G-Computation to Estimate Interpretable Epidemiological Effects
c(
person(given = "Jessica",
family = "Grembi",
role = c("aut", "cre", "cph"),
email = "jess.grembi@gmail.com",
comment = c(ORCID = "0000-0001-6142-4913")),
person("Elizabeth", "Rogawski McQuade", role = "ctb", comment = c(ORCID = "0000-0002-4942-3747")))
boot, dplyr, ggplot2, gridExtra, magrittr, purrr, stats,
rlang, tidyr, tidyselect, tidyverse
Estimates flexible epidemiological effect measures including both differences and ratios using the parametric G-formula developed as an alternative to inverse probability weighting.  It is useful for estimating the impact of interventions in the presence of treatment-confounder-feedback. G-computation was originally described by Robbins (1986) <doi:10.1016/0270-0255(86)90088-6> and has been described in detail by Ahern, Hubbard, and Galea (2009) <doi:10.1093/aje/kwp015>; Snowden, Rose, and Mortimer (2011) <doi:10.1093/aje/kwq472>; and Westreich et al. (2012) <doi:10.1002/sim.5316>.
Jessica Grembi [aut, cre, cph]
(<https://orcid.org/0000-0001-6142-4913>),
Elizabeth Rogawski McQuade [ctb]
(<https://orcid.org/0000-0002-4942-3747>)

boot, dplyr, ggplot2, gridExtra, magrittr, MASS, purrr, rlang,
stats, tidyr, tidyselect
knitr, rmarkdown, testthat, tidyverse, printr

boot, dplyr, ggplot2, ggpubr, magrittr, MASS, methods, purrr,
rlang, stats, tidyr, tidyselect
knitr, rmarkdown, testthat, tidyverse, printr, stringr,
formatR, sandwich

Fast design of risk-parity portfolios for financial investment.
The goal of the risk-parity portfolio formulation is to equalize or distribute
the risk contributions of the different assets, which is missing if we simply
consider the overall volatility of the portfolio as in the mean-variance
Markowitz portfolio. In addition to the vanilla formulation, where the risk
contributions are perfectly equalized subject to no shortselling and budget
constraints, many other formulations are considered that allow for box
constraints and shortselling, as well as the inclusion of additional
objectives like the expected return and overall variance. See vignette for
a detailed documentation and comparison, with several illustrative examples.
The package is based on the papers:
Y. Feng, and D. P. Palomar, "SCRIP: Successive Convex Optimization Methods
for Risk Parity Portfolio Design," IEEE Trans. on Signal Processing, vol. 63,
no. 19, pp. 5285-5300, Oct. 2015. <doi:10.1109/TSP.2015.2452219>.
F. Spinu, "An Algorithm for Computing Risk Parity Weights," 2013.
Available at SSRN: <https://ssrn.com/abstract=2297383>
or <doi:10.2139/ssrn.2297383>.
T. Griveau-Billion, J. Richard, and T. Roncalli, "A fast algorithm for computing
High-dimensional risk parity portfolios," 2013.
ArXiv preprint: <arXiv:1311.4057>.
c(
person("Ze", "Vinicius", role = "aut", email = "jvmirca@gmail.com"),
person(c("Daniel", "P."), "Palomar", role = c("cre", "aut"), email = "daniel.p.palomar@gmail.com")
)
bookdown, cccp, FinCovRegularization, knitr, microbenchmark,
numDeriv, patrick, prettydoc, RiskPortfolios, rmarkdown, R.rsp,
sparseIndexTracking, testthat, xts
bookdown, cccp, FinCovRegularization, knitr,
microbenchmark, prettydoc, RiskPortfolios, rmarkdown, R.rsp,
sparseIndexTracking, xts
Ze Vinicius [aut],
Daniel P. Palomar [cre, aut]