Merge pull request #257 from hendersontrent/trent-2024

startup message

DESCRIPTION

@@ -2,7 +2,7 @@ Package: theft
 Type: Package
 Title: Tools for Handling Extraction of Features from Time Series
 Version: 0.6.1
-Date: 2024-03-06
+Date: 2024-03-09
 Authors@R: c(
   person("Trent", "Henderson", email = "then6675@uni.sydney.edu.au", role = c("cre", "aut")),
   person("Annie", "Bryant", role = "ctb")

R/zzz.R

@@ -0,0 +1,5 @@
+
+.onAttach <- function(libname, pkgname) {
+  packageStartupMessage("This is version ", packageVersion(pkgname), 
+                        " of ", pkgname, ". All analysis and visualisation functions (plus a host of new ones!) have been moved to the {theftdlc} package\nand subsequently renamed and refactored.\nYou can install {theftdlc} via install.packages('theftdlc').\nPlease see https://hendersontrent.github.io/theftdlc/ for more.")
+}

README.Rmd

@@ -44,18 +44,20 @@ Please also check out our paper [Feature-Based Time-Series Analysis in R using t
 
 Note that `Kats`, `tsfresh` and `TSFEL` are Python packages. `theft` has built-in functionality for helping you install these libraries---all you need to do is install Python 3.9 on your machine. If you wish to access the Python feature sets, please run `?install_python_pkgs` in R after downloading `theft` or consult the vignette in the package for more information. For a comprehensive comparison of these six feature sets across a range of domains (including computation speed, within-set feature composition, and between-set feature correlations), please refer to the paper [An Empirical Evaluation of Time-Series Feature Sets](https://ieeexplore.ieee.org/document/9679937).
 
-Users can also supply their own features to `theft` (see the vignette for more information).
+As of `v0.6.1`, users can also supply their own features to `theft` (see the vignette for more information)!
 
 ## Package extensibility
 
 The companion package [`theftdlc`](https://github.com/hendersontrent/theftdlc) ('`theft` downloadable content'---just like you get [DLCs and expansions](https://en.bandainamcoent.eu/elden-ring/elden-ring/shadow-of-the-erdtree) for video games) contains an extensive suite of functions for analysing, interpreting, and visualising time-series features calculated from `theft`. Collectively, these packages are referred to as the '`theft` ecosystem'.
 
 <img src="man/figures/theft-packages.png" width="400" height="200" alt="Hex stickers of the theft and theftdlc packages for R" />
 
-A high-level overview of how the `theft` ecosystem for R is typically accessed by users is shown below. Many more functions and options for customisation are available within the packages.
+A high-level overview of how the `theft` ecosystem for R is typically accessed by users is shown below. Note that prior to `v0.6.1` of, many of the `theftdlc` functions were contained in `theft` but under other names. To ensure the `theft` ecosystem is as user-friendly as possible and can scale to meet future demands, `theft` has been refactored to be just feature extraction, while `theftdlc` handles all the analysis of the extracted features. The deprecated names---such as `tsfeature_classifier()` being the outdated version of `classify()`---are also still available for now in `theftdlc`.
 
 <img src="man/figures/theft-ecosystem.png" width="900" alt="Schematic of the theft ecosystem in R" />
 
+Many more functions and options for customisation are available within the packages and users are encouraged to explore the vignettes and helper files for more information.
+
 ## Quick tour
 
 `theft` and `theftdlc` combine to create an intuitive and efficient tidy feature-based workflow. Here is an example of a single code chunk that calculates features using [`catch22`](https://github.com/hendersontrent/Rcatch22) and a custom set of mean and standard deviation, and projects the feature space into an interpretable two-dimensional space using principal components analysis:

README.md

@@ -59,8 +59,8 @@ feature composition, and between-set feature correlations), please refer
 to the paper [An Empirical Evaluation of Time-Series Feature
 Sets](https://ieeexplore.ieee.org/document/9679937).
 
-Users can also supply their own features to `theft` (see the vignette
-for more information).
+As of `v0.6.1`, users can also supply their own features to `theft` (see
+the vignette for more information)!
 
 ## Package extensibility
 
@@ -76,11 +76,21 @@ ecosystem’.
 <img src="man/figures/theft-packages.png" width="400" height="200" alt="Hex stickers of the theft and theftdlc packages for R" />
 
 A high-level overview of how the `theft` ecosystem for R is typically
-accessed by users is shown below. Many more functions and options for
-customisation are available within the packages.
+accessed by users is shown below. Note that prior to `v0.6.1` of, many
+of the `theftdlc` functions were contained in `theft` but under other
+names. To ensure the `theft` ecosystem is as user-friendly as possible
+and can scale to meet future demands, `theft` has been refactored to be
+just feature extraction, while `theftdlc` handles all the analysis of
+the extracted features. The deprecated names—such as
+`tsfeature_classifier()` being the outdated version of `classify()`—are
+also still available for now in `theftdlc`.
 
 <img src="man/figures/theft-ecosystem.png" width="900" alt="Schematic of the theft ecosystem in R" />
 
+Many more functions and options for customisation are available within
+the packages and users are encouraged to explore the vignettes and
+helper files for more information.
+
 ## Quick tour
 
 `theft` and `theftdlc` combine to create an intuitive and efficient tidy

docs/pkgdown.yml

@@ -3,7 +3,7 @@ pkgdown: 2.0.2
 pkgdown_sha: ~
 articles:
   theft: theft.html
-last_built: 2024-03-06T11:16Z
+last_built: 2024-03-08T23:30Z
 urls:
   reference: https://hendersontrent.github.io/theft/reference
   article: https://hendersontrent.github.io/theft/articles