Package index • DMAR

Effect sizes

Point estimates of standardized effects, their expectations, and corrections.

smd(): Standardized Mean Difference
smd_c(): Standardized Mean Difference Using the Control Group as the Basis of Standardization
smd_trimmed(): Robust Standardized Mean Difference (Algina-Keselman-Penfield)
eta_squared(): Eta Squared (Effect Size for ANOVA)
eta_squared_generalized(): Generalized Eta Squared (Effect Size for ANOVA, Comparable Across Designs)
eta_squared_partial(): Partial Eta Squared (Effect Size for ANOVA)
omega_squared(): Omega Squared (Effect Size for ANOVA)
omega_squared_partial(): Partial Omega Squared (Effect Size for ANOVA)
cohen_f(): Cohen's f Effect Size
cohen_h(): Cohen's h Effect Size for a Difference Between Two Proportions
cles(): Common-Language Effect Size (McGraw & Wong, 1992)
cliff_delta(): Cliff's \(\delta\) Ordinal Effect Size
vargha_delaney_A(): Vargha and Delaney's A (Stochastic-Superiority Effect Size)
proportion_of_superiority(): Proportion of Superiority (Sometimes Called Cohen's \(U_3\))
probability_of_superiority_paired(): Probability of Superiority for a Paired-Samples Design
nnt_from_smd(): Number Needed to Treat (NNT) From Cohen's d
responder_analysis(): Responder Analysis: Who Cleared the Threshold, by Group
correction_for_attenuation(): Correct a Correlation for Attenuation Due to Measurement Error
signal_to_noise_R2(): Signal to Noise Estimators for the Squared Multiple Correlation Coefficient
expected_R2(): Expected Value of the Squared Multiple Correlation Coefficient
expected_partial_r(): Exact Expected Value of the Sample Partial Correlation
expected_r(): Exact Expected Value of the Sample Pearson Correlation Given \(\rho\) and \(n\)
expected_smd(): Exact Expected Value of Cohen's d (and Hedges' g Bias Correction)
unbiased_R2(): Unbiased and Adjusted Estimators of the Population Squared Multiple Correlation
sd_unbiased(): Unbiased Estimate of the Population Standard Deviation Under Normality
descriptives(): Descriptive Statistics for One or More Variables
skewness(): Bias-Corrected Sample Skewness
kurtosis(): Bias-Corrected Sample Excess Kurtosis

Confidence intervals

Confidence intervals for effect sizes and related quantities.

ci_R2(): Confidence Interval for the Population Squared Multiple Correlation Coefficient
ci_c(): Confidence Interval for a Contrast in a Fixed Effects ANOVA
ci_c_ancova(): Confidence Interval for an (Unstandardized) Contrast in ANCOVA With One Covariate
ci_c_ancova_bp(): Bryant–Paulson Simultaneous Confidence Intervals for Contrasts of Adjusted Means in ANCOVA
ci_r() ci_R(): Confidence Intervals for the Population Correlation and Multiple Correlation
ci_cv(): Confidence Interval for the Coefficient of Variation
ci_dunnett(): Dunnett's Simultaneous Confidence Intervals Against a Control
ci_eigenvalue(): Confidence Interval on the Largest Eigenvalue of a Sample Covariance Matrix
ci_eta_squared(): Confidence Interval for Eta Squared (Effect Size for ANOVA)
ci_eta_squared_generalized(): Confidence Interval for Generalized Eta Squared (Approximate)
ci_eta_squared_partial(): Confidence Interval for Partial Eta Squared (Effect Size for ANOVA)
ci_games_howell(): Provides Games–Howell Simultaneous Confidence Intervals for All Pairwise Comparisons Without Assuming Homogeneity of Variance
ci_mahalanobis(): Confidence Interval for the Squared Mahalanobis Distance
ci_omega_squared(): Confidence Interval for Omega Squared (Effect Size for ANOVA)
ci_proportion(): Confidence Interval for a Single Proportion
ci_pvaf(): Confidence Interval for the Proportion of Variance Accounted for (in the Dependent Variable by Knowing the Levels of the Factor)
ci_rc(): Confidence Interval for an Unstandardized Regression Coefficient
ci_reg_coef(): Confidence Interval for a Regression Coefficient, Raw or Standardized
ci_rmsea(): Confidence Interval for the Population Root Mean Square Error of Approximation
ci_sc(): Confidence Interval for a Standardized Contrast in a Fixed Effects ANOVA
ci_sc_ancova(): Confidence Interval for a Standardized Contrast in ANCOVA With One Covariate
ci_scheffe(): Scheffe-Adjusted Simultaneous Confidence Intervals for Contrasts
ci_sm(): Confidence Interval for the Standardized Mean
ci_smd(): Confidence Interval for the Standardized Mean Difference (Two Independent Groups)
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
ci_tukey_kramer(): Tukey-Kramer Simultaneous Confidence Intervals for Pairwise Contrasts

