### Install SimplyAgree R Package from GitHub

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/index.html

This snippet shows how to install the latest version of the SimplyAgree R package directly from GitHub using the devtools package. Ensure you have devtools installed.

```R
devtools::install_github("arcaldwell49/SimplyAgree")
```

--------------------------------

### Install SimplyAgree R Package

Source: https://github.com/arcaldwell49/simplyagree/blob/master/README.md

Installs the latest version of the SimplyAgree R package directly from GitHub using the devtools package. This is the primary method for obtaining the most up-to-date functionality.

```r
devtools::install_github("arcaldwell49/SimplyAgree")
```

--------------------------------

### Install SimplyAgree Development Version from GitHub

Source: https://github.com/arcaldwell49/simplyagree/blob/master/paper/paper.md

Installs the developmental version of the SimplyAgree R package directly from GitHub using the devtools package. This allows users to access the latest features and bug fixes before they are released on CRAN.

```R
devtools::install_github("arcaldwell49/SimplyAgree")
```

--------------------------------

### Load SimplyAgree and Data

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/agreement_analysis.html

Loads the SimplyAgree package and the example 'temps' dataset for agreement analysis.

```R
library(SimplyAgree)
data(temps)
```

--------------------------------

### Install SimplyAgree from CRAN

Source: https://github.com/arcaldwell49/simplyagree/blob/master/paper/paper.md

Installs the SimplyAgree R package from the Comprehensive R Archive Network (CRAN). This is the standard method for obtaining the latest stable release of the package.

```R
install.packages("SimplyAgree")
```

--------------------------------

### Install SimplyAgree R Package from GitHub

Source: https://github.com/arcaldwell49/simplyagree/blob/master/paper/ARCHIVE/Untitled.md

This code snippet demonstrates how to install the developmental version of the SimplyAgree R package from GitHub using the devtools package. This allows users to access the latest features and bug fixes before they are released on CRAN.

```R
devtools::install_github("arcaldwell49/SimplyAgree")
```

--------------------------------

### Install SimplyAgree R Package from CRAN

Source: https://github.com/arcaldwell49/simplyagree/blob/master/paper/ARCHIVE/Untitled.md

This code snippet shows how to install the SimplyAgree R package directly from the Comprehensive R Archive Network (CRAN). This is the standard method for obtaining the latest stable release of the package.

```R
install.packages("SimplyAgree")
```

--------------------------------

### R: Example Usage of agree_nest

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/agree_nest.html

This example demonstrates how to use the `agree_nest` function with sample data. It shows the function call with specified arguments for measurements, subject ID, data, and the agreement threshold. The output includes a warning about deprecation and the results of the agreement analysis.

```R
data('reps')
agree_nest(x = "x", y = "y", id = "id", data = reps, delta = 2)
```

--------------------------------

### R: Example Usage of agree_reps

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/agree_reps.html

This example demonstrates how to use the `agree_reps` function with sample data. It specifies the measurement columns, the ID column, the data frame, and the delta value. The output includes a warning about the function being superseded and provides Bland-Altman limits of agreement, hypothesis test decisions, and CCC results.

```R
data('reps')
agree_reps(x = "x", y = "y", id = "id", data = reps, delta = 2)
```

--------------------------------

### Example Usage of agree_test

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/agree_test.html

This example demonstrates how to use the `agree_test` function with sample data. It shows the expected output, including a warning about the function being superseded, results from the Shieh test, Bland-Altman limits of agreement, and the concordance correlation coefficient.

```R
data('reps')
agree_test(x=reps$x, y=reps$y, delta = 2)
#> Warning: `agree_test()` was deprecated in SimplyAgree 0.2.0.
#> ℹ Please use `agreement_limit()` instead.
#> Limit of Agreement = 95%
#> 
#> ###- Shieh Results -###
#> Exact 90% C.I.  [-2.6418, 3.5184]
#> Hypothesis Test: don't reject h0
#> 
#> ###- Bland-Altman Limits of Agreement (LoA) -###
#>           Estimate Lower CI Upper CI CI Level
#> Bias        0.4383  -0.1669    1.044     0.95
#> Lower LoA  -1.9470  -2.8162   -1.078     0.90
#> Upper LoA   2.8237   1.9545    3.693     0.90
#> 
#> ###- Concordance Correlation Coefficient (CCC) -###
#> CCC: 0.4791, 95% C.I. [0.1276, 0.7237]
```

