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ui.R
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ui.R
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library(shiny)
prismDependencies <- tags$head(
tags$script(src = "https://cdnjs.cloudflare.com/ajax/libs/prism/1.8.4/prism.min.js"),
tags$link(rel = "stylesheet", type = "text/css",
href = "https://cdnjs.cloudflare.com/ajax/libs/prism/1.8.4/themes/prism.min.css")
)
prismLanguageDependencies <- function(languages) {
lapply(languages, function(x) {
tags$head(
tags$script(
src = paste0("https://cdnjs.cloudflare.com/ajax/libs/prism/1.8.4/components/prism-",
x, ".min.js")
)
)
})
}
# Define UI for random distribution application
shinyUI(fluidPage(
# Application title
titlePanel("Distribution explorer"),
prismDependencies,
prismLanguageDependencies(c("r", "python", "latex",
"matlab", "mathematica", "cpp",
"julia")),
# Sidebar with controls to select the random distribution type
# and number of observations to generate. Note the use of the
# br() element to introduce extra vertical spacing
sidebarLayout(
sidebarPanel(
selectInput("distType", "Category of distribution",
c("Continuous Univariate"="Continuous",
"Discrete Univariate"="Discrete",
"Multivariate"="Multivariate"),
selected="Continuous"),
conditionalPanel("input.distType=='Continuous'",
selectInput("dist", "Distribution type:",
c("Normal" = "Normal",
"Uniform" = "Uniform",
"Log-Normal" = "LogNormal",
"Exponential" = "Exponential",
"Gamma" = "Gamma",
"Student-t" = "t",
"Beta"="Beta",
"Cauchy"="Cauchy",
"Half-Cauchy"="HalfCauchy",
"Inverse-Gamma"="InverseGamma",
"Inverse-Chi-Squared"="InverseChiSquared",
"Logit-Normal"="LogitNormal"))),
conditionalPanel("input.distType=='Discrete'",
selectInput("dist1", "Distribution type:",
c("Bernoulli" = "Bernoulli",
"Binomial" = "Binomial",
"Poisson" = "Poisson",
"Negative-Binomial" = "NegativeBinomial",
"Beta-Binomial" = "BetaBinomial"))),
conditionalPanel("input.distType=='Multivariate'",
selectInput("dist2", "Distribution type:",
c("Multivariate-Normal" = "MultivariateNormal",
"Multivariate-Student-t" = "MultivariateT",
"Multinomial"="Multinomial",
"Wishart"="Wishart",
"Inverse-Wishart"="InverseWishart",
"LKJ"="LKJ",
"Dirichlet"="Dirichlet",
"Multinomial"="Multinomial",
"Categorical"="Categorical"))),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist!='Beta'",
sliderInput("n", "Range", value = 10,min = 0, max = 1000)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='Normal'",
sliderInput("mu", "Mean", min=-30, max=30, value=0, step=0.2),
sliderInput("sigma", "Standard deviation", min=0.1, max=20, value=1, step=0.2)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='Uniform'",
sliderInput("a", "Lower bound", min=-30, max=0, value=0, step=0.2),
sliderInput("b", "Upper bound", min=0.1, max=30, value=1, step=0.2)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='LogNormal'",
sliderInput("meanlog", "Mean of log", min=-10, max=10, value=0, step=0.2),
sliderInput("sdlog", "Standard deviation of log", min=0.1, max=20, value=1, step=0.2)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='Exponential'",
sliderInput("rate", "Rate parameter", min=0, max=1.1, value=0.5)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='Gamma'",
sliderInput("shape", "Shape parameter", min=0.1, max=10, value=1),
sliderInput("rateGam", "Rate parameter", min=0.1, max=2, value=0.5)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='t'",
sliderInput("muT", "mode", min=-30, max=30, value=0, step=0.2),
sliderInput("sigmaT", "sigma parameter", min=0.1, max=10, value=1, step=0.2),
sliderInput("nuT", "degrees of freedom", min=0, max=20, value=3, step=0.2)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='Beta'",
sliderInput("alpha", "Shape parameter 1", min=0.5, max=10, value=1),
sliderInput("beta", "Shape parameter 2", min=0.5, max=10, value=1)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='Cauchy'",
sliderInput("locationC", "location parameter", min=-30, max=30, value=0, step=0.2),
sliderInput("scaleC", "scale parameter", min=0.5, max=10, value=1, step=0.2)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='HalfCauchy'",
sliderInput("locationHC", "location parameter", min=-30, max=30, value=0, step=0.2),
sliderInput("scaleHC", "scale parameter", min=0.5, max=10, value=1, step=0.2)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='InverseGamma'",
sliderInput("shapeIG", "shape parameter", min=0.5, max=10, value=2),
sliderInput("scaleIG", "scale parameter", min=0.5, max=10, value=1)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='InverseChiSquared'",
sliderInput("dfIC", "degrees of freedom", min=0.5, max=10, value=3)),
