yi and Jian Huang (2017) <doi:10.1080/10618600.2016.1256816>.

Maintained by Congrui Yi. Last updated 5 months ago.

elastic-net high-dimensional huber-loss-regression lasso machine-learning-algorithms quantile-regression regularization-paths

10 stars 6.31 score 57 scripts 4 dependents

egpivo

QuantRegGLasso:Adaptively Weighted Group Lasso for Semiparametric Quantile Regression Models

Implements an adaptively weighted group Lasso procedure for simultaneous variable selection and structure identification in varying coefficient quantile regression models and additive quantile regression models with ultra-high dimensional covariates. The methodology, grounded in a strong sparsity condition, establishes selection consistency under certain weight conditions. To address the challenge of tuning parameter selection in practice, a BIC-type criterion named high-dimensional information criterion (HDIC) is proposed. The Lasso procedure, guided by HDIC-determined tuning parameters, maintains selection consistency. Theoretical findings are strongly supported by simulation studies. (Toshio Honda, Ching-Kang Ing, Wei-Ying Wu, 2019, <DOI:10.3150/18-BEJ1091>).

Maintained by Wen-Ting Wang. Last updated 5 months ago.

admm group-lasso high-dimensional quantile-regression rcpp rcpparmadillo openblas cpp

4 stars 3.60 score 2 scripts

bayerse

esreg:Joint Quantile and Expected Shortfall Regression

Simultaneous modeling of the quantile and the expected shortfall of a response variable given a set of covariates, see Dimitriadis and Bayer (2019) <doi:10.1214/19-EJS1560>.

Maintained by Sebastian Bayer. Last updated 2 years ago.

expected-shortfall quantile-regression value-at-risk openblas cpp

2 stars 3.52 score 11 scripts 1 dependents

gregorkb

QregBB:Block Bootstrap Methods for Quantile Regression in Time Series

Implements moving-blocks bootstrap and extended tapered-blocks bootstrap, as well as smooth versions of each, for quantile regression in time series. This package accompanies the paper: Gregory, K. B., Lahiri, S. N., & Nordman, D. J. (2018). A smooth block bootstrap for quantile regression with time series. The Annals of Statistics, 46(3), 1138-1166.

Maintained by Karl Gregory. Last updated 3 years ago.

bootstrap quantile-regression time-series

2 stars 3.00 score 1 scripts