Review changes to the app over time:

- v2.3.1 2022/05/16
- Fixed bug in MSA when appraisers are identified by number
- Fixed bug that was not checking all out of control conditions on long-term individual charts
- Fixed bug that was not saving the updated version of a plot if that plot had been saved before
- Added feature in MSA to use whatever identifier is used for appraiser, number or text, as a factor name. Jack and Jill now have names on the charts!
- Gave the long-term MSA plots of parts larger vertical dimensions
- Sped up MSA plot generation when changing inputs and when first loading into MSA tab
- Known Issues:
- ANOVA approach only does 6 sigma calculations

- v2.3 2022/04/28
- Added measurement system analysis for continuous data
- Can select three different types of analysis
- Potential study, a.k.a. GRR - a short study to see if your measurement system has even a hope of being useful
- Short-term study - A more detailed study that measures more parts and gives a better estimate of gauge variability
- Long-term study - An ongoing study of a measurement system that is in use to ensure it remains stable and trusted through time

- Each study can be conducted using one of three ways to calculate the measurement error
- The range (good for subsets of less than 8)
- The standard deviation (good for subsets of 8 or more)
- ANOVA (gives more information at the expense of robustness)

- You can select the AIAG standard of 5.15 measurement errors or the statistical standard of 6 measurement errors (known bug - for now ANOVA only calculates the 6 sigma estimates)
- If true values are known for samples, bias and linearity can be analyzed
- UNIQUE to ROIstat - if the process being measured is known, estimates of misclassification (out of spec called in and in spec called out) can be used as part of your decision around acceptability.
- Calculates repeatability, reproducibility, and total gauge error
- ANOVA also calculates the part x appraiser interaction
- Automatically generates all of the suitable charts and graphs to help in your analysis:
- For long-term charts, each part is tracked on an individuals charts with out-of-control conditions marked. Charts are generated for each appraiser.

For long-term charts, the parts average and standard deviation charts are generated, with correct moving range limits. Charts are generated for each appraiser. - Within part dispersion is shown on a range chart. Charts are generated for each appraiser.
- The bias and linearity analysis plots the magnitude vs the bias and tests for significance for the overall bias as well as linearity. If linearity is significant, a linear equation is generated to correct readings to zero bias on average. The user can select to jitter the data or draw density-based "violins" to aid in reading the chart. Charts are generated for each appraiser.
- Uniformity of dispersion plots the magnitude versus the dispersion metric selected and tests for significance. Lines and regressions are generated for each appraiser.
- Individual readings per part are plotted along with the average per part. This chart can be normalized so that all the averages are equal (normalized) and the user can add jitter and violins to aid in interpretation. Charts are generated for each appraiser.
- Boxplots for each part for each appraiser are plotted. These can also be normalized.
- A graph of the components of variation shows the contribution of repeatability and reproducibility to the overall measurement error variance. For ANOVA models the reproducibility is further broken down into Appraiser and the Appraiser x Part interaction.
- A plot that is unique to ROIstat shows an estimate of the underlying process variation compared to the specifications, with a gradient around the specs indicating the "danger zone" related to misclassifying conforming product and nonconforming and nonconforming product as conforming. It also calculates the number of discrete categories the measurement system can measure within the process variability and the discrimination ratio.
- Should the user wish to investigate the measurement system in depth, additional tests are displayed showing tests for mean and dispersion within and between appraisers.
- All charts can be downloaded in a variety of publication-ready formats"
- eps
- ps
- tex
- jpeg
- tiff
- png
- bmp
- svg
- wmf

- For long-term charts, each part is tracked on an individuals charts with out-of-control conditions marked. Charts are generated for each appraiser.

- v2.0 2022/02/18
- In order to fix an irrelevant console error in one of the packages, load a fresh RStudio instance and type:

install.packages("devtools")- require(devtools)
- remotes::install_github("dreamRs/shinyWidgets")

