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    "brar_select_au_known_var",
    "brar_select_au_unknown_var",
    "convert_chisq_to_gamma",
    "convert_gamma_to_chisq",
    "dabcd_max_power",
    "dabcd_min_var",
    "flgi_cut_off_binary",
    "flgi_cut_off_known_var",
    "flgi_cut_off_unknown_var",
    "Gittins",
    "pgreater_beta",
    "pgreater_NIX",
    "pgreater_normal",
    "pmax_beta",
    "pmax_NIX",
    "pmax_normal",
    "sim_A_optimal_known_var",
    "sim_A_optimal_unknown_var",
    "sim_Aa_optimal_known_var",
    "sim_Aa_optimal_unknown_var",
    "sim_brar_binary",
    "sim_brar_known_var",
    "sim_brar_unknown_var",
    "sim_dabcd_max_power",
    "sim_dabcd_min_var",
    "sim_flgi_binary",
    "sim_flgi_known_var",
    "sim_flgi_unknown_var",
    "sim_RPTW",
    "sim_RSIHR_optimal_known_var",
    "sim_RSIHR_optimal_unknown_var",
    "update_par_nichisq"
  ],
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    {
      "page": "brar_select_au_binary",
      "title": "Select au in Bayesian Response-Adaptive Randomization with a Control Group for Binary Endpoint",
      "topics": [
        "brar_select_au_binary"
      ]
    },
    {
      "page": "brar_select_au_known_var",
      "title": "Select au in Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Known Variances",
      "topics": [
        "brar_select_au_known_var"
      ]
    },
    {
      "page": "brar_select_au_unknown_var",
      "title": "Select au in Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Unknown Variances",
      "topics": [
        "brar_select_au_unknown_var"
      ]
    },
    {
      "page": "convert_chisq_to_gamma",
      "title": "Convert parameters from a Normal-Inverse-Chi-Squared Distribution to a Normal-Inverse-Gamma Distribution",
      "topics": [
        "convert_chisq_to_gamma"
      ]
    },
    {
      "page": "convert_gamma_to_chisq",
      "title": "Convert parameters from a Normal-Inverse-Gamma Distribution to a Normal-Inverse-Chi-Squared Distribution",
      "topics": [
        "convert_gamma_to_chisq"
      ]
    },
    {
      "page": "dabcd_max_power",
      "title": "Allocation Probabilities Using Doubly Adaptive Biased Coin Design with Maximal Power Strategy for Binary Endpoint",
      "topics": [
        "dabcd_max_power"
      ]
    },
    {
      "page": "dabcd_min_var",
      "title": "Allocation Probabilities Using Doubly Adaptive Biased Coin Design with Minimal Variance Strategy for Binary Endpoint",
      "topics": [
        "dabcd_min_var"
      ]
    },
    {
      "page": "flgi_cut_off_binary",
      "title": "Cut-off Value of the Forward-looking Gittins Index Rule in Binary Endpoint",
      "topics": [
        "flgi_cut_off_binary"
      ]
    },
    {
      "page": "flgi_cut_off_known_var",
      "title": "Cut-off Value of the Forward-looking Gittins Index Rule in Continuous Endpoint with Known Variances",
      "topics": [
        "flgi_cut_off_known_var"
      ]
    },
    {
      "page": "flgi_cut_off_unknown_var",
      "title": "Cut-off Value of the Forward-looking Gittins Index rule in Continuous Endpoint with Unknown Variances",
      "topics": [
        "flgi_cut_off_unknown_var"
      ]
    },
    {
      "page": "Gittins",
      "title": "Gittins Indices",
      "topics": [
        "Gittins"
      ]
    },
    {
      "page": "pgreater_beta",
      "title": "Calculate the Futility Stopping Probability for Binary Endpoint with Beta Distribution",
      "topics": [
        "pgreater_beta"
      ]
    },
    {
      "page": "pgreater_NIX",
      "title": "Calculate the Futility Stopping Probability for Continuous Endpoint with Unknown Variances Using a Normal-Inverse-Chi-Squared Distribution",
      "topics": [
        "pgreater_NIX"
      ]
    },
    {
      "page": "pgreater_normal",
      "title": "Calculate the Futility Stopping Probability for Continuous Endpoint with Known Variances Using Normal Distribution",
      "topics": [
        "pgreater_normal"
      ]
    },
    {
      "page": "pmax_beta",
      "title": "Posterior Probability that a Particular Arm is the Best for Binary Endpoint",
      "topics": [
        "pmax_beta"
      ]
    },
    {
      "page": "pmax_NIX",
