analyse_baSAR.md
In Luminescence: Comprehensive Luminescence Dating Data Analysis

Full check of analyse_baSAR function

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              "value": ["model {\n            central_D ~  dunif(lower_centralD,upper_centralD)\n\n            sigma_D ~ dunif(0.01, 1 * central_D)\n\n            for (i in 1:Nb_aliquots) {\n              a[i] ~  dnorm(6.5 , 1/(9.2^2) ) T(0, )\n              b[i] ~  dnorm(50 , 1/(1000^2) )  T(0, )\n              c[i] ~  dnorm(1.002 , 1/(0.9^2) ) T(0, )\n              g[i] ~  dnorm(0.5 , 1/(2.5^2) ) I(-a[i], )\n              sigma_f[i]  ~  dexp (20)\n\n              D[i] ~ dnorm ( central_D , 1/(sigma_D^2) )   #   Normal distribution\n\n              S_y[1,i] <-  1/(sLum[1,i]^2 + sigma_f[i]^2)\n              Lum[1,i] ~ dnorm ( Q[1,i] , S_y[1,i])\n              Q[1,i]  <-  GC_Origin * g[i] + LinGC * (c[i] * D[i] ) + ExpoGC * (a[i] * (1 - exp (-D[i] /b[i])) )\n\n\n              for (m in 2:Limited_cycles[i]) {\n                S_y[m,i] <-  1/(sLum[m,i]^2 + sigma_f[i]^2)\n                Lum[m,i] ~ dnorm( Q[m,i] , S_y[m,i] )\n                Q[m,i]  <-  GC_Origin * g[i] + LinGC * (c[i] * Dose[m,i]) + ExpoGC * (a[i] * (1 - exp (-Dose[m,i]/b[i])) )\n              }\n            }\n            }"]
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              "value": ["model {\n            central_D ~  dunif(lower_centralD,upper_centralD)\n\n            log_central_D <-  log(central_D) - 0.5 * l_sigma_D^2\n            l_sigma_D ~ dunif(0.01, 1 * log(central_D))\n            sigma_D <-  sqrt((exp(l_sigma_D^2) -1) * exp( 2*log_central_D + l_sigma_D^2) )\n\n            for (i in 1:Nb_aliquots) {\n              a[i] ~  dnorm(6.5 , 1/(9.2^2) ) T(0, )\n              b[i] ~  dnorm(50 , 1/(1000^2) )  T(0, )\n              c[i] ~  dnorm(1.002 , 1/(0.9^2) ) T(0, )\n              g[i] ~  dnorm(0.5 , 1/(2.5^2) ) I(-a[i], )\n              sigma_f[i]  ~  dexp (20)\n\n              log_D[i] ~ dnorm ( log_central_D , 1/(l_sigma_D^2) )  #   Log-Normal distribution\n              D[i] <-  exp(log_D[i])\n\n              S_y[1,i] <-  1/(sLum[1,i]^2 + sigma_f[i]^2)\n              Lum[1,i] ~ dnorm ( Q[1,i] , S_y[1,i])\n              Q[1,i]  <-  GC_Origin * g[i] + LinGC * (c[i] * D[i] ) + ExpoGC * (a[i] * (1 - exp (-D[i] /b[i])) )\n\n            for (m in 2:Limited_cycles[i]) {\n                S_y[m,i] <-  1/(sLum[m,i]^2 + sigma_f[i]^2)\n                Lum[m,i] ~ dnorm( Q[m,i] , S_y[m,i] )\n                Q[m,i]  <-  GC_Origin * g[i] + LinGC * (c[i] * Dose[m,i]) + ExpoGC * (a[i] * (1 - exp (-Dose[m,i]/b[i])) )\n              }\n            }\n        }"]
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              "value": ["cauchy", "normal", "log_normal", "user_defined"]
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              "value": ["model {\n\n            central_D ~  dunif(lower_centralD,upper_centralD)\n\n            precision_D ~ dt(0, pow(0.16*central_D, -2), 1)T(0, )\n            sigma_D <-  1/sqrt(precision_D)\n\n            for (i in 1:Nb_aliquots) {\n              a[i] ~  dnorm(6.5 , 1/(9.2^2) ) T(0, )\n              b[i] ~  dnorm(50 , 1/(1000^2) )  T(0, )\n              c[i] ~  dnorm(1.002 , 1/(0.9^2) ) T(0, )\n              g[i] ~  dnorm(0.5 , 1/(2.5^2) ) I(-a[i], )\n              sigma_f[i]  ~  dexp (20)\n\n              D[i] ~ dt ( central_D , precision_D, 1)    #  Cauchy distribution\n\n              S_y[1,i] <-  1/(sLum[1,i]^2 + sigma_f[i]^2)\n              Lum[1,i] ~ dnorm ( Q[1,i] , S_y[1,i])\n              Q[1,i]  <-  GC_Origin * g[i] + LinGC * (c[i] * D[i] ) + ExpoGC * (a[i] * (1 - exp (-D[i] /b[i])) )\n\n              for (m in 2:Limited_cycles[i]) {\n                S_y[m,i] <-  1/(sLum[m,i]^2 + sigma_f[i]^2)\n                Lum[m,i] ~ dnorm( Q[m,i] , S_y[m,i] )\n                Q[m,i]  <-  GC_Origin * g[i] + LinGC * (c[i] * Dose[m,i]) + ExpoGC * (a[i] * (1 - exp (-Dose[m,i]/b[i])) )\n              }\n            }\n          }"]
            },
            {
              "type": "character",
              "attributes": {},
              "value": ["model {\n            central_D ~  dunif(lower_centralD,upper_centralD)\n\n            sigma_D ~ dunif(0.01, 1 * central_D)\n\n            for (i in 1:Nb_aliquots) {\n              a[i] ~  dnorm(6.5 , 1/(9.2^2) ) T(0, )\n              b[i] ~  dnorm(50 , 1/(1000^2) )  T(0, )\n              c[i] ~  dnorm(1.002 , 1/(0.9^2) ) T(0, )\n              g[i] ~  dnorm(0.5 , 1/(2.5^2) ) I(-a[i], )\n              sigma_f[i]  ~  dexp (20)\n\n              D[i] ~ dnorm ( central_D , 1/(sigma_D^2) )   #   Normal distribution\n\n              S_y[1,i] <-  1/(sLum[1,i]^2 + sigma_f[i]^2)\n              Lum[1,i] ~ dnorm ( Q[1,i] , S_y[1,i])\n              Q[1,i]  <-  GC_Origin * g[i] + LinGC * (c[i] * D[i] ) + ExpoGC * (a[i] * (1 - exp (-D[i] /b[i])) )\n\n\n              for (m in 2:Limited_cycles[i]) {\n                S_y[m,i] <-  1/(sLum[m,i]^2 + sigma_f[i]^2)\n                Lum[m,i] ~ dnorm( Q[m,i] , S_y[m,i] )\n                Q[m,i]  <-  GC_Origin * g[i] + LinGC * (c[i] * Dose[m,i]) + ExpoGC * (a[i] * (1 - exp (-Dose[m,i]/b[i])) )\n              }\n            }\n            }"]
            },
            {
              "type": "character",
              "attributes": {},
              "value": ["model {\n            central_D ~  dunif(lower_centralD,upper_centralD)\n\n            log_central_D <-  log(central_D) - 0.5 * l_sigma_D^2\n            l_sigma_D ~ dunif(0.01, 1 * log(central_D))\n            sigma_D <-  sqrt((exp(l_sigma_D^2) -1) * exp( 2*log_central_D + l_sigma_D^2) )\n\n            for (i in 1:Nb_aliquots) {\n              a[i] ~  dnorm(6.5 , 1/(9.2^2) ) T(0, )\n              b[i] ~  dnorm(50 , 1/(1000^2) )  T(0, )\n              c[i] ~  dnorm(1.002 , 1/(0.9^2) ) T(0, )\n              g[i] ~  dnorm(0.5 , 1/(2.5^2) ) I(-a[i], )\n              sigma_f[i]  ~  dexp (20)\n\n              log_D[i] ~ dnorm ( log_central_D , 1/(l_sigma_D^2) )  #   Log-Normal distribution\n              D[i] <-  exp(log_D[i])\n\n              S_y[1,i] <-  1/(sLum[1,i]^2 + sigma_f[i]^2)\n              Lum[1,i] ~ dnorm ( Q[1,i] , S_y[1,i])\n              Q[1,i]  <-  GC_Origin * g[i] + LinGC * (c[i] * D[i] ) + ExpoGC * (a[i] * (1 - exp (-D[i] /b[i])) )\n\n            for (m in 2:Limited_cycles[i]) {\n                S_y[m,i] <-  1/(sLum[m,i]^2 + sigma_f[i]^2)\n                Lum[m,i] ~ dnorm( Q[m,i] , S_y[m,i] )\n                Q[m,i]  <-  GC_Origin * g[i] + LinGC * (c[i] * Dose[m,i]) + ExpoGC * (a[i] * (1 - exp (-Dose[m,i]/b[i])) )\n              }\n            }\n        }"]
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