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| ancova.random.data | Generate random data for an ANCOVA model |
| ci.c | Confidence interval for a contrast in a fixed effets ANOVA |
| ci.c.ancova | Confidence Interval for an (unstandardized) contrast in ANCOVA with one covariate |
| ci.cv | Confidence interval for the coefficient of variation |
| ci.pvaf | Confidennce Interval for the Proportion of Variance Accounted for (in the dependent variable by knowing the levels of the factor) |
| ci.R | Confidence interval for the multiple correlation coefficient |
| ci.R2 | Confidence intervals for the squared multiple correlation coefficient |
| ci.rc | Confidence Interval for a Regression Coefficient |
| ci.reg.coef | confidence interval for a regression coefficient |
| ci.rmsea | Confidence interval for the population root mean square error of approximation |
| ci.sc | Confidence Interval for a Standardized Contrast in a Fixed Effets ANOVA |
| ci.sc.ancova | Confidence interval for a standardized contrast in ANCOVA with one covariate |
| ci.sm | Confidence Interval for the Standardized Mean |
| ci.smd | Confidence limits for the standardized mean difference. |
| ci.smd.c | Confidence limits for the standardized mean difference using the control group standard deviation as the divisor. |
| ci.snr | Confidence Interval for the Signal-To-Noise Ratio |
| ci.src | Confidence Interval for a Standardized Regression Coefficient |
| ci.srsnr | Confidence Interval for the Square Root of the Signal-To-Noise Ratio |
| conf.limits.nc.chisq | Confidence limits for noncentral chi square parameters |
| conf.limits.ncf | Confidence limits for noncentral F parameters |
| conf.limits.nct | Confidence limits for a noncentrality parameter from a t-distribution |
| conf.limits.nct.M1 | Confidence limits for a noncentrality parameter from a t-distribution (Method 1 of 3) |
| conf.limits.nct.M2 | Confidence limits for a noncentrality parameter from a t-distribution (Method 2 of 3) |
| conf.limits.nct.M3 | Confidence limits for a noncentrality parameter from a t-distribution (Method 3 of 3) |
| Cor.Mat.Lomax | Correlation matrix for Lomax (1983) data set |
| Cor.Mat.MM | Correlation matrix for Maruyama & McGarvey (1980) data set |
| cor2cov | Correlation Matrix to Covariance Matrix Conversion |
| cv | Function to calculate the regular (and biased) estimate of the coefficient of variation or the unbiased estimate of the coefficient of variation. |
| delta2lambda | Conversion functions for noncentral t-distribution |
| Expected.R2 | Expected value of the squared multiple correlation coefficient |
| F2Rsquare | Conversion functions from noncentral noncentral values to their corresponding and vice versa, for those related to the F-test and R Square. |
| Gardner.LD | The Gardner learning data, which was used by L.R. Tucker |
| HS.data | Complete Data Set of Holzinger and Swineford's (1939) Study |
| intr.plot | Regression Surface Containing Interaction |
| intr.plot.2d | Plotting Conditional Regression Lines with Interactions in Two Dimensions |
| lambda2delta | Conversion functions for noncentral t-distribution |
| Lambda2Rsquare | Conversion functions from noncentral noncentral values to their corresponding and vice versa, for those related to the F-test and R Square. |
| MBES | Methods for the Behavioral, Educational, and Social Sciences |
| mbes | Methods for the Behavioral, Educational, and Social Sciences |
| MBESS | Methods for the Behavioral, Educational, and Social Sciences |
| mbess | Methods for the Behavioral, Educational, and Social Sciences |
| prof.salary | Cohen et. al. (2003)'s professor salary data set |
| Rsquare2F | Conversion functions from noncentral noncentral values to their corresponding and vice versa, for those related to the F-test and R Square. |
| Rsquare2Lambda | Conversion functions from noncentral noncentral values to their corresponding and vice versa, for those related to the F-test and R Square. |
| s.u | Unbiased estiamte for the standard deviation |
| signal.to.noise.R2 | Signal to noise using squared multiple correlation coefficient |
| smd | Standardized mean difference |
