Parametric Bootstrapping Assuming Multivariate Normal Distribution
pb.vm( muthetahat, Sigmathetahat, skewnessthetahat, kurtosisthetahat, n, std = FALSE, B = 5000, par = TRUE, ncores = NULL, blas_threads = TRUE, mc = TRUE, lb = FALSE )
muthetahat | Numeric vector. Model-implied mean vector \( \boldsymbol{\mu} \left( \boldsymbol{\hat{\theta}} \right) \) . |
---|---|
Sigmathetahat | Numeric matrix. Model-implied variance-covariance matrix \( \boldsymbol{\Sigma} \left( \boldsymbol{\hat{\theta}} \right) \) . |
skewnessthetahat | Numeric vector. Estimated skewness. |
kurtosisthetahat | Numeric vector. Estimated excess kurtosis |
n | Integer. Sample size. |
std | Logical. Standardize the indirect effect \( \hat{\alpha}^{\prime} \hat{\beta}^{\prime} = \hat{\alpha} \hat{\beta} \frac{\hat{\sigma}_x}{\hat{\sigma}_y}\). |
B | Integer. Number of bootstrap samples. |
par | Logical.
If |
ncores | Integer.
Number of cores to use if |
blas_threads | Logical.
If |
mc | Logical.
If |
lb | Logical.
If |
Other parametric functions:
beta_pb.beta_bcaci_simulation()
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beta_pb.beta_bcaci_task()
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beta_pb.beta_bcci_simulation()
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beta_pb.beta_bcci_task()
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beta_pb.beta_pcci_simulation()
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beta_pb.beta_pcci_task()
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beta_pb.beta_simulation()
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beta_pb.beta_task()
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beta_pb.beta()
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beta_pb.mvn_bcaci_simulation()
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beta_pb.mvn_bcaci_task()
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beta_pb.mvn_bcci_simulation()
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beta_pb.mvn_bcci_task()
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beta_pb.mvn_pcci_simulation()
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beta_pb.mvn_pcci_task()
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beta_pb.mvn_simulation()
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beta_pb.mvn_task()
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beta_pb.mvn()
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exp_pb.exp_bcaci_simulation()
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exp_pb.exp_bcaci_task()
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exp_pb.exp_bcci_simulation()
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exp_pb.exp_bcci_task()
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exp_pb.exp_pcci_simulation()
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exp_pb.exp_pcci_task()
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exp_pb.exp_simulation()
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exp_pb.exp_task()
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exp_pb.exp()
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exp_pb.mvn_bcaci_simulation()
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exp_pb.mvn_bcaci_task()
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exp_pb.mvn_bcci_simulation()
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exp_pb.mvn_bcci_task()
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exp_pb.mvn_pcci_simulation()
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exp_pb.mvn_pcci_task()
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exp_pb.mvn_simulation()
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exp_pb.mvn_task()
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exp_pb.mvn()
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mvn_mar_10_pb.mvn_bcci_simulation()
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mvn_mar_10_pb.mvn_bcci_task()
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mvn_mar_10_pb.mvn_pcci_simulation()
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mvn_mar_10_pb.mvn_pcci_task()
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mvn_mar_10_pb.mvn_simulation()
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mvn_mar_10_pb.mvn_task()
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mvn_mar_10_pb.mvn()
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mvn_mar_20_pb.mvn_bcci_simulation()
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mvn_mar_20_pb.mvn_bcci_task()
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mvn_mar_20_pb.mvn_pcci_simulation()
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mvn_mar_20_pb.mvn_pcci_task()
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mvn_mar_20_pb.mvn_simulation()
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mvn_mar_20_pb.mvn_task()
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mvn_mar_20_pb.mvn()
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mvn_mar_30_pb.mvn_bcci_simulation()
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mvn_mar_30_pb.mvn_bcci_task()
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mvn_mar_30_pb.mvn_pcci_simulation()
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mvn_mar_30_pb.mvn_pcci_task()
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mvn_mar_30_pb.mvn_simulation()