      "title": "Posterior Probability that a Particular Arm is the Best for Continuous Endpoint with Unknown Variances",
      "topics": [
        "pmax_NIX"
      ]
    },
    {
      "page": "pmax_normal",
      "title": "Posterior Probability that a Particular Arm is the Best for Continuous Endpoint with Known Variances",
      "topics": [
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      ]
    },
    {
      "page": "sim_A_optimal_known_var",
      "title": "Simulate a Trial Using A-Optimal Allocation for Continuous Endpoint with Known Variances",
      "topics": [
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      ]
    },
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      "page": "sim_A_optimal_unknown_var",
      "title": "Simulate a Trial Using A-Optimal Allocation for Continuous Endpoint with Unknown Variances",
      "topics": [
        "sim_A_optimal_unknown_var"
      ]
    },
    {
      "page": "sim_Aa_optimal_known_var",
      "title": "Simulate a Trial Using Aa-Optimal Allocation for Continuous Endpoint with Known Variances",
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      ]
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    {
      "page": "sim_Aa_optimal_unknown_var",
      "title": "Simulate a Trial Using Aa-Optimal Allocation for Continuous Endpoint with Unknown Variances",
      "topics": [
        "sim_Aa_optimal_unknown_var"
      ]
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      "page": "sim_brar_binary",
      "title": "Simulate a Trial Using Bayesian Response-Adaptive Randomization with a Control Group for Binary Outcomes",
      "topics": [
        "sim_brar_binary"
      ]
    },
    {
      "page": "sim_brar_known_var",
      "title": "Simulate a Trial Using Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Known Variances",
      "topics": [
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      ]
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    {
      "page": "sim_brar_unknown_var",
      "title": "Simulate a Trial Using Bayesian Response-Adaptive Randomization with a Control Group for Continuous Endpoint with Unknown Variances",
      "topics": [
        "sim_brar_unknown_var"
      ]
    },
    {
      "page": "sim_dabcd_max_power",
      "title": "Simulate a Trial Using Doubly Adaptive Biased Coin Design with Maximal Power Strategy for Binary Endpoint",
      "topics": [
        "sim_dabcd_max_power"
      ]
    },
    {
      "page": "sim_dabcd_min_var",
      "title": "Simulate a Trial Using Doubly Adaptive Biased Coin Design with Minmial Variance Strategy for Binary Endpoint",
      "topics": [
        "sim_dabcd_min_var"
      ]
    },
    {
      "page": "sim_flgi_binary",
      "title": "Simulate a Trial Using Forward-Looking Gittins Index for Binary Endpoint",
      "topics": [
        "sim_flgi_binary"
      ]
    },
    {
      "page": "sim_flgi_known_var",
      "title": "Simulate a Trial Using Forward-Looking Gittins Index for Continuous Endpoint with Known Variances",
      "topics": [
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      ]
    },
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      "page": "sim_flgi_unknown_var",
      "title": "Simulate a Trial Using Forward-Looking Gittins Index for Continuous Endpoint with Unknown Variances",
      "topics": [
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      ]
    },
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      "title": "Simulate a Trial Using Randomized Play-the-Winner Rule for Binary Endpoint",
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    },
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      "title": "Simulate a Trial Using Generalized RSIHR Allocation for Continuous Endpoint with Known Variances",
      "topics": [
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    },
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      "title": "Simulate a Trial Using Generalized RSIHR Allocation for Continuous Endpoint with Unknown Variances",
      "topics": [
        "sim_RSIHR_optimal_unknown_var"
      ]
    },
    {
      "page": "update_par_nichisq",
      "title": "Update Parameters of a Normal-Inverse-Chi-Squared Distribution with Available Data",
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