| smd.c | Standardized mean difference using the control group as the basis of standardization |
| ss.aipe.c | Sample size planning for an ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) perspective |
| ss.aipe.c.ancova | Sample size planning for a contrast in randomized ANCOVA from the Accuracy in Parameter Estimation (AIPE) perspective |
| ss.aipe.c.ancova.sensitivity | Sensitivity analysis for sample size planning for the (unstandardized) contrast in randomized ANCOVA from the Accuracy in Parameter Estimation (AIPE) Perspective |
| ss.aipe.cv | Sample size planning for the coefficient of variation given the goal of Accuracy in Parameter Estimation approach to sample size planning. |
| ss.aipe.cv.sensitivity | Sensitivity analysis for sample size planning given the Accuracy in Parameter Estimation approach for the coefficient of variation. |
| ss.aipe.R2 | Sample Size Planning for Accuracy in Parameter Estimation for the multiple correlation coefficient. |
| ss.aipe.R2.sensitivity | Sensitivity analysis for sample size planning with the goal of Accuracy in Parameter Estimation (i.e., a narrow observed confidence interval) |
| ss.aipe.rc | sample size necessary for the accuracy in parameter estimation approach for an unstandardized regression coefficient of interest |
| ss.aipe.rc.sensitivity | Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the unstandardized regression coefficient |
| ss.aipe.reg.coef | sample size necessary for the accuracy in parameter estimation approach for a regression coefficient of interest |
| ss.aipe.reg.coef.sensitivity | Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the (standardized and unstandardized) regression coefficient |
| ss.aipe.rmsea | Sample siza planning for population root mean square error of approximation |
| ss.aipe.sc | Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized contrast in ANOVA |
| ss.aipe.sc.ancova | Sample size planning from the AIPE perspective for standardized ANCOVA contrasts |
| ss.aipe.sc.ancova.sensitivity | Sensitivity analysis for the sample size planning method for standardized ANCOVA contrast |
| ss.aipe.sc.sensitivity | Sensitivity analysis for sample size planning for the standardized ANOVA contrast from the Accuracy in Parameter Estimation (AIPE) Perspective |
| ss.aipe.sm | Sample size planning for Accuracy in Parameter Estimation (AIPE) of the standardized mean |
| ss.aipe.sm.sensitivity | Sensitivity analysis for sample size planning for the standardized mean from the Accuracy in Parameter Estimation (AIPE) Perspective |
| ss.aipe.smd | Sample size planning for the standardized mean difference from the Accuracy in Parameter Estimation (AIPE) perspective |
| ss.aipe.smd.full | Sample size planning for the standardized mean different from the accuracy in parameter estimation approach |
| ss.aipe.smd.lower | Sample size planning for the standardized mean different from the accuracy in parameter estimation approach |
| ss.aipe.smd.sensitivity | Sensitivity analysis for sample size given the Accuracy in Parameter Estimation approach for the standardized mean difference. |
| ss.aipe.smd.upper | Sample size planning for the standardized mean different from the accuracy in parameter estimation approach |
| ss.aipe.src | sample size necessary for the accuracy in parameter estimation approach for a standardized regression coefficient of interest |
| ss.aipe.src.sensitivity | Sensitivity analysis for sample size planing from the Accuracy in Parameter Estimation Perspective for the standardized regression coefficient |
| ss.power.R2 | Function to plan sample size so that the test of the squred multiple correlation coefficient is sufficiently powerful. |
| ss.power.rc | sample size for a targeted regression coefficient |
| ss.power.reg.coef | sample size for a targeted regression coefficient |
| Variance.R2 | Variance of squared multiple correlation coefficient |
| verify.ss.aipe.R2 | Internal MBESS function for verifying the sample size in ss.aipe.R2 |
| vit | Visualize individual trajectories |
| vit.fitted | Visualize individual trajectories with fitted curve and quality of fit |