Derived from the work of Kruschke (2015,

The purpose of * bfw* is to establish a framework for conducting
Bayesian analysis in R, using
MCMC and
JAGS (Plummer, 2003). The framework
provides several modules to conduct linear and non-linear (hierarchical)
analyses, and allows the use of custom functions and complex JAGS
models.

Derived from the excellent work of Kruschke (2015), the goal of the
framework is to easily estimate parameter values and the stability of
estimates from the *highest density interval* (HDI), make null value
assessment through *region of practical equivalence testing* (ROPE) and
conduct convergence diagnostics (e.g., Gelman & Rubin, 1992).

Users are encouraged to apply justified priors by modifying existing
JAGS models found in `extdata/models`

or by adding custom models.
Similarly, one might modify models to conduct posterior predictive
checks (see Kruschke, 2013). The purpose of the framework is not to
provide generic modules suitable for all circumstances, but rather act
as a platform for modifying or developing models for a given project.

- Bernoulli trials
- Covariate estimations (including correlation and Cronbach's alpha)
- Fit observed and latent data (e.g., SEM, CFA, mediation models)
- Bayesian equivalent of Cohen's kappa
- Mean and standard deviation estimations
- Predict metric values (cf., ANOVA)
- Predict nominal values (cf., chi-square test)
- Simple, multiple and hierarchical regression
- Softmax regression (i.e., multinomial logistic regression)

- Plot density of parameter values (including ROPE)
- Plot mean data (including repeated measures)
- Plot nominal data (e.g., expected and observed values)
- Plot circlize data (e.g., multiple response categories)

- JAGS (>=4.3.0): http://mcmc-jags.sourceforge.net/
- Java JDK (>=1.4): https://www.java.com/en/download/manual.jsp

Dependencies are automatically installed from CRAN. By default, outdated dependencies are automatically upgraded.

You can install * bfw* from GitHub. If you already have a previous
version of

`bfw`

```
devtools::install_github("oeysan/bfw")
```

Please note that stable versions are hosted at CRAN, whereas GitHub versions are in active development.

```
utils::install.packages("bfw")
```

Please report any bugs/issues here

Compute mean and standard deviation estimates.

Please see manual for more examples.

```
# Apply MASS to create normal distributed data with mean = 0 and standard deviation = 1
set.seed(99)
data <- data.frame(y =
MASS::mvrnorm(n=100,
mu=0,
Sigma=matrix(1, 1),
empirical=TRUE) )
# Run normal distribution analysis on data
## Use 50 000 iterations, set seed for replication.
### Null value assessment is set at ROPE = -0.5 - 0.5 for mean
mcmc <- bfw(project.data = data,
y = "y",
saved.steps = 50000,
jags.model = "mean",
jags.seed = 100,
ROPE = c(-0.5,0.5),
silent = TRUE)
# Run t-distribution analysis on data
mcmc.robust <- bfw(project.data = data,
y = "y",
saved.steps = 50000,
jags.model = "mean",
jags.seed = 101,
ROPE = c(-0.5,0.5),
run.robust = TRUE,
silent = TRUE)
# Use psych to describe the normally distributed data
psych::describe(data)[,c(2:12)]
#> n mean sd median trimmed mad min max range skew kurtosis
#> X1 100 0 1 0.17 0.04 0.81 -3.26 2.19 5.45 -0.57 0.53
# Print summary of normal distribution analysis
## Only the most relevant information is shown here
round(mcmc$summary.MCMC[,c(3:6,9:12)],3)
#> Mode ESS HDIlo HDIhi ROPElo ROPEhi ROPEin n
#> mu[1]: Y -0.003 49830 -0.201 0.196 0 0 100 100
#> sigma[1]: Y 0.995 48890 0.869 1.150 0 100 0 100
# Print summary of t-distribution analysis
round(mcmc.robust$summary.MCMC[,c(3:6,9:12)],3)
#> Mode ESS HDIlo HDIhi ROPElo ROPEhi ROPEin n
#> mu[1]: Y 0.027 25887 -0.167 0.229 0 0 100 100
#> sigma[1]: Y 0.933 10275 0.749 1.115 0 100 0 100
```

```
# Add 10 outliers, each with a value of 10.
biased.data <- rbind(data,data.frame(y = rep(10,10)))
# Run normal distribution analyis on biased data
biased.mcmc <- bfw(project.data = biased.data,
y = "y",
saved.steps = 50000,
jags.model = "mean",
jags.seed = 102,
ROPE = c(-0.5,0.5),
silent = TRUE)
# Run t-distribution analysis on biased data
biased.mcmc.robust <- bfw(project.data = biased.data,
y = "y",
saved.steps = 50000,
jags.model = "mean",
jags.seed = 103,
ROPE = c(-0.5,0.5),
run.robust =TRUE,
silent = TRUE)
# Use psych to describe the biased data
psych::describe(biased.data)[,c(2:12)]
#> n mean sd median trimmed mad min max range skew kurtosis
#> X1 110 0.91 3.04 0.25 0.21 0.92 -3.26 10 13.3 2.3 4.38
# Print summary of normal distribution analysis on biased data
## Only the most relevant information is shown here
round(biased.mcmc$summary.MCMC[,c(3:6,9:12)],3)
#> Mode ESS HDIlo HDIhi ROPElo ROPEhi ROPEin n
#> mu[1]: Y 0.922 50000 0.346 1.50 0 92 8.05 110
#> sigma[1]: Y 2.995 49069 2.662 3.48 0 100 0.00 110
# # Print summary of t-distribution analysis on biased data
round(biased.mcmc.robust$summary.MCMC[,c(3:6,9:12)],3)
#> Mode ESS HDIlo HDIhi ROPElo ROPEhi ROPEin n
#> mu[1]: Y 0.168 29405 -0.015 0.355 0 0.008 99.992 110
#> sigma[1]: Y 0.679 17597 0.512 0.903 0 99.128 0.872 110
```

