Assumptions             Compute power for Multiple Regression with
                        Violated assumptions (Beta)
Assumptions_resample    Compute power for Multiple Regression with
                        Violated assumptions using Resamples
Chi2X3                  Compute power for an Chi Square 2x3 Takes
                        proportions for each group. Alpha is .05 by
                        default, alternative values may be entered by
                        user
Chi2x2                  Compute power for an Chi Square 2x2 Takes
                        proportions for each group. Alpha is .05 by
                        default, alternative values may be entered by
                        user
ChiES                   Compute power for Chi Square Based on Effect
                        Size Takes phi, degrees of freedom, and a range
                        of sample sizes. Alpha is .05 by default,
                        alternative values may be entered by user
ChiGOF                  Compute power for an Chi Square Goodness of Fit
                        Takes proportions for up to six group. Alpha is
                        .05 by default, alternative values may be
                        entered by user
LRcat                   Compute Power for Logistic Regression with a
                        Single Categorical Predictor
LRcont                  Compute Power for Logistic Regression with
                        Continuous Predictors
MANOVA1f                Compute power for a One Factor MANOVA with up
                        to two levels and up to four measures. Takes
                        means, sds, and sample sizes for each group.
                        Alpha is .05 by default, alternative values may
                        be entered by user
MRC                     Compute power for Multiple Regression with up
                        to Five Predictors Example code below for three
                        predictors. Expand as needed for four or five
MRC_all                 Compute power for Multiple Regression with Up
                        to Five Predictors Requires correlations
                        between all variables as sample size. Means,
                        sds, and alpha are option. Also computes
                        Power(All)
MRC_short2              Compute Multiple Regression shortcuts with
                        three predictors for Ind Coefficients Requires
                        correlations between all variables as sample
                        size. Means and sds are option. Also computes
                        Power(All)
MRC_shortcuts           Compute Multiple Regression shortcuts with
                        three predictors (will expand to handle two to
                        five) Requires correlations between all
                        variables as sample size. Means and sds are
                        option. Also computes Power(All)
R2_prec                 Compute Precision Analyses for R-Squared This
                        approach simply loops a function from MBESS
R2ch                    Compute power for R2 change in Multiple
                        Regression (up to three predictors) Requires
                        correlations between all variables as sample
                        size. Means, sds, and alpha are option. Also
                        computes Power(All) Example code below for
                        three predictors. Expand as needed for four or
                        five
anova1f_3               Compute power for a One Factor ANOVA with three
                        levels. Takes means, sds, and sample sizes for
                        each group. Alpha is .05 by default,
                        alternative values may be entered by user
anova1f_3c              Compute power for a One Factor ANOVA with three
                        levels and contrasts. Takes means, sds, and
                        sample sizes for each group. Alpha is .05 by
                        default, alternative values may be entered by
                        user
anova1f_4               Compute power for a One Factor Between Subjects
                        ANOVA with four levels Takes means, sds, and
                        sample sizes for each group
anova1f_4c              Compute power for a One Factor ANOVA with four
                        levels. Takes means, sds, and sample sizes for
                        each group. Alpha is .05 by default,
                        alternative values may be entered by user
anova2x2                Compute power for a Two by Two Between Subjects
                        ANOVA. Takes means, sds, and sample sizes for
                        each group. Alpha is .05 by default,
                        alternative values may be entered by user
anova2x2_se             Compute power for Simple Effects in a Two by
                        Two Between Subjects ANOVA with two levels for
                        each factor. Takes means, sds, and sample sizes
                        for each group. Alpha is .05 by default,
                        alternative values may be entered by user
corr                    Compute power for Pearson's Correlation Takes
                        correlation and range of values
d_prec                  Compute Precision Analyses for Standardized
                        Mean Differences
depb                    Power for Comparing Dependent Coefficients in
                        Multiple Regression with Two or Three
                        Predictors Requires correlations between all
                        variables as sample size. Means, sds, and alpha
                        are option. Also computes Power(All)
depcorr0                Compute Power for Comparing Two Dependent
                        Correlations, No Variables in Common Takes
                        correlations and range of values. First
                        variable in each pair is termed predictor,
                        second is DV
depcorr1                Compute Power for Comparing Two Dependent
                        Correlations, One Variable in Common Takes
                        correlations and range of values
indR2                   Power for Comparing Independent R2 in Multiple
                        Regression with Two or Three Predictors
                        Requires correlations between all variables as
                        sample size. Means, sds, and alpha are option.
                        Also computes Power(All)
indb                    Power for Comparing Independent Coefficients in
                        Multiple Regression with Two or Three
                        Predictors Requires correlations between all
                        variables as sample size. Means, sds, and alpha
                        are option. Also computes Power(All)
indcorr                 Compute Power for Comparing Two Independent
                        Correlations Takes correlations and range of
                        values
indt                    Compute power for an Independent Samples t-test
                        Takes means, sds, and sample sizes for each
                        group. Alpha is .05 by default, alternative
                        values may be entered by user
lmm1F                   Compute power for a One Factor Within Subjects
                        Linear Mixed Model with up to four levels.
