Description: Fit flexible (excess) hazard regression models with the possibility of including non-proportional effects of covariables and of adding a shared random effect.
Imports: statmod, MASS, numDeriv, splines

Title: Mexican Politics-Inspired Color Palette Generator
Authors@R: person("Alejandro", "Platas-López", role = c("aut", "cre"),
email = "alejandroplatasl@gmail.com", comment = c(ORCID = "0000-0002-2584-343X"))
Description: A color palette generator inspired by Mexican politics, with
colors ranging from red on the left to gray in the middle and green on the
right. Palette options range from only a few colors to several colors, but with
discrete and continuous options to offer greatest flexibility to the user.
This package allows for a range of applications, from mapping brief discrete scales
(e.g., four colors for Morena, PRI, and PAN) to continuous interpolated arrays
including dozens of shades graded from red to green.
Author: Alejandro Platas-López [aut, cre]

Description: Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: volatility forecasting using multiplicative component GARCH models" by Conrad, Kleen (2018, Working Paper). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.
Imports: Rcpp, graphics, stats, numDeriv, zoo
Suggests: testthat, dplyr, ggplot2, covr, rmarkdown

Description: Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2018, Working Paper, <doi:10.2139/ssrn.2752354>). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.