Shamelessly adapted from here (credits to James Curran).

```
# Create a function for left-censored data
custom.function <- function(DF, ...) {
x <- as.vector(unlist(DF))
x[x < log(29)] = NA
n <- length(x)
LOD <- rep(log(29), n)
is.above.LOD <- ifelse(!is.na(x), 1, 0)
lambda <- 0.5
initial.x = rep(NA, length(x))
n.missing <- sum(!is.above.LOD)
initial.x[!is.above.LOD] <- runif(n.missing, 0, log(29))
initial.list <- list(lambda = lambda,
x = initial.x
)
data.list <- list(n = n,
x = x,
LOD = LOD,
is.above.LOD = is.above.LOD
)
# Return data list
return (
list (
params = "lambda",
initial.list = initial.list,
data.list = data.list,
n.data = as.matrix(n)
)
)
}
# Create a model
custom.model = "
model{
for(i in 1:n){
is.above.LOD[i] ~ dinterval(x[i], LOD[i])
x[i] ~ dexp(lambda)
}
lambda ~ dgamma(0.001, 0.001)
}
"
# Simulate some data
set.seed(35202)
project.data <- as.matrix(rexp(10000, rate = 1.05))
# Run analysis
custom.mcmc <- bfw(project.data = project.data,
custom.function = custom.function,
custom.model = custom.model,
saved.steps = 50000,
jags.seed = 100,
ROPE = c(1.01,1.05),
silent = TRUE)
# Print analysis
round(custom.mcmc$summary.MCMC[,c(3,5,6,9:12)],3)
#> Mode HDIlo HDIhi ROPElo ROPEhi ROPEin n
#> 1.03 1.00 1.06 7.64 14.33 78.03 10000.00
```

```
# Running time for normal distribution analyis
biased.mcmc$run.time[2] - biased.mcmc$run.time[1]
#> Time difference of 7.66 secs
# Running time for t-distribution analysis
biased.mcmc.robust$run.time[2] - biased.mcmc.robust$run.time[1]
#> Time difference of 32.9 secs
```

This project is licensed under the MIT License - see LICENSE.md for details

- John Kruschke for his overall amazing work, and especially for his workshop at ICPSR 2016. It opened my eyes to Bayesian statistics.
- Martyn Plummer for his work on JAGS. Cheers!

- Gelman, A., & Rubin, D. B. (1992). Inference from Iterative
Simulation Using Multiple Sequences.
*Statistical Science*,*7*(4), 457-472. https://doi.org/10.1214/ss/1177011136 - Kruschke, J. K. (2013). Posterior predictive checks can and should
be Bayesian: Comment on Gelman and Shalizi, 'Philosophy and the
practice of Bayesian statistics'.
*British Journal of Mathematical and Statistical Psychology*,*66*(1), 4556. https://doi.org/10.1111/j.2044-8317.2012.02063.x - Kruschke, J. K. (2015).
*Doing Bayesian data analysis: a tutorial with R, JAGS, and Stan*. Academic Press: Boston. - Plummer, M. (2003). JAGS A program for analysis of Bayesian graphical models using Gibbs sampling (Version 4.3.0). Retrieved from http://mcmc-jags.sourceforge.net/

- Fixed an sorting error in
`contrasts`

function

- Added the ability to use custom save names
- Removed the appended "Plot" in save names for
`ParsePlot`

function

- Fixed factor-to-numeric-bug
`metric`

function

- Added
`InverseHDI`

function to compute inverse cumulative density function of the distribution

- Added option to add results to
`ETA`

function

- Added
`PlotParam`

function to plot density of parameter values (including ROPE)