--------------------------------

### Improve Jamovi Functions and Documentation in SimplyAgree

Source: https://github.com/arcaldwell49/simplyagree/blob/master/NEWS.md

Version 0.0.2 of SimplyAgree focuses on improving the jamovi/jmv functions by fixing typos and adding more descriptive error messages. It also removes `dontrun` tags from examples in the documentation and enhances the detail and references provided for the package's functions.

```R
# Jamovi/jmv function improvements
# Enhanced error messages
# Updated documentation with more details and references
```

--------------------------------

### Example: Replicated data agreement limit calculation

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/agreement_limit.html

Shows an example of using the `agreement_limit` function with replicated data. This requires specifying the 'id' column and setting 'data_type' to 'rep'.

```R
agreement_limit(x = "x", y ="y", data = reps, id = "id", data_type = "rep")
#> MOVER Limits of Agreement (LoA)
```

--------------------------------

### Example Usage of agree_np

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/agree_np.html

This example demonstrates how to use the `agree_np` function with sample data. It specifies the measurement columns, the ID column, the data frame, and the delta threshold. The output includes a warning about prior weights, agreement proportions, hypothesis test results, and quantile limits of agreement.

```R
data('reps')
agree_np(x = "x", y = "y", id = "id", data = reps, delta = 2)
```

--------------------------------

### Example: Simple data agreement limit calculation

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/agreement_limit.html

Demonstrates how to use the `agreement_limit` function with simple, independent data points. It calculates the MOVER Limits of Agreement and displays the bias, confidence intervals, and LoA.

```R
data('reps')

# Simple
agreement_limit(x = "x", y = "y", data = reps)
#> MOVER Limits of Agreement (LoA)
#> 95% LoA @ 5% Alpha-Level
#> Independent Data Points
#> 
#>    Bias           Bias CI Lower LoA Upper LoA            LoA CI
#>  0.4383 [-0.1669, 1.0436]    -1.947     2.824 [-3.0117, 3.8884]
#> 
#> SD of Differences = 1.217
```

--------------------------------

### GNU GPL Copyright Notice

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/LICENSE.html

Standard copyright notice and licensing terms for free software distributed under the GNU General Public License (GPL). Includes copyright line, distribution terms, warranty disclaimer, and license link.

```text
Copyright (C) <year>  <name of author>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
```

--------------------------------

### Load SimplyAgree and Data

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/reanalysis.html

Loads the SimplyAgree package along with necessary data manipulation and plotting libraries. It also loads the 'temps' dataset for analysis.

```R
library(SimplyAgree)
#library(tidyverse)
library(dplyr)
library(tidyr)
library(ggplot2)
library(magrittr)
data("temps")
df_temps = temps
```

--------------------------------

### SimplyAgree Package Documentation

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/SimplyAgree-package.html

This snippet provides the core documentation for the SimplyAgree R package, outlining its purpose in simplifying agreement and reliability analyses. It highlights the package's functions for various agreement tests and reliability assessments, as well as its utility in study planning.

```R
#' Reliability and agreement analyses often have limited software support. Therefore, this package was created to make agreement and reliability analyses easier for the average researcher. The functions within this package include simple tests of agreement, agreement analysis for nested and replicate data, and provide robust analyses of reliability. In addition, this package contains a set of functions to help when planning studies looking to assess measurement agreement.
```

--------------------------------

### jamovi Module for Agreement and Reliability Analysis

Source: https://github.com/arcaldwell49/simplyagree/blob/master/paper/paper.md

The SimplyAgree jamovi module offers a graphical user interface (GUI) for performing agreement and reliability analyses. This module makes advanced statistical methods accessible to researchers without programming experience, complementing the R package by providing an easy-to-use platform within the jamovi statistical software.

```jamovi
# To use the SimplyAgree module in jamovi:
# 1. Open jamovi.
# 2. Go to the 'Modules' tab.
# 3. Search for 'SimplyAgree' and install it.
# 4. Once installed, the SimplyAgree analyses will be available under the 'Analyses' menu.
```

