Changelog

Source: NEWS.md

envalysis 0.7.0

CRAN release: 2024-03-20

New Features

• predict() method for class ‘calibration’

envalysis 0.6.0

CRAN release: 2023-09-20

New Features

• Inverse predict concentrations from calibration curves using inv_predict()
• as.list() method for class ‘calibration’
• pkgdown documentation

Minor Improvements

• Code coverage
• GitHub Actions for macOS
• Don’t export mselect() fork anymore; use drc::mselect() instead

envalysis 0.5.5

CRAN release: 2023-08-07

Minor Improvements

• The check_assumptions argument in calibration() is now less verbose; test results may be retrieved by calling print()
• Replaced size argument in ggplot2::element_rect() and ggplot2::element_line() with linewidth
• Update SOP for particle size estimations using texture()
• Changed maintainer email address
• Corrected typos

envalysis 0.5.4

CRAN release: 2022-10-07

Bug Fixes

• skip tests for ggplot2 v3.4.0 due to deprecation warnings; replace size argument in ggplot2::element_rect() with linewidth later

envalysis 0.5.3

CRAN release: 2022-09-21

Bug Fixes

• Fixed moved URLs

envalysis 0.5.2

Minor Improvements

• Update GitHub Actions
• Tidy news file

Bug Fixes

• Fix ‘invalid nsmall argument’ error when using signifig() with certain value combinations

envalysis 0.5.1

CRAN release: 2021-03-05

New Features

• Finding optimum weights for weighted calibrations using weight_select()
• Calculating matrix effects (signal suppression/enhancement) with matrix_effect()
• calibration() now checks for model assumptions

Minor Improvements

• Additional “blanks” parameter introduced to calibration(), lod(), and loq()
• Snapshot testing
• Improved and more consistent documentation

envalysis 0.4.2

CRAN release: 2021-02-04

Minor Improvements

• Move to testthat 3rd edition

Bug Fixes

• Fix regression when using weights in calibration()

envalysis 0.4.1

CRAN release: 2021-01-21

New Features

• First preparations for weights support in calibration()

Minor Improvements

• Rename master branch to main

Bug Fixes

• Update testthat::expect_equal() calls to keep compatibility with R 4.1.0

envalysis 0.4.0

Minor Improvements

• texture() now takes data as formula
• tibble support for texture()
• loq() iterates only until significant digits won’t change anymore

Bug Fixes

• Force percentage bounds for texture() to 0 and 100
• Increased margins for theme_publish()

envalysis 0.3.3

CRAN release: 2018-04-27

Minor Improvements

• First CRAN release
• Better package description

envalysis 0.3.2

Bug Fixes

• Reimplementation of drc’s mselect() for texture() to get rid of global variables

envalysis 0.3.1

Minor Improvements

• loq() now uses iterations instead of estimating the value from lod()

Bug Fixes

• Better handling of unbalanced designs in calibration()

Defunct Functions

• make.raw(), use rep() instead ;-)

envalysis 0.3.0

New Features

• Starting with testthat

Minor Improvements

• signifig() supports ‘siunitx’ LaTeX output
• Better data handling in calibration()
• Updated man pages

Bug Fixes

• theme_publish() updated to work with current ggplot2 versions
• signifig() can handle zeros better

Defunct Functions

• puri.test(), use lmer on ranks (lme4) with Type II-ANOVA (car) instead

envalysis 0.2.2

Bug Fixes

• Temporary fix to make mselect() work
• TODO: Get rid of assignment to .GlobalEnv

envalysis 0.2.1

Minor Improvements

• Switch to drc package for texture curve fitting

envalysis 0.2.0

New Features

• texture class for automatic determination of particle size distribution using a hydrometer in accordance with ASTM D422-63(2007)e2

Minor Improvements

• updated theme_publish()
• demo file added

envalysis 0.1.0

Initial Feature Set

• Confidence intervals CI()
• Root mean square errors rmse()
• Limit of detection (LOD) lod()
• Limit of quantification (LOQ) loq()
• Various sorption isotherms sorption()
• Convert frequency data back to raw data make.raw()
• ANOVA on ranks according to Sen and Puri (also known as Scheirer-Ray-Hare-Test) puri.test()
• Categorize water drop penetration times according to Bisdom et al. (1993) bisdom()
• Report significant figures, i.e. round means and erros to the least significant digit, using signifig()
• Clean, black-and-white ggplot2 theme for scientific publications theme_publish()