conditionalPanel(condition="input.distType=='Continuous'&&input.dist=='LogitNormal'",
sliderInput("muLogitN", "mu parameter", min=-10, max=10, value=1, step=0.2),
sliderInput("sigmaLogitN", "sigma parameter", min=0.5, max=10, value=1, step=0.2)),
conditionalPanel(condition="input.distType=='Discrete'&&input.dist1=='Bernoulli'",
sliderInput("probBer", "probability", min=0, max=1, value=0.5)),
conditionalPanel(condition="input.distType=='Discrete'&&input.dist1=='Binomial'",
sliderInput("sizeBin", "size", min=0, max=50, value=10),
sliderInput("probBin", "probability", min=0, max=1, value=0.5)),
conditionalPanel(condition="input.distType=='Discrete'&&input.dist1=='Poisson'",
sliderInput("lambdaPois", "Rate", min=0, max=50, value=10, step=0.2),
sliderInput("rangePois", "Range", min=0, max=100, value=40)),
conditionalPanel(condition="input.distType=='Discrete'&&input.dist1=='NegativeBinomial'",
sliderInput("meanNB", "Mean", min=0, max=50, value=10, step=0.2),
sliderInput("dispersionNB", "Dispersion", min=0, max=100, value=3,step=0.2),
sliderInput("rangeNB", "Range", min=0, max=100, value=40)),
conditionalPanel(condition="input.distType=='Discrete'&&input.dist1=='BetaBinomial'",
sliderInput("sizeBetaBin", "Size", min=0, max=50, value=10),
sliderInput("shapeBetaBin1", "Shape parameter 1", min=0, max=50, value=1, step=0.2),
sliderInput("shapeBetaBin2", "Shape parameter 2", min=0, max=50, value=1, step=0.2)),
conditionalPanel(condition="input.distType=='Multivariate'&&input.dist2=='MultivariateNormal'",
sliderInput("meanXN", "Mean of X", min=-10, max=10, value=0, step=0.2),
sliderInput("meanYN", "Mean of y", min=-10, max=10, value=0, step=0.2),
sliderInput("sigmaXN", "Standard deviation of x", min=0, max=5, value=1, step=0.2),
sliderInput("sigmaYN", "Standard deviation of y", min=0, max=5, value=1, step=0.2),
sliderInput("rhoxyN", "Correlation between x and y", min=-1, max=1, value=0, step=0.2),
sliderInput("rangeN", "Range of plot", min=0, max=100, value=10)),
conditionalPanel(condition="input.distType=='Multivariate'&&input.dist2=='MultivariateT'",
sliderInput("meanXT", "Mode of x", min=-10, max=10, value=0, step=0.2),
sliderInput("meanYT", "Mode of y", min=-10, max=10, value=0, step=0.2),
sliderInput("sigmaXT", "Sigma x", min=0, max=5, value=1, step=0.2),
sliderInput("sigmaYT", "Sigma y", min=0, max=5, value=1, step=0.2),
sliderInput("rhoxyT", "Correlation component", min=-1, max=1, value=0,step=0.2),
sliderInput("dfMVT", "Degrees of freedom", min=0, max=50, value=10,step=0.2),
sliderInput("rangeN", "Range of plot", min=0, max=100, value=10)),
conditionalPanel(condition="input.distType=='Multivariate'&&input.dist2=='Wishart'",
sliderInput("dimensionWish", "Dimensions", min=4, max=20, value=4),
sliderInput("dfWish", "Degrees of freedom", min=4, max=100, value=8, step=0.2),
sliderInput("sampleSizeWish", "Sample size", min=1000, max=20000, value=5000)),
conditionalPanel(condition="input.distType=='Multivariate'&&input.dist2=='InverseWishart'",
sliderInput("dimensionInWish", "Dimensions", min=4, max=20, value=4),
sliderInput("dfInvWish", "Degrees of freedom", min=4, max=100, value=8, step=0.2),
sliderInput("sampleSizeInvWish", "Sample size", min=1000, max=20000, value=5000)),
conditionalPanel(condition="input.distType=='Multivariate'&&input.dist2=='Dirichlet'",
sliderInput("dimensionsDirichlet","Dimensions",min=2,max=4,value=2,step=1),
sliderInput("sampleSizeDirichlet", "Sample size", min=10, max=20000, value=1000),
sliderInput("alphaDirichlet1","alpha 1",min=0.1,max=10,value=2),
sliderInput("alphaDirichlet2","alpha 2",min=0.1,max=10,value=2),
conditionalPanel(condition="input.dimensionsDirichlet>'2'",
sliderInput("alphaDirichlet3","alpha 3",min=0.1,max=10,value=2),
conditionalPanel(condition="input.dimensionsDirichlet>'3'",
sliderInput("alphaDirichlet4","alpha 4",min=0.1,max=10,value=2)))),
conditionalPanel(condition="input.distType=='Multivariate'&&input.dist2=='Multinomial'",
sliderInput("angleMultinomial","Viewpoint angle",min=0,max=360,value=100),
sliderInput("sizeMultinomial","size",min=2,max=100,value=6),
sliderInput("probMultinomial1","probability 1",min=0,max=1,value=0.5),
sliderInput("probMultinomial2","probability 2",min=0,max=1,value=0.5),
sliderInput("probMultinomial3","probability 3",min=0,max=1,value=0.5)),
conditionalPanel(condition="input.distType=='Multivariate'&&input.dist2=='LKJ'",
sliderInput("dimensionLKJ", "Dimensions", min=4, max=20, value=4),
sliderInput("etaLKJ", "Degrees of freedom", min=0, max=40, value=1,step=0.2),
sliderInput("sampleSizeLKJ", "Sample size", min=1000, max=20000, value=2000)),
br()
),
# Show a tabset that includes a plot, summary, and table view
# of the generated distribution
mainPanel(
uiOutput('mytabs')
)
)
)
)