the data (see Judd et al., (2018) <doi:10.1016/j.palaeo.2017.09.034>). This modeling is carried out along a sliding window through the data and yields estimates of
the day of the year (Julian Day) and local growth rate for each data point. Uncertainties in both modeling
Packaged: 2021-07-05 12:28:05 UTC; Convernaum

Title: Exactly Computing the Tail of the Poisson-Binomial Distribution
person("Andrew", "Lee", role = c("aut", "cre"), email = "andrewl@maths.usyd.edu.au"),
person("Noah", "Peres", role = c("aut", "ctb"), email = "noahp@maths.usyd.edu.au"),
person("Uri", "Keich", role = c("aut", "ctb"), email = "uri.keich@sydney.edu.au"),
person("Alexander", "Mukhin", role = c("cph"), email = "alexander.i.mukhin@gmail.com"))
Description: An exact method for computing the Poisson-Binomial Distribution (PBD). The package provides a function for generating a random sample from the PBD, as well as two distinct approaches for computing the density, distribution, and quantile functions of the PBD. The first method uses direct-convolution, or a dynamic-programming approach which is numerically stable but can be slow for a large input due to its quadratic complexity. The second method is much faster on large inputs thanks to its use of Fast Fourier Transform (FFT) based convolutions. Notably in this case the package uses an exponential shift to practically guarantee the relative accuracy of the computation of an arbitrarily small tail of the PBD -- something that FFT-based methods often struggle with. This ShiftConvolvePoiBin method is described in Peres, Lee and Keich (2020) <arXiv:2004.07429> where it is also shown to be competitive with the fastest implementations for exactly computing the entire Poisson-Binomial distribution.
Packaged: 2020-04-29 05:45:33 UTC; andrewlee

Title: Inference in Regressions with Shift-Share Structure
Description: Provides confidence intervals in least-squares regressions when the
variable of interest has a shift-share structure, and in instrumental
variables regressions when the instrument has a shift-share structure. The
confidence intervals implement the AKM and AKM0 methods developed in Adão,
Kolesár, and Morales (2019) <doi:10.1093/qje/qjz025>.
Suggests: testthat (>= 2.1.0), knitr, rmarkdown, AER, spelling, formatR
Author: Michal Kolesár [aut, cre] (<https://orcid.org/0000-0002-2482-7796>),