1 |
Markov Matrix |
- Input the number of Recruitment States: \(K\)
- On the next line, input the recruitment values: \(R_1,R_2,...,R_K\)
- On the next line, input number of spawning biomass states: \(J\)
- On the next line, input \(J-1\) cut points : \(B_{S,1},B_{S,2},...,B_{S,J-1}\)
- On the next \(J\) lines, input the conditional recruitment probabilities for the spawning biomass states:
- \(P_{1,1}, \quad P_{1,2}, \quad \dots, \quad P_{1,K}\)
\(P_{2,1}, \quad P_{2,2}, \quad \dots, \quad P_{2,k}\) \(\quad \vdots \quad\quad \vdots \qquad\quad \vdots \qquad\quad \vdots\) \(P_{J,1}, \quad P_{J,2}, \quad \dots, \quad P_{J,K}\)
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2 |
Empirical Recruits Per Spawning Biomass Distribution |
- Input the number of stock recruitment data points: \(T\)
- On the next line, input the recruitments: \(R_1,R_2,...,R_T\)
- On the next line, input the spawning biomasses: \(B_{S,1}, B_{S,2}, ..., B_{S,T}\)
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3 |
Empirical Recruitment Distribution |
- Input the number of recruitment data points: \(T\)
- On the next line, input the recruitments: \(R_1,R_2,...,R_T\)
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4 |
Two-Stage Empirical Recruits Per Spawning Biomass Distribution |
- Input the number of low and high recruits per spawning biomass data points: \(T_{Low} \cdot T_{High}\)
- On the next line, input the cutoff level of spawning biomass: \(B^*_S\)
- On the next line, input the low state recruitments: \(R_1,R_2,...,R_{T_{Low}}\)
- On the next line, input the low state spawning biomasses: \(B_{S,1}, B_{S,2}, ..., B_{S,T_{Low}}\)
- On the next line, input the high state recruitments: \(R_1,R_2,...,R_{T_{High}}\)
- On the next line, input the high state spawning biomasses: \(B_{S,1}, B_{S,2}, ..., B_{S,T_{High}}\)
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5 |
Beverton-Holt Curve with Lognormal Error |
- Input the stock-recruitment parameters: \(\alpha, \beta, \sigma^2_w\)
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6 |
Ricker Curve with Lognormal Error |
- Input the stock-recruitment parameters: \(\alpha, \beta, \sigma^2_w\)
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7 |
Shepherd Curve with Lognormal Error |
- Input the stock-recruitment parameters: \(\alpha, \beta, k, \sigma^2_w\)
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8 |
Lognormal Distribution |
- Input the log-scale mean and standard deviation: \(\mu_{\log(r)},\sigma_{\log(r)}\)
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10 |
Beverton-Holt Curve with Autocorrected Lognornal Error |
- Input the stock-recruitment parameters: \(\alpha, \beta, \sigma^2_w\)
- On the next line, input the autoregressive parameters: \(\phi, \varepsilon_{0}\)
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11 |
Ricker Curve with Autocorrected Lognormal Error |
- Input the stock-recruitment parameters: \(\alpha, \beta, \sigma^2_w\)
- On the next line, input the autoregressive parameters: \(\phi, \varepsilon_{0}\)
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12 |
Shepherd Curve with Autocorrected Lognormal Error |
- Input the stock-recruitment parameters: \(\alpha, \beta, k, \sigma^2_w\)
- On the next line, input the autoregressive parameters: \(\phi, \varepsilon_{0}\)
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13 |
Autocorrected Lognormal Distribution |
- Input the log-scale mean and standard deviation: \(\mu_{\log(r)},\sigma_{\log(r)}\)
- On the next line, input the autoregressive parameters: \(\phi, \varepsilon_{0}\)
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14 |
Empirical Cumulative Distribution Function of Recruitment |
- Input the number of recruitment data points: \(T\)
- On the next line, input the recruitments \(R_1,R_2,...,R_T\)
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15 |