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mvn_mar_30_pb.mvn_task()
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mvn_mar_30_pb.mvn()
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mvn_mcar_10_pb.mvn_bcci_simulation()
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mvn_mcar_10_pb.mvn_bcci_task()
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mvn_mcar_10_pb.mvn_pcci_simulation()
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mvn_mcar_10_pb.mvn_pcci_task()
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mvn_mcar_10_pb.mvn_simulation()
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mvn_mcar_10_pb.mvn_task()
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mvn_mcar_10_pb.mvn()
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mvn_mcar_20_pb.mvn_bcci_simulation()
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mvn_mcar_20_pb.mvn_bcci_task()
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mvn_mcar_20_pb.mvn_pcci_simulation()
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mvn_mcar_20_pb.mvn_pcci_task()
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mvn_mcar_20_pb.mvn_simulation()
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mvn_mcar_20_pb.mvn_task()
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mvn_mcar_20_pb.mvn()
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mvn_mcar_30_pb.mvn_bcci_simulation()
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mvn_mcar_30_pb.mvn_bcci_task()
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mvn_mcar_30_pb.mvn_pcci_simulation()
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mvn_mcar_30_pb.mvn_pcci_task()
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mvn_mcar_30_pb.mvn_simulation()
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mvn_mcar_30_pb.mvn_task()
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mvn_mcar_30_pb.mvn()
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mvn_mnar_10_pb.mvn_bcci_simulation()
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mvn_mnar_10_pb.mvn_bcci_task()
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mvn_mnar_10_pb.mvn_pcci_simulation()
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mvn_mnar_10_pb.mvn_pcci_task()
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mvn_mnar_10_pb.mvn_simulation()
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mvn_mnar_10_pb.mvn_task()
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mvn_mnar_10_pb.mvn()
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mvn_mnar_20_pb.mvn_bcci_simulation()
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mvn_mnar_20_pb.mvn_bcci_task()
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mvn_mnar_20_pb.mvn_pcci_simulation()
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mvn_mnar_20_pb.mvn_pcci_task()
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mvn_mnar_20_pb.mvn_simulation()
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mvn_mnar_20_pb.mvn_task()
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mvn_mnar_20_pb.mvn()
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mvn_mnar_30_pb.mvn_bcci_simulation()
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mvn_mnar_30_pb.mvn_bcci_task()
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mvn_mnar_30_pb.mvn_pcci_simulation()
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mvn_mnar_30_pb.mvn_pcci_task()
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mvn_mnar_30_pb.mvn_simulation()
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mvn_mnar_30_pb.mvn_task()
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mvn_mnar_30_pb.mvn()
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mvn_pb.mvn_bcaci_simulation()
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mvn_pb.mvn_bcaci_task()
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mvn_pb.mvn_bcci_simulation()
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mvn_pb.mvn_bcci_task()
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mvn_pb.mvn_pcci_simulation()
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mvn_pb.mvn_pcci_task()
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mvn_pb.mvn_simulation()
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mvn_pb.mvn_task()
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mvn_pb.mvn()
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mvn_std_pb.mvn_bcaci_simulation()
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mvn_std_pb.mvn_bcaci_task()
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mvn_std_pb.mvn_bcci_simulation()
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mvn_std_pb.mvn_bcci_task()
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mvn_std_pb.mvn_pcci_simulation()
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mvn_std_pb.mvn_pcci_task()
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mvn_std_pb.mvn_simulation()
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mvn_std_pb.mvn_task()
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mvn_std_pb.mvn()
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pb.beta()
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pb.exp()
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pb.mvn()