--------------------------------

### Run Simple Agreement Test with SimplyAgree

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/intro_vignette.html

Demonstrates how to use the `agree_test` function from the SimplyAgree package to analyze simple agreement between two measurements. It includes setting agreement levels and printing the results, which encompass Shieh's test, Bland-Altman limits of agreement, and Lin's Concordance Correlation Coefficient.

```R
library(SimplyAgree)
a1 = agree_test(x = reps$x,
                y = reps$y,
                agree.level = .8)
print(a1)
```

--------------------------------

### Visualize Large Datasets

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/agreement_analysis.html

Demonstrates how to modify plots for large datasets by changing the `geom` argument in plotting functions. This example generates sample data and sets up a data frame for visualization.

```R
set.seed(81346)
x = rnorm(750, 100, 10)
diff = rnorm(750, 0, 1)
y = x + diff

df = data.frame(x = x,

```

--------------------------------

### Simple Deming Regression with SimplyAgree

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/Deming.html

Demonstrates how to perform a simple Deming Regression using the `dem_reg` function from the SimplyAgree package. It includes creating sample data, specifying measurement error ratio, fitting the model, and printing the results.

```R
library(SimplyAgree)

dat = data.frame(
  x = c(7, 8.3, 10.5, 9, 5.1, 8.2, 10.2, 10.3, 7.1, 5.9),
  y = c(7.9, 8.2, 9.6, 9, 6.5, 7.3, 10.2, 10.6, 6.3, 5.2)
)

dem1 = dem_reg(
               x = "x",
               y = "y",
               data = dat,
               error.ratio = 4,
               weighted = FALSE
)
dem1
```

--------------------------------

### Adjust for Proportional Bias

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/agree_tests.html

This example shows how to adjust agreement analysis for proportional bias using the prop_bias argument in the agree_test function. It also includes printing the results and plotting the adjusted agreement.

```R
a1 = agree_test(x = reps$x,
                y = reps$y,
                prop_bias = TRUE,
                agree.level = .8)
    #> prop_bias set to TRUE. Hypothesis test may be bogus. Check plots.
    print(a1)
    #> Limit of Agreement = 80%
    #> 
    #> ###- Shieh Results -###
    #> Exact 90% C.I.  [-1.2551, 2.1318]
    #> Hypothesis Test: No Hypothesis Test
    #> Warning: hypothesis test likely bogus with proportional bias.
    #> ###- Bland-Altman Limits of Agreement (LoA) -###
    #>           Estimate Lower CI Upper CI CI Level
    #> Bias        0.4383 -0.08968   0.9663     0.95
    #> Lower LoA  -0.9159 -1.51049  -0.3213     0.90
    #> Upper LoA   1.7926  1.19801   2.3872     0.90
    #> 
    #> LoA at average of both measures. Please check plot.
    #> ###- Concordance Correlation Coefficient (CCC) -###
    #> CCC: 0.4791, 95% C.I. [0.1276, 0.7237]
    
    plot(a1)
```

--------------------------------

### Non-parametric Analysis with Proportional Bias

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/agree_tests.html

This example shows how to perform a non-parametric agreement analysis with proportional bias enabled using the agree_np function. It highlights how changing the prop_bias argument affects the LoA results.

```R
a1 = agree_np(x = "x",
                  y = "y",
                  data = reps,
                  delta = 2,
                  prop_bias = TRUE,
                  agree.level = .8)
    #> Warning in matrix(sample(n, mofn * R, replace = TRUE), mofn, R): non-empty data
    #> for zero-extent matrix
    
    #> Warning in matrix(sample(n, mofn * R, replace = TRUE), mofn, R): non-empty data
    #> for zero-extent matrix
    
    #> Warning in matrix(sample(n, mofn * R, replace = TRUE), mofn, R): non-empty data
    #> for zero-extent matrix
    
    #> Warning in matrix(sample(n, mofn * R, replace = TRUE), mofn, R): non-empty data
    #> for zero-extent matrix
```

--------------------------------

### Calculate Agreement and Tolerance Limits

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/agreement_analysis.html

Demonstrates the basic usage of agreement_limit and tolerance_limit functions to calculate agreement and tolerance limits for a dataset. It involves passing data and specifying the x and y variables.

```R
test_agree = agreement_limit(x = "x",
                                 y = "y",
                                 data = reps)

check(test_agree)
```

```R
test_tol = tolerance_limit(x = "x",
                               y = "y",
                               data = reps)

check(test_tol)
```