- Fixed error with oneway ANOVA post-hoc that could cause test to fail if factors were defined as factors
- Fixed unlikely but possible error in random-effects ANOVA (if the between sample MS is less than the within-sample MS the sample to sample variance and ICC would be negative)
- Fixed a bug that would break parts of the EDA section, the Oneway ANOVA, and scatterplots if the column titles were not syntactically valid (e.g. had a space)
- Fixed a bug in calculating the natural tolerance for exponential distributions
- Added the ability to download scatterplots in a variety of publication-ready formats
- Added the ability to hover over a point on a scatterplot to get its x and y location
- Moved the scatterplot equation model to a title on the graph so that it downloads with the graph. This is the actual model used in the non-linear regression and plotted, not the transformed data model used in the correlation analysis below it
- Added option in EDA Natural Tolerance for exponential with 0 low
- Added SPC charts
- Added SPC for variables data
- Data can be column or row format
- A column can be used to designate "sets" of data that should be analyzed together. Sets do not have to be contiguous.
- You can select either 2 or 3 standard errors for charts, with the default being 3
- You can select X-bar charts and use various limit calculations and use the mean or median as a centerline. The limits below are similar across the variables charts:
- Average Range - Good at robustly estimating the underlying process
- Median Range - Good at robustly estimating the underlying process if the range has some unexplained out of control points
- Average Standard Deviation Within Samples - Good if you are really sure that your process is in control
- Median Standard Deviation Within Samples - Good if you are really sure that your process is in control but a few samples are unusually variable
- Average Variance - Good if you are really sure that your process is in control and you are a statistician
- Average Moving Range of the X-bars - Good for when the within-sample variability does not predict the between-sample variability. For example, stratified processes or setup-dependent processes.
- Median Moving Range of the X-bars - Good for when the within-sample variability does not predict the between-sample variability but there are unexplained jumps in the means. For example, stratified processes or setup-dependent processes.
- Standard Deviation of the X-bars - Good for when the within-sample variability does not predict the between-sample variability and you are really sure that the between-sample variability is in control.
- Known s - Good for when you have a lot of data and evidence that the process produces normally distributed data that is in control and you know the true standard deviation. Mostly present to show why it is a bad idea to do this in a real process.
- Custom limits - Good for when you have known limits from previous data. Also how to enter limits from distribution fits.

- Range with various limit and variance estimate calculations and use the mean or median as a centerline.
- Standard deviation with various limit and variance estimate calculations and use the mean or median as a centerline.
- Variance with various limit and variance estimate calculations and use the mean or median as a centerline.
- Selecting a method for the dispersion calculation will automatically suggest matching centerlines and matching limit calculations. These defaults can be changed.
- All variables charts can generate a brief analysis that shows how the limits were calculated, what the total standard deviation of all the individuals is, what the standard deviation of individuals within each set is, and what the estimated standard deviation is for the process based on the dispersion method selected. The correlation between the location and dispersion chart is tested for significance. If you enter specifications, you will also get capability measures (Cp, Cpk, Cpm) based on the estimated standard deviation, process performance measures (Pp, Ppk, Ppm) based on the observed standard deviation, and predicted and actual parts non-conforming. For means, a oneway random-effects analysis is also performed to see if there are additional sources of variation present between samples beyond the within variability. For individuals, autocorrelation is tested.

- Added SPC charts for attributes data. Data where each observation is a row.
- p charts. The limits below are similar across the attributes charts.
- Exact binomial for p and np charts (exact Poisson for c and u charts)
- Normal approximation
- Average moving range
- Median moving range
- Standard deviation
- Custom limits

- np charts
- c charts
- u charts

- p charts. The limits below are similar across the attributes charts.

- Added SPC for variables data
- All SPC charts can be downloaded in a variety of publication-ready formats
- All SPC chart data can be displayed should you wish to save it, including the results of the various out of control rules
- All SPC charts allow you to get information about each point by hovering the mouse near the point
- All SPC charts can select from various out-of-control rules including zone rules. The run rule can be 8 or 9 points. Mean and dispersion charts can use different rule sets.
- All SPC charts can display control zones
- All SPC charts can be modified by showing or hiding
- Lines connecting points
- Control limits
- Centerlines
- Labels indicating the type of control violation for a point

- In order to fix an irrelevant console error in one of the packages, load a fresh RStudio instance and type:

- v1.2 2021/12/08
- In order to fix a bug with single-column data not working with dynamic filtering, load a fresh RStudio instance and type:
- remotes::install_github("dreamRs/datamods")

- Fixed a bug with Mean and Dispersion Tests, Enter Statistics where it would not allow the user to choose between the Welch and Fisher t-tests
- Fixed a bug with Mean and Dispersion Tests, Use Data, data in columns where it would not allow the user to choose between the Welch and Fisher t-tests
- Also added some additional confidence intervals for the latter

- Added control chart limit calculations using various statistics for
- X-bar
- X
- Range
- Standard deviation
- Variance
- p (exact binomial and approximate)
- np (exact binomial and approximate)
- c (exact Poisson and approximate)
- u (exact Poisson and approximate)

- In order to fix a bug with single-column data not working with dynamic filtering, load a fresh RStudio instance and type:

- v1.1.1 2021/11/17
- Fixed a bug in oneway post-hocs that broke the output if there were multiple columns beyond the factor and data. The new code should be robust to this.