- Removed
`PlotData`

function. All plots are now called from seperate functions:`PlotCirclize`

to create a circlize plot`PlotMean`

to create a mean plot`PlotNominal`

to create a nominal plot`PlotParam`

to create a density plot with parameter values

- Small fix in
`SumMCMC`

function to compute sample sizes

- Updated
`CFA`

function to include correlation matrix - Added a option to run
`PPP`

for every kth length of MCMC chains (Default is every 10th)

- Optimized
`RunContrasts`

to allow larger MCMC simulations (2nd review)

- Fixed
`plot_data`

vignette - Updated
`README`

- Fixed title bug in
`circlize`

plots - Added
`RemoveGarbage`

function to clear up working memory - Added
`MultiGrep`

function to use multiple patterns to select an element from a vector - Fixed bug in
`kappa`

function - Fixed bug in
`covariate`

function - Fixed inline comment bug in
`TidyCode`

function - Added option to define which parameters to use for diagnostics
- Removed (some of the...) unnecessary arguments in
`bfw`

function - Added a
`apa`

PowerPoint template

- Added
`nominal`

and`circlize`

(using the`circlize`

package) plot types`mean`

plots are now seperated from main`PlotData`

function`ParsePlot`

and`PlotData`

functions are also seperated

- Fixed an error in
`nominal`

function

- Fixed variables namnes in
`nominal`

function when using only 1 variable

- Fixed issue of line break after HTML tags when rendering Rmd files.

- Fixed
`ParsePlot`

to accommodate ggplots.`PlotData`

now returns`ggplot2`

and not`grDevices::recordPlot()`

- Added a second badge, a more informative badge, 'cos why not.

- Some typos

- Added a badge because all the cool kids have them
- Fixed small inconsistencies in ParsePlot parameters (i.e., type png with layout pw).
- Defaults are now rasterized pdf on a4 layout with 12 pointsize.

- Optimized
`RunContrasts`

to allow larger MCMC simulations

- Optimized
`ParsePlot`

to handle large amounts of plots - Added
`png`

package to`Suggests`

to handle rasterized graphics for pdf/ps. - Created a small function called
`TidyCode`

to format messy code. - Modified
`nominal`

and`metric`

functions to use a single flexible jags model for each function respectively - Added an extended
`covariate function`

to include y and x variables. - Redefined fit
`data models`

- Created a separate model for confirmatory factor analysis using Wishart distribution
- Redefined structural equation models, making the models more efficient

- Added options to have more control over JAGS simulations
`jags.method`

, specify method for JAGS (e.g., parallel or simple)`jags.chains`

, specify specify number of chains for JAGS`adapt.steps`

, the number of adaptive iterations to use at the start of each simulation`burnin.steps`

, the number of burnin iterations, NOT including the adaptive iterations to use for the simulation.

- Added a small
`ETA`

function to display running time of functions - Made feedback from MCMC functions more informative
- Fixed
`custom model`

in`settings`

- Fixed
`job.title`

bug in covariate - Fixed NULL values in
`TrimSplit`

and`CapWords`

- Replaced robust estimates of R^2 with Spearman
- Moved vignettes from
`vignettes`

to`doc`

- Migrated from the orphaned
`ReporteRs`

to`officer`

(thanks to Professor Brian Ripley at University of Oxford for notifying me)- Added two PowerPoint templates
`legacy`

(4:3) and`widescreen`

(16:9)

- Added two PowerPoint templates
- The following packages are moved from
`Imports`

to`Suggests`

- ggplot2
- lavaan
- officer
- psych
- robust
- rvg
- scales
- truncnorm

- Modified title of package from
`Computational Modelling`

to`Computational Modeling`

to conform with US spelling - Recoded diagnostics, making the code more efficent.
- Correcting some typos and code aesthetics (e.g., replaced print with cat to display running time of analyses)
- Reviewed
`TrimSplit`

function to include removing empty elements from vector - Removed
`methods`

and`rJava`

from imports

- Initial launch with the following modules:
- Bernoulli trials
- Covariate estimations (including correlation and Cronbach`s alpha)
- Fit data (e.g., SEM, CFA, mediation models)
- Bayesian equivalent of Cohen`s kappa
- Mean and standard deviation estimations
- Predict metric values (cf., ANOVA)
- Predict nominal values (c.f., chi-square test)
- Simple, multiple and hierarchical regression
- Softmax regression (i.e., multinomial logistic regression)

- Added a
`NEWS.md`

file to track changes to the package. - Added a
`TODO.md`

file to track future work on the package. - Added a
`README.md`

file as an introduction to the package.