--------------------------------

### Fix SEP/SEE Calculations and Add Vignette in SimplyAgree

Source: https://github.com/arcaldwell49/simplyagree/blob/master/NEWS.md

SimplyAgree version 0.1.4 addresses a major error in the calculation of SEP (Standard Error of Prediction) and SEE (Standard Error of Estimate). The calculation for the total standard deviation has been adjusted. For detailed information and examples, users are directed to the 'reliability_analysis' vignette.

```R
vignette("reliability_analysis", package = "SimplyAgree")
```

--------------------------------

### View reps dataset (R)

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/Data.html

This snippet displays the first few rows of the 'reps' dataset, allowing users to inspect the actual data values for 'id', 'x', and 'y' measurements.

```R
head(reps)
```

--------------------------------

### Calculate Agreement Limit for Nested Data

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/agreement_analysis.html

Illustrates the use of the `agreement_limit` function for nested data, applicable when underlying values within subjects are expected to vary across different conditions or time points. This example specifies the measurement columns, subject ID, data frame, data type as 'nest', the preferred calculation method ('mover'), and the agreement level.

```R
a3 = agreement_limit(x = "x",
                    y = "y",
                    id = "id",
                    data = reps,
                    data_type = "nest",
                    loa_calc = "mover",
                    agree.level = .95)
a3
```

--------------------------------

### View reps dataset structure (R)

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/Data.html

This snippet shows how to view the structure of the 'reps' dataset. It displays the number of rows and variables, along with the names and types of each variable, providing insight into the data's organization.

```R
str(reps)
```

--------------------------------

### Print simple_reli object

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/simple_reli-methods.html

The print method for simple_reli objects provides a concise summary of the Limits of Agreement. It is part of the SimplyAgree R package.

```R
print(x, ...)
```

--------------------------------

### Calculate reliability statistics using reli_stats in R

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/reli_stats.html

This example demonstrates how to use the `reli_stats` function to calculate reliability statistics, including Coefficient of Variation (CV), Standard Error of Measurement (SEM), Standard Error of the Estimate (SEE), and Standard Error of Prediction (SEP). It also calculates Intraclass Correlation Coefficients with confidence intervals.

```R
data('reps')
reli_stats(data = reps, wide = TRUE, col.names = c("x","y"))
#> Only 2 items in data. It is recommended to use reli_aov instead of reli_stats.
#> 
#> Coefficient of Variation (%):  15.8
#> Standard Error of Measurement (SEM):  0.863
#> Standard Error of the Estimate (SEE):  0.655
#> Standard Error of Prediction (SEP):  1.11
#> 
#> Intraclass Correlation Coefficients with  95 % C.I.
#>            Model         Measures  Type    ICC Lower CI Upper CI
#> 1 one-way random        Agreement  ICC1 0.4963   0.1632   0.7299
#> 2 two-way random        Agreement  ICC2 0.5097   0.1911   0.7355
#> 3  two-way fixed      Consistency  ICC3 0.5384   0.2117   0.7569
#> 4 one-way random   Avg. Agreement ICC1k 0.6634   0.2806   0.8438
#> 5 two-way random   Avg. Agreement ICC2k 0.6753   0.3209   0.8476
#> 6  two-way fixed Avg. Consistency ICC3k 0.7000   0.3495   0.8616
```

--------------------------------

### Example: Plotting and Finding Sample Size for Power Curve

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/blandPowerCurve.html

Demonstrates how to use the `blandPowerCurve` function to generate a power curve and then use `find_n` to determine the sample size required to achieve a specific power level. This involves calling `blandPowerCurve` with sample sizes, mean, standard deviation, delta, confidence level, and agreement level, then plotting the result and finding the necessary N for a target power.

```R
# Plot the power curve
plot(powerCurve, type = 1)

# Find at what N power of .8 is achieved
find_n(powerCurve, power = .8)
```

--------------------------------

### Plot Power Curve in R

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/intro_vignette.html

This snippet demonstrates how to plot a power curve using the 'power_res' object in R. This visualization helps in understanding the relationship between agreement levels (delta, agree.level, conf.level) and statistical power.

```r
plot(power_res)
```

--------------------------------

### Load SimplyAgree Package

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/agree_tests.html

Loads the SimplyAgree R package to utilize its functions for agreement analysis.

```R
library(SimplyAgree)
```

