dat1                    Dataset with Simulated (Y, C) Values for
                        Examples in dfa_xerrors and logreg_xerrors
dat1_xtilde             Dataset with Simulated Xtilde Values for
                        Examples in dfa_xerrors and logreg_xerrors
dfa_xerrors             Discriminant Function Approach for Estimating
                        Odds Ratio with Normal Exposure Subject to
                        Measurement Error
gamma_constantscale     Fit Constant-Scale Gamma Model for Y vs.
                        Covariates
lognormal               Fit Lognormal Regression for Y vs. Covariates
logreg_xerrors          Logistic Regression with Normal Exposure
                        Subject to Errors
p_dfa_xerrors           Discriminant Function Approach for Estimating
                        Odds Ratio with Normal Exposure Measured in
                        Pools and Subject to Errors
p_dfa_xerrors2          Discriminant Function Approach for Estimating
                        Odds Ratio with Gamma Exposure Measured in
                        Pools and Subject to Errors
p_logreg                Poolwise Logistic Regression
p_logreg_xerrors        Poolwise Logistic Regression with Normal
                        Exposure Subject to Errors
p_logreg_xerrors2       Poolwise Logistic Regression with Gamma
                        Exposure Subject to Errors
pdat1                   Dataset with Simulated (Y, Xtilde, C) Values
                        for Examples in p_dfa_xerrors and
                        p_logreg_xerrors
pdat2                   Dataset with Simulated (Y, Xtilde) Values for
                        Examples in p_dfa_xerrors2 and
                        p_logreg_xerrors2
pdat2_c                 Dataset with Simulated C Values for Examples in
                        p_dfa_xerrors2 and p_logreg_xerrors2
plot_dfa                Plot Log-OR vs. X for Normal Discriminant
                        Function Approach
plot_dfa2               Plot Log-OR vs. X for Gamma Discriminant
                        Function Approach
poolcost_t              Visualize Total Costs for Pooling Design as a
                        Function of Pool Size
pooling                 Fit Poolwise Regression Models
poolpower_t             Visualize T-test Power for Pooling Design
poolvar_t               Visualize Ratio of Variance of Each Pooled
                        Measurement to Variance of Each Unpooled
                        Measurement as Function of Pool Size
test_pe                 Test for Underestimated Processing Error
                        Variance in Pooling Studies