                        Takes means, sds, and sample sizes for each
                        group. Alpha is .05 by default, alternative
                        values may be entered by user
lmm1Ftrends             Compute power for a One Factor Within Subjects
                        LMM Trends with up to four levels. Takes means,
                        sds, and sample sizes for each group. Alpha is
                        .05 by default, alternative values may be
                        entered by user
lmm1w1b                 Compute power for a One Factor Within Subjects
                        and One Factor Between LMM with up to two by
                        four levels (within). Takes means, sds, and
                        sample sizes for each group. Alpha is .05 by
                        default, alternative values may be entered by
                        user
lmm2F                   Compute power for a Two Factor Within Subjects
                        Using Linear Mixed Models with up to two by
                        four levels. Takes means, sds, and sample sizes
                        for each group. Alpha is .05 by default,
                        alternative values may be entered by user
lmm2Fse                 Compute power for a Two Factor Within Subjects
                        Using Linear Mixed Models with up to two by
                        four levels. Takes means, sds, and sample sizes
                        for each group. Alpha is .05 by default,
                        alternative values may be entered by user
md_prec                 Compute Precision Analyses for Mean Differences
med                     Compute Power for Mediated (Indirect) Effects
                        Requires correlations between all variables as
                        sample size. This approach calculates power for
                        the Sobel test. The medjs function calculates
                        power based on joint significance (recommended)
medjs                   Compute Power for Mediated (Indirect) Effects
                        Using Joint Significance Requires correlations
                        between all variables as sample size. This is
                        the recommended approach for determining power
medjs_paths             Compute Power for Mediated (Indirect) Effects
                        Using Joint Significance Requires paths for all
                        effects (and if 2 mediators, correlation)
                        Standard deviations/variances set to 1.0 so
                        paths are technically standardized
medserial               Compute Power for Serial Mediation Effects
                        Requires correlations between all variables as
                        sample size. This approach calculates power for
                        the serial mediation using joint significance
                        (recommended)
medserial_paths         Compute Power for Serial Mediation Effects
                        Requires correlations between all variables as
                        sample size. This approach calculates power for
                        the serial mediation using joint significance
                        (recommended) and path coefficients
modmed14                Compute Power for Conditional Process Model 14
                        Joint Significance Requires correlations
                        between all variables as sample size. This is
                        the recommended approach for determining power
modmed7                 Compute Power for Model 7 Conditional Processes
                        Using Joint Significance Requires correlations
                        between all variables as sample size Several
                        values default to zero if no value provided
                        This is the recommended approach for
                        determining power
pairt                   Compute power for a Paired t-test Takes means,
                        sd, and sample sizes. Alpha is .05 by default,
                        alternative values may be entered by user.
                        correlation (r) defaults to .50.
prop1                   Compute power for a single sample proportion
                        test Takes phi, degrees of freedom, and a range
                        of sample sizes. Alpha is .05 by default,
                        alternative values may be entered by user
propind                 Compute power for Tests of Two Independent
                        Proportions Takes phi, degrees of freedom, and
                        a range of sample sizes. Alpha is .05 by
                        default, alternative values may be entered by
                        user
r_prec                  Compute Precision Analyses for Correlations
                        This approach simply loops a function from
                        MBESS
regint                  Compute Power for Regression Interaction
                        (Correlation/Coefficient Approach)
regintR2                Compute Power for Regression Interaction (R2
                        Change Approach)
tfromd                  Compute power for a t test using d statistic
                        Takes d, sample size range, type of test, and
                        tails.
win1F                   Compute power for a One Factor Within Subjects
                        ANOVA with up to four levels. Takes means, sds,
                        and sample sizes for each group. Alpha is .05
                        by default, alternative values may be entered
                        by user
win1Ftrends             Compute power for a One Factor Within Subjects
                        Trends with up to four levels. Takes means,
                        sds, and sample sizes for each group. Alpha is
                        .05 by default, alternative values may be
                        entered by user
win1bg1                 Compute power for a One Factor Within Subjects
                        and One Factor Between ANOVA with up to two by
                        four levels (within). Takes means, sds, and
                        sample sizes for each group. Alpha is .05 by
                        default, alternative values may be entered by
                        user
win2F                   Compute power for a Two Factor Within Subjects
                        ANOVA with up to two by four levels. Takes
                        means, sds, and sample sizes for each group.
                        Alpha is .05 by default, alternative values may
                        be entered by user
win2Fse                 Compute power for Simple Effects in Two Factor
                        Within Subjects ANOVA with up to two by four
                        levels. Takes means, sds, and sample sizes for
                        each group. Alpha is .05 by default,
                        alternative values may be entered by user