--------------------------------

### Load reps dataset (R)

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/Data.html

This snippet demonstrates how to load the 'reps' dataset, which contains measurements from an agreement study. The dataset is structured as a data frame with subject identifiers and measurements.

```R
data(reps)
```

--------------------------------

### Load ba1986 dataset (R)

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/Data.html

This snippet demonstrates how to load the 'ba1986' dataset, which contains measurements from the original Bland & Altman paper. The dataset includes subject identifiers and multiple measurements from different devices.

```R
data(ba1986)
```

--------------------------------

### Calculate Tolerance Limits

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/agreement_analysis.html

Demonstrates how to calculate tolerance limits using the `tolerance_limit` function. It specifies the data, measurement columns, subject ID, condition, and correlation structure. The result is then visualized with a maximal allowable difference.

```R
res1 = tolerance_limit(
  data = temps,
  x = "trec_pre", # First measure
  y = "teso_pre", # Second measure
  id = "id", # Subject ID
  condition = "tod", # Identify condition that may affect differences
  cor_type = "sym" # Set correlation structure as Compound Symmetry
)
plot(res1, delta = .25) # Set maximal allowable difference to .25 units
```

--------------------------------

### Methods for Simple Agreement Objects

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/index.html

Provides generic methods (print, plot, check) for objects of class 'simple_agree', which are the output of various agreement testing functions. These methods facilitate the interpretation and visualization of agreement test results.

```R
print(_<simple_agree>_)
```

```R
plot(_<simple_agree>_)
```

```R
check()
```

--------------------------------

### Find Sample Size for Desired Power Level in R

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/intro_vignette.html

This code uses the `find_n` method to determine the sample size required to achieve a desired power level based on the results of the `blandPowerCurve` function. It takes the `powerCurve` object and the desired power level as input and returns the corresponding sample size.

```R
find_n(power_res, power = .8)
```

--------------------------------

### Methods for Simple Reliability Objects

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/index.html

Provides generic methods (print, plot, check) for objects of class 'simple_reli', which represent results from simple reliability analyses. These methods aid in the interpretation and visualization of reliability findings.

```R
print(_<simple_reli>_)
```

```R
plot(_<simple_reli>_)
```

```R
check(_<simple_reli>_)
```

--------------------------------

### Methods for LOA Objects

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/index.html

Provides generic methods (print, plot, check) for objects of class 'loa', which are generated by agreement analysis functions. These methods allow for easy inspection and visualization of agreement results.

```R
print(_<loa>_)
```

```R
plot(_<loa>_)
```

```R
check(_<loa>_)
```

--------------------------------

### Create Wide Format Data Frame in R

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/intro_vignette.html

This code creates a data frame `dat` in a wide format from a matrix `sf`. The matrix is populated with sample data, and column and row names are assigned. This data frame is then used as input for the `reli_stats` function.

```R
sf <- matrix(
  c(9,    2,   5,    8,
    6,    1,   3,    2,
    8,    4,   6,    8,
    7,    1,   2,    6,
    10,   5,   6,    9,
    6,   2,   4,    7),
  ncol = 4,
  byrow = TRUE
)

colnames(sf) <- paste("J", 1:4, sep = "")
rownames(sf) <- paste("S", 1:6, sep = "")
#sf  #example from Shrout and Fleiss (1979)
dat = as.data.frame(sf)
```

--------------------------------

### Print Agreement Analysis Results (R)

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/intro_vignette.html

The `print` method for `agree_reps` objects displays the results of the agreement analysis. This includes the Bland-Altman limits of agreement with confidence intervals, and the Concordance Correlation Coefficient (CCC) with its confidence interval. The output specifies whether the data represents replicate measurements and notes any missing hypothesis tests or specific calculation methods used.

```R
print(a2)
```

--------------------------------

### Print simple_agree object

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/simple_agree-methods.html

S3 method for printing a simple_agree object, providing a short summary of the Limits of Agreement.

```R
print(x, ...)
```

--------------------------------

### Print loa_mixed_bs object

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/loa_mixed_bs-methods.html

The print method for loa_mixed_bs objects displays a concise summary of the Limits of Agreement. It takes an object of class 'loa_mixed_bs' as input and can accept additional arguments.

```R
# S3 method for loa_mixed_bs
print(x, ...)
```