Two-Stage Empirical Cumulative Distribution Function of Recruitment |
- Input the number of low and high recruits per spawning biomass data points: \(T_{Low} \cdot T_{High}\)
- On the next line, input cutoff level of spawning biomass: \(B^*_S\)
- On the next line, input the low state recruitments: \(R_1,R_2,...,R_{T_{Low}}\)
- On the next line, input the high state recruitments: \(R_1,R_2,...,R_{T_{High}}\)
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16 |
Linear Recruits Per Spawning Biomass Predictor with Normal Error |
- Input the predictors: \(N_P\)
- On the next line, input the intercept coefficient: \(\beta_0\)
- On the next line, input the slope coefficient for each predictor: \(\beta_1, \beta_2, ..., \beta_{N_p}\)
- On the next line, input the error variance: \(\sigma^2\)
- On the next \(N_P\) lines, input the expected value of the predictor through projection time horizon:
- \(X_1(1), \quad\ \dots, \quad\ X_1(Y)\)
\(X_2(1), \quad\ \dots, \quad\ X_2(Y)\) \(\ \vdots \qquad\qquad\ \ \vdots \qquad\quad\ \ \vdots\) \(X_P(1), \quad\ \dots, \quad\ X_P(Y)\)
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17 |
Linear Recruits Per Spawning Biomass Predictor with Lognormal Error |
- Input the number of predictors: \(N_P\)
- On the next line, input the intercept: \(\beta_0\)
- On the next line, input the linear coefficient for each predictor: \(\beta_1, \beta_2, ..., \beta_{N_P}\)
- On the next line, input the log-scale error variance: \(\sigma^2\)
- And on the next \(N_P\) lines, input the expected predictor values over the forecast time horizon \(1, ..., Y\)
- \(X_1(1) \quad X_1(2) \quad \dots \quad X_1(Y)\)
\(X_2(1) \quad X_2(2) \quad \dots \quad X_2(Y)\) \(\quad \vdots \qquad\quad\ \vdots \qquad\quad \vdots \qquad\quad \vdots\) \(X_P(1) \quad X_P(2) \quad \dots \quad X_P(Y)\)
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18 |
Linear Recruitment Predictor with Normal Error |
- Input the number of predictors: \(N_P\)
- On the next line, input the intercept: \(\beta_0\)
- On the next line, input the linear coefficient for each predictor: \(\beta_1, \beta_2, ..., \beta_{N_P}\)
- On the next line, input the error variance: \(\sigma^2\)
- And on the next \(N_P\) lines, input the expected predictor values over the forecast time horizon \(1, ..., Y\)
- \(X_1(1) \quad X_1(2) \quad \dots \quad X_1(Y)\)
\(X_2(1) \quad X_2(2) \quad \dots \quad X_2(Y)\) \(\quad \vdots \qquad\quad\ \vdots \qquad\quad \vdots \qquad\quad \vdots\) \(X_P(1) \quad X_P(2) \quad \dots \quad X_P(Y)\)
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19 |
Loglinear Recruitment Predictor with Lognormal Error |
- Input the number of predictors: \(N_P\)
- On the next line, input the intercept: \(\beta_0\)
- On the next line, input the linear coefficient for each predictor: \(\beta_1, \beta_2, ..., \beta_{N_P}\)
- On the next line, input the log-scale error variance: \(\sigma^2\)
- And on the next \(N_P\) lines, input the expected predictor values over the forecast time horizon \(1, ..., Y\)
- \(X_1(1) \quad X_1(2) \quad \dots \quad X_1(Y)\)
\(X_2(1) \quad X_2(2) \quad \dots \quad X_2(Y)\) \(\quad \vdots \qquad\quad\ \vdots \qquad\quad \vdots \qquad\quad \vdots\) \(X_P(1) \quad X_P(2) \quad \dots \quad X_P(Y)\)
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20 |
Fixed Recruitment |
- Input the number of recruitment data points: \(T\)
- On the next line, input the Recruitments: \(R_1,R_2,...,R_T\)
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21 |
Empirical Cumulative Distribution Function of Recruitment with Linear Decline to Zero |
- Input the number of number of observed recruitment values: \(T\)
- On the next line, input the recruitment values: \(R_1, R_2, ..., R_T\)
- And on the next line, input spawning biomass threshold: \(B^*_S\)
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