15:[["$","meta","0",{"name":"viewport","content":"width=device-width, initial-scale=1"}],["$","meta","1",{"charSet":"utf-8"}],["$","title","2",{"children":"fitbayesJSB() R function from [ForestFit] | R PACKAGES"}],["$","meta","3",{"name":"description","content":"Suppose $x=(x_1,\\dots,x_n)^T$ denotes a vector of $n$ independent observations coming from a four-parameter JSB distribution with probability density function given given by [REMOVE_ME]$$ f\\bigl(x\\big|\\Theta\\bigr) = \\frac {\\delta \\lambda}{\\sqrt{2\\pi}(x-\\xi)(\\lambda+\\xi-x)}\\exp\\Biggl\\{-\\frac{1}{2}\\Bigg[\\gamma+\\delta\\log \\biggl(\\frac{x-\\xi}{\\lambda+\\xi-x}\\biggr) \\Bigg]^2\\Biggr\\}, [REMOVE_ME_2]$$\n\nwhere $\\xi 0$, $-\\infty<\\gamma<\\infty$, and $-\\infty<\\xi<\\infty$. Using the Bayesian approach, we compute the Bayes' estimators of the JSB distribution parameters."}],["$","meta","4",{"property":"og:title","content":"fitbayesJSB() R function from [ForestFit]"}],["$","meta","5",{"property":"og:description","content":"Suppose $x=(x_1,\\dots,x_n)^T$ denotes a vector of $n$ independent observations coming from a four-parameter JSB distribution with probability density function given given by [REMOVE_ME]$$ f\\bigl(x\\big|\\Theta\\bigr) = \\frac {\\delta \\lambda}{\\sqrt{2\\pi}(x-\\xi)(\\lambda+\\xi-x)}\\exp\\Biggl\\{-\\frac{1}{2}\\Bigg[\\gamma+\\delta\\log \\biggl(\\frac{x-\\xi}{\\lambda+\\xi-x}\\biggr) \\Bigg]^2\\Biggr\\}, [REMOVE_ME_2]$$\n\nwhere $\\xi 0$, $-\\infty<\\gamma<\\infty$, and $-\\infty<\\xi<\\infty$. Using the Bayesian approach, we compute the Bayes' estimators of the JSB distribution parameters."}],["$","meta","6",{"property":"og:url","content":"https://r-packages.io/packages/ForestFit/fitbayesJSB"}],["$","meta","7",{"property":"og:site_name","content":"R PACKAGES"}],["$","meta","8",{"property":"og:locale","content":"en_US"}],["$","meta","9",{"property":"og:image:type","content":"image/png"}],["$","meta","10",{"property":"og:image","content":"https://r-packages.io/packages/ForestFit/fitbayesJSB/opengraph-image?6cdc95b0c38aa621"}],["$","meta","11",{"property":"og:image:width","content":"1200"}],["$","meta","12",{"property":"og:image:height","content":"630"}],["$","meta","13",{"property":"og:type","content":"website"}],["$","meta","14",{"name":"twitter:card","content":"summary_large_image"}],["$","meta","15",{"name":"twitter:title","content":"fitbayesJSB() R function from [ForestFit]"}],["$","meta","16",{"name":"twitter:description","content":"Suppose $x=(x_1,\\dots,x_n)^T$ denotes a vector of $n$ independent observations coming from a four-parameter JSB distribution with probability density function given given by [REMOVE_ME]$$ f\\bigl(x\\big|\\Theta\\bigr) = \\frac {\\delta \\lambda}{\\sqrt{2\\pi}(x-\\xi)(\\lambda+\\xi-x)}\\exp\\Biggl\\{-\\frac{1}{2}\\Bigg[\\gamma+\\delta\\log \\biggl(\\frac{x-\\xi}{\\lambda+\\xi-x}\\biggr) \\Bigg]^2\\Biggr\\}, [REMOVE_ME_2]$$\n\nwhere $\\xi 0$, $-\\infty<\\gamma<\\infty$, and $-\\infty<\\xi<\\infty$. Using the Bayesian approach, we compute the Bayes' estimators of the JSB distribution parameters."}],["$","meta","17",{"name":"twitter:image:type","content":"image/png"}],["$","meta","18",{"name":"twitter:image","content":"https://r-packages.io/packages/ForestFit/fitbayesJSB/opengraph-image?6cdc95b0c38aa621"}],["$","meta","19",{"name":"twitter:image:width","content":"1200"}],["$","meta","20",{"name":"twitter:image:height","content":"630"}],["$","link","21",{"rel":"icon","href":"/favicon.ico","type":"image/x-icon","sizes":"16x16"}],["$","meta","22",{"name":"next-size-adjust"}]]

fitbayesJSB function