--------------------------------

### Methods for Tolerance Delta Objects

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/index.html

Provides generic methods (print, plot, check) for objects of class 'tolerance_delta', which are used to represent differences in tolerance limits. These methods facilitate the analysis and visualization of tolerance-related data.

```R
print(_<tolerance_delta>_)
```

```R
plot(_<tolerance_delta>_)
```

```R
check(_<tolerance_delta>_)
```

--------------------------------

### Load and Inspect Ferritin Data

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/Deming.html

Loads the 'ferritin' dataset from the 'deming' R package and displays the first few rows using the 'head' function. This is a preliminary step to understand the data structure before performing regression analysis.

```R
library(deming)
data('ferritin')

head(ferritin)
```

--------------------------------

### Print Rectal Post-Exercise Tolerance Limits

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/reanalysis.html

Prints the calculated tolerance limits for post-exercise rectal temperature measurements, displaying bias, confidence intervals, prediction intervals, and tolerance limits for different conditions.

```R
print(rec.post_tol)
```

--------------------------------

### Checking Deming Regression Assumptions

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/Deming.html

Illustrates how to check the assumptions of the Deming regression model, such as normality and homogeneity of variance, using the `check` method provided for Deming regression results.

```R
check(dem1)
```

--------------------------------

### Plot Rectal Post-Exercise Tolerance Limits

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/reanalysis.html

Generates a Bland-Altman style plot to visualize the tolerance limits for post-exercise rectal temperature measurements.

```R
plot(rec.post_tol)
```

--------------------------------

### Calculate and Plot Agreement Limits (Bin Plot)

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/agreement_analysis.html

Calculates agreement limits using the `agreement_limit` function and visualizes the results using a 2D bin plot.

```R
plot(a1,
         geom = "geom_bin2d")
```

--------------------------------

### Print loa_mermod object

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/loa_mermod-methods.html

S3 method for printing loa_mermod objects, providing a short summary of the Limits of Agreement.

```R
print(x, ...)
```

--------------------------------

### Visualize Agreement Test Results with SimplyAgree

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/intro_vignette.html

Shows how to visualize the results of an agreement test performed using the `agree_test` function in the SimplyAgree package. It includes generating a Bland-Altman plot (type 1) and a line-of-identity plot (type 2).

```R
plot(a1, type = 1)
```

```R
plot(a1, type = 2)
```

--------------------------------

### Reliability Analysis for jamovi

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/index.html

Offers reliability analysis tools for the jamovi platform. This function allows users to assess the consistency and reliability of measurements within jamovi.

```R
jmvreli()
```

--------------------------------

### Print tolerance_delta object

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/tolerance_delta-methods.html

The print method for tolerance_delta objects displays a short summary of the calculated tolerance limits. It takes the object 'x' and the number of digits to print as arguments.

```R
print(x, digits = 4, ...)
```

--------------------------------

### Perform Agreement Test

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/articles/agree_tests.html

This snippet demonstrates how to perform a basic agreement test using the agree_test function. It takes x and y data, and an agreement level, and returns an object that can be checked and plotted.

```R
a1 = agree_test(x = reps$x,
                y = reps$y,
                agree.level = .8)

check(a1)
```

--------------------------------

### R Package for Agreement and Reliability Analysis

Source: https://github.com/arcaldwell49/simplyagree/blob/master/paper/paper.md

The SimplyAgree R package provides functions to calculate various measures of agreement and reliability for continuous data. It supports advanced methods like those accounting for multiple observations per subject and hypothesis tests of agreement, which are often missing in other statistical software. The package aims to offer a comprehensive and user-friendly solution for researchers.

```R
install.packages("SimplyAgree")
library(SimplyAgree)

# Example usage (assuming you have data in a data frame called 'my_data')
# result <- simply_agree(data = my_data, method = "BlandAltman")
# print(result)
```

--------------------------------

### Print simple_eiv object

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/simple_eiv-methods.html

The print method for simple_eiv objects provides a concise summary of the error-in-variables (e.g., Deming) regression model.

```R
print(x, ...)
```

--------------------------------

### View ba1986 dataset (R)

Source: https://github.com/arcaldwell49/simplyagree/blob/master/docs/reference/Data.html

This snippet displays the first few rows of the 'ba1986' dataset, providing a glimpse of the data related to the Bland & Altman study, including measurements from Wright and Mini devices.

```R
head(ba1986)
```