| title | output |
|---|---|
NEWS |
github_document |
Switched to new dependency free, simplermarkdown package!
Updated all datasets on Dec 12th, 2021 using data-raw/data-script.R
Experimenting with minidown for vignettes.
Updated all datasets on July 19th, 2021
Submitted 3.3 to CRAN and received a NOTE concerning documentation files for the GDPCI data set, referencing the Guide to the National Income and Product Accounts of the United States (NIPA). Updated source from http://www.bea.gov/national/pdf/nipaguid.pdf to https://www.bea.gov/resources/methodologies/nipa-handbook
The knitr maintainer decided to remove rmarkdown as a dependency, so the vignette build now fails. It must be added, or else CRAN will remove the package by 2021-05-14.
Updated all data sets
SP500 data includes more variables from Robert Schiller's data set for U.S. Stock Markets 1871-2021.
Update data for FEDFUNDS, GS10, PAYEMS, UNRATENSA, and SP500.
Content edits and cleanup of vignettes.
These included, removing the redundant call to library(xts) as it has been
moved to Depends instead of merely Suggests, as documented in 0.3-0 below.
Thus, calling neverhpfilter includes it.
While the vignette builder uses the knitr package, I was also loading the
knitr package to access the kable function for tables. Testing was going
fine, but then knitr inexplicably began throwing a variety of differing errors
across Linux and Windows builds. This appears to be due to Suggested packages it
couldn't import, so removing calls to knitr in the vignette was an easy place
to begin reducing the area of an unknown attack surface. In the modern era,
regardless of the original error, any opportunity to reduce dependencies seems
the most sensible approach as ever increasing dependency sprawl has bestowed
upon R package maintainers a constant, exponentially growing, attack surface.
The decision to remove knitr::kable from vignettes was also an aesthetic one.
In my experience, tables remain an important device for graphic displays of
information. While knitr's html format appears clean at first, closer inspection
reveals the undesirable trait of fitting tables to full page width regardless of
the number of columns to display. On deeper reflection, I view this as a bug, as
it produces the undesirable side effect of too much white space for the reader's
eye to traverse when comparing numbers across columns.
Printing the raw output of an xts or data.frame objects keeps columns compact,
allowing for clearer visual comparison. The raw output also better communicates
to our reader the table was created as a result of some computational process.
Plus, in an increasingly sophisticated digital world of Ux, these raw outputs
look increasingly, unique, computationally cool, and clean. They serve as a
reminder of the objective and scientific nature we strive for in our endeavors.
Feature, updated data through January 2020.
New vignette Getting started reworks and replaces Additional examples.
Increased R version dependency to (>= 3.5.0) for the .Rdata files.
Moved from testtthat to tinytest, and wrote additional function unit tests
and data unit tests.
Moved xts and zoo from imports to depends. Now xts (>= 0.11-0) and zoo (>= 1.8-0)
Bug fix, see issue-1 here.
Updated data from original to roughly Q2 2019.
Consolidated into two functions. yth_glm remains unchanged, while
yth_filter has been given an output argument to specify the return of specific series. This feature eliminates the need for yth_cycle and yth_trend, which were helpful when applying the function to multiple data sets. Done so at the strong suggestion of CRAN, and has ultimatly proven a good idea.
Additional data sets have been added to replicate most all of Hamilton's table 2.
The "Reproducing Hamilton" vignette has been expanded and content has been edited for clarity.
First complete version. Has four functions yth_glm, yth_filter, yth_cycle, and
yth_trend. Three data sets are included to reproduce part of Hamilton's work.
They are GDPC1, PAYEMS, and Hamilton_table_2. A vignette titled "Reproducing Hamilton" illustrated the work and shows users how to implement functions.