SqueakR 1.3.0

version 1.3.0 [GitHub release] [CRAN release]

Added animal field for each added experiment data, which updates experiment metadata for animals field. Updated compatibility to match semi-automatic and automatic SqueakR pipelines, as well as describe_experiment() function. Updated documentation to reflect the change
Added Plotly sunburst plot (to functions list and dashboard) to graph proportions of collected data done per experimenter, as well as data collected per animal per group
Added Plotly 3D surface plot (plotSurface()) using 2D kernel density estimation to represent call density against principal frequency and call length
Added functions for unblinding experimenters to data
Added function — plotContours() — which renders plotSurface() as a 2D rendering, flanked by histograms displaying distributions of principal frequency and call length
Standardized some function naming (plotSurface instead of plotCallDataSurface, etc)

Used report package for analyses: added functions for reporting descriptive statistics for grouped data in SqueakR Dashboard tab
Deprecated SqueakR Dashboard repository on GitHub and merged functions into SqueakR
Added 3D cluster plot visualization function plotClusters() against call length (z), principal frequency (x) and mean power (y) using plotly, available in base SqueakR and on dashboard

Merged SqueakR dashboard into SqueakR package! Dashboard can now be run after loading the package by running squeakRDashboard()
Added function for calculating ANOVA between groups
Added ANOVA tab on SqueakR Dashboard for determining statistical significance between call features

Added fully-automatic pipeline function (autosqueakRpipeline()) which references Google Sheet to add data without experimenter input for every file
Renamed experiment_pipeline() to semisqueakRpipeline()
Added function (remove_experiment_data()) to remove data from experiment object
Updated vignette to reflect package changes

Recreated NEWS.md file to track changes to the package.
Added functions for generating experiment and analyzing data between experimental groups.
Added visualizations including ethnograms, histograms, box-plots, and correlation matrices.