Sample size planning, accuracy in parameter estimation (AIPE)

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_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_c_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for an Unstandardized Contrast
ss_aipe_cliff_delta(): Sample Size for AIPE on Cliff's \(\delta\)
ss_aipe_cliff_delta_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for Cliff's Delta
ss_aipe_composite_sem(): Sample Size for Accurate Estimation of a Set of SEM Parameters
ss_aipe_crd_n_clusters_fixed_width() ss_aipe_crd_n_individuals_fixed_width() ss_aipe_crd_n_clusters_fixed_budget() ss_aipe_crd_n_individuals_fixed_budget() ss_aipe_crd_both_fixed_budget() ss_aipe_crd_both_fixed_width(): Find Target Sample Sizes for the Accuracy in Unstandardized Conditions Means Estimation in CRD
ss_aipe_crd_es_n_clusters_fixed_width() ss_aipe_crd_es_n_individuals_fixed_width() ss_aipe_crd_es_n_clusters_fixed_budget() ss_aipe_crd_es_n_individuals_fixed_budget() ss_aipe_crd_es_both_fixed_budget() ss_aipe_crd_es_both_fixed_width(): Find Target Sample Sizes for the Accuracy in Standardized Conditions Means Estimation in CRD
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 From the Accuracy in Parameter Estimation Perspective for the Coefficient of Variation
ss_aipe_equivalence_r(): AIPE Sample Size Planning for an Equivalence Test on the Pearson Correlation
ss_aipe_equivalence_r_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for an Equivalence-Test Correlation
ss_aipe_equivalence_smd(): AIPE Sample Size Planning for an Equivalence Test on the Standardized Mean Difference
ss_aipe_equivalence_smd_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for an Equivalence-Test SMD
ss_aipe_icc(): Sample Size for AIPE on an Intraclass Correlation Coefficient
ss_aipe_icc_sensitivity(): Sensitivity Analysis for Sample Size Planning From the Accuracy in Parameter Estimation Perspective for an Intraclass Correlation Coefficient
ss_aipe_indirect_effect(): Sample Size for AIPE on a Mediated (Indirect) Effect \(ab\)
ss_aipe_indirect_effect_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for an Indirect Effect
ss_aipe_mixed_effects(): AIPE Sample Size Planning for a Fixed Effect in a Two-Level Mixed-Effects Model
ss_aipe_mixed_effects_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Mixed-Effects Fixed Effect
ss_aipe_omega_squared(): Sample Size for AIPE on Omega Squared (ANOVA Effect Size)
ss_aipe_omega_squared_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for Omega Squared
ss_aipe_partial_r(): Sample Size for AIPE on a Partial Correlation
ss_aipe_partial_r_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Partial Correlation
ss_aipe_pcm(): Sample Size Planning for Polynomial Change Models in Longitudinal Study
ss_aipe_pcm_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Polynomial Change Parameter
ss_aipe_r(): Sample Size for AIPE on a Pearson Correlation
ss_aipe_r_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Pearson Correlation
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 Planning From the Accuracy in Parameter Estimation Perspective for the Unstandardized Regression Coefficient
ss_aipe_reg_coef(): Sample Size Planning for a Single Regression Coefficient (AIPE)
ss_aipe_reg_coef_sensitivity(): Sensitivity Analysis for Sample Size Planning From the Accuracy in Parameter Estimation Perspective for the (Standardized and Unstandardized) Regression Coefficient
ss_aipe_reliability(): Sample Size Planning for Accuracy in Parameter Estimation for Reliability Coefficients
ss_aipe_reliability_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Reliability Coefficient
ss_aipe_rmsea(): Sample Size Planning for RMSEA in SEM
ss_aipe_rmsea_sensitivity(): A Priori Monte Carlo Simulation for Sample Size Planning for RMSEA in SEM
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_sem_path(): Sample Size Planning for SEM Targeted Effects
ss_aipe_sem_path_sensitivity(): A Priori Monte Carlo Simulation for Sample Size Planning for SEM Targeted Effects
ss_aipe_semipartial_r(): Sample Size for AIPE on a Semipartial (Part) Correlation
ss_aipe_semipartial_r_sensitivity(): Sensitivity Analysis for Sample Size Planning From the AIPE Perspective for a Semipartial Correlation
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 (AIPE)
ss_aipe_smd_sensitivity(): Sensitivity Analysis for Sample Size Given the Accuracy in Parameter Estimation Approach for the Standardized Mean Difference
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 Planning From the Accuracy in Parameter Estimation Perspective for the Standardized Regression Coefficient

Sample size planning, power

ss_power_R2(): Plan Sample Size to Make the Test of the Squared Multiple Correlation Coefficient Sufficiently Powerful
ss_power_R2_sensitivity(): Sensitivity Analysis for Sample Size Planning to Make the Omnibus Test of \(R^2\) Sufficiently Powerful
ss_power_c(): Sample Size or Power for an Unstandardized Contrast in a One-Way Between-Subjects ANOVA
ss_power_c_ancova(): Sample Size or Power for an Unstandardized Contrast in a One-Way ANCOVA
ss_power_composite_ancova(): Sample Size or Composite Power for a One-Way or Factorial ANCOVA
ss_power_composite_ancova_2group() plot(<dmar_composite_power>): Sample Size or Composite Power for a Two-Group ANCOVA With a Covariate
ss_power_composite_anova(): Sample Size or Composite Power for a One-Way or Factorial ANOVA
ss_power_composite_factorial_ancova() plot(<dmar_composite_power_factorial>): Sample Size or Composite Power for a Factorial ANCOVA
ss_power_composite_factorial_ancova_het() plot(<dmar_composite_power_factorial_het>): Composite Power for a Factorial ANCOVA With Heterogeneous Slopes
ss_power_composite_factorial_anova(): Sample Size or Composite Power for a Factorial ANOVA
ss_power_composite_sem(): Sample Size or Composite Power for a Set of SEM Parameters
ss_power_contrast(): Sample Size and Statistical Power for a Contrast in a Fixed-Effects ANOVA
ss_power_equivalence_c(): Sample Size for Equivalence or Noninferiority of a Linear Contrast
ss_power_factorial_ancova(): Sample Size Planning for Power in Factorial ANCOVA
ss_power_factorial_anova(): Sample Size or Power for a Factorial Between-Subjects ANOVA Effect
ss_power_indirect_effect(): Sample Size Planning for Power for the Indirect (Mediation) Effect
ss_power_mixed_effects(): Sample Size or Power for a Treatment Effect in a Two-Level Mixed-Effects Model
ss_power_one_way_anova(): Sample Size or Power for a One-Way Between-Subjects ANOVA Omnibus F Test
ss_power_pcm(): Sample Size Planning for Power for Polynomial Change Models
ss_power_r(): Sample Size or Power for a Pearson Correlation Coefficient (Fisher Z Transformation)
ss_power_rc(): Sample Size for a Targeted Regression Coefficient
ss_power_reg_coef(): Sample Size for a Targeted Regression Coefficient
ss_power_reg_coef_sensitivity(): Sensitivity Analysis for the Power of a Targeted Regression Coefficient
ss_power_rm_anova(): Sample Size or Power for a One-Way Repeated Measures ANOVA Omnibus F Test
ss_power_sc(): Sample Size or Power for a Standardized Contrast in a One-Way Between-Subjects ANOVA
ss_power_sem(): Sample Size Planning for Structural Equation Modeling From the Power Analysis Perspective
ss_power_smd(): Sample Size or Power for a Standardized Mean Difference (Two Independent Groups)
ss_power_split_plot_anova(): Sample Size or Power