- v1.1 2021/11/12
- Added ability to draw one or two tails on binomial, Poisson, and exponential and get the area inside or outside of those tails
- Added some color to oneway ANOVA post-hoc graphs
- Added note on oneway ANOVA post-hoc graphs for fixed and Kruskal-Wallis to indicate that the points are means and medians respectively
- Added exact one-sample Poisson sample size and power calculations
- Added approximate two-sample Poisson sample size and power calculations
- Changed bandwidth on violin plots for ANOVA to type SJ to match EDA density plots
- Cleaned up the output tables for distributions, added an option to show or hide the table for Poisson and Binomial

- v1.0 2021/11/02
- First full public release
- Added skewness and kurtosis in EDA descriptive statistics
- Fixed a bug in the Natural Tolerance calculations when using data
- Reworked UI in mean and dispersion tests to be more consistent with other UI elements in the app
- Fixed a bug that caused some people to experience "argument is missing with no default" error
- Fixed a bug with oneway random effects plot not using the selected color palette and non-rounded data
- Added the ability to either auto select normality tests or manually select them
- When doing a two-sample independent t-test, added the ability to select Welch (recommended), Fisher, or auto select based on variance test
- Added McNemar's Test of Change to Two-Sample Dependent tests
- Added Cohen's kappa to Correlation and Association - Enter Statistics for a quick way to get κ

- v0.85 2021/10/28
- Added post-hoc analysis for fixed effect ANOVA
- Plot of levels with points for the means, connecting lines, violin or boxplot
- Tukey's HSD results table along with detailed results for each test

- Added post-hoc analysis for random effect ANOVA
- Plot of the random effect magnitude compared to the population so you can see what might be possible by reducing the random effect
- Includes a confidence interval for the interclass-correlation coefficient

- Added post-hoc analysis for Kruskal-Wallis rank order ANOVA
- Plot of levels with points for the means, connecting lines, violin or boxplot
- All-pairwise comparisons using the Mann-Whitney U test, error controlled by Bonferroni-Dunn
- Each U test p-value is multiplied by the number of tests (with a maximum of 1), so compare the p-value as reported to the desired family-wise alpha

- Various bug fixes

- Added post-hoc analysis for fixed effect ANOVA

- v0.84 2021/10/22
- Added a oneway ANOVA for fixed and random effects including dispersion test and correct importance calculations
- Added Kruskal-Wallace oneway ANOVA by ranks
- Fixed bug in sample sizes that led to incorrect calculations when "less than" was selected
- That fix cascaded multiple bugs that led to incorrect beta calculations and labeling when "less than" was selected, fixed

- Fixed minor formatting issue with sample size output
- Fixed a bug with reference type data in nonparametrics that resulted in an error "object x2 not found"

- v0.83 2021/10/13
- Fixed bug in sample size calculations - now allows you to test for the alternative being equal, lesser, or greater than the null
- Added confidence intervals and natural tolerances for column and factor data
- Added many selectable descriptive statistics for column and factor data
- Added common and custom quantiles for column and factor data
- Added scatterplots with various regression models, including confidence intervals and significance test
- Added new requirement - ggplot2
- Added new requirement - propagate (forked)
- Need to update
- lolcat
- datamods

- v0.82 2021/09/28
- Added the ability to choose different color palettes. These palettes have similar luminance and are to one degree or another colorblind-safe
- Added histograms, frequency polygons, and density plots for column and reference data with normal curves and spec limit options. Column data can be overlayed on the same axis.
- Added boxplots for column and reference data
- Adjusted CSS for output tables to make them more compact
- Added variable name for normality tests for factor type data
- Various minor bugfixes
- Fixed bug that broke the UI for factors used in the two-way independent t-test where there were more than two categories for the factor
- KNOWN ISSUE
- If the imported data has NA for one of the vectors and you try to use that column and one without NAs in the normality tests, you will get an error.

- WORKAROUND
- You can assess the normality of the data with NAs by itself

- v0.81 2021/09/09
- Added the ability to use one or more columns as factors for normality testing
- Added the two-sample independent z-test for data via calculations
- Normality test p-values are now highlighted in grey if p > α and in yellow if p < α
- Added the ability to choose a factor and data column for:
- Normality testing
- Independent two-sample mean and dispersion tests
- Independent two-sample nonparametric tests

- Various minor bugfixes
- Fixed "data labels don't change if updated and then new data loaded" bug
- Fixed the "correlations don't show until one with a crosstab is selected"

- v0.71 (unreleased)
- Moved "one sample test for variance" checkbox for the data and non-data mean and dispersion tabs

- v0.7 2021/08/25
- First public alpha release