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vm_mod_pb.mvn_bcaci_simulation()
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vm_mod_pb.mvn_bcaci_task()
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vm_mod_pb.mvn_bcci_simulation()
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vm_mod_pb.mvn_bcci_task()
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vm_mod_pb.mvn_pcci_simulation()
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vm_mod_pb.mvn_pcci_task()
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vm_mod_pb.mvn_simulation()
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vm_mod_pb.mvn_task()
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vm_mod_pb.mvn()
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vm_mod_pb.vm_bcaci_simulation()
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vm_mod_pb.vm_bcaci_task()
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vm_mod_pb.vm_bcci_simulation()
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vm_mod_pb.vm_bcci_task()
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vm_mod_pb.vm_pcci_simulation()
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vm_mod_pb.vm_pcci_task()
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vm_mod_pb.vm_simulation()
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vm_mod_pb.vm_task()
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vm_mod_pb.vm()
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vm_mod_std_pb.mvn_bcaci_simulation()
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vm_mod_std_pb.mvn_bcaci_task()
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vm_mod_std_pb.mvn_bcci_simulation()
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vm_mod_std_pb.mvn_bcci_task()
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vm_mod_std_pb.mvn_pcci_simulation()
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vm_mod_std_pb.mvn_pcci_task()
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vm_mod_std_pb.mvn_simulation()
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vm_mod_std_pb.mvn_task()
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vm_mod_std_pb.mvn()
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vm_sev_pb.mvn_bcaci_simulation()
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vm_sev_pb.mvn_bcaci_task()
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vm_sev_pb.mvn_bcci_simulation()
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vm_sev_pb.mvn_bcci_task()
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vm_sev_pb.mvn_pcci_simulation()
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vm_sev_pb.mvn_pcci_task()
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vm_sev_pb.mvn_simulation()
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vm_sev_pb.mvn_task()
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vm_sev_pb.mvn()
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vm_sev_pb.vm_bcaci_simulation()
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vm_sev_pb.vm_bcaci_task()
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vm_sev_pb.vm_bcci_simulation()
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vm_sev_pb.vm_bcci_task()
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vm_sev_pb.vm_pcci_simulation()
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vm_sev_pb.vm_pcci_task()
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vm_sev_pb.vm_simulation()
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vm_sev_pb.vm_task()
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vm_sev_pb.vm()
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vm_sev_std_pb.mvn_bcaci_simulation()
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vm_sev_std_pb.mvn_bcaci_task()
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vm_sev_std_pb.mvn_bcci_simulation()
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vm_sev_std_pb.mvn_bcci_task()
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vm_sev_std_pb.mvn_pcci_simulation()
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vm_sev_std_pb.mvn_pcci_task()
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vm_sev_std_pb.mvn_simulation()
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vm_sev_std_pb.mvn_task()
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vm_sev_std_pb.mvn()
Ivan Jacob Agaloos Pesigan
muthetahat <- mutheta( mux = 70.18000, deltam = 26.82246, deltay = 29.91071, taudot = 0.207648, beta = 0.451039, alpha = 0.338593 ) Sigmathetahat <- Sigmatheta( taudot = 0.207648, beta = 0.451039, alpha = 0.338593, sigma2x = 1.293469, sigma2epsilonm = 0.9296691, sigma2epsilony = 0.9310597 ) skewnessthetahat <- c( -0.542156, -0.2377815, -0.06016235 ) kurtosisthetahat <- c( 0.2215535, -0.4361385, -0.6906525 ) # Unstandardized ------------------------------------------------------------- thetahatstar <- pb.vm( muthetahat = muthetahat, Sigmathetahat = Sigmathetahat, skewnessthetahat = skewnessthetahat, kurtosisthetahat = kurtosisthetahat, n = 50, B = 5000, par = FALSE ) hist(thetahatstar)# Standardized --------------------------------------------------------------- thetahatstar <- pb.vm( muthetahat = muthetahat, Sigmathetahat = Sigmathetahat, skewnessthetahat = skewnessthetahat, kurtosisthetahat = kurtosisthetahat, n = 50, std = TRUE, B = 5000, par = FALSE ) hist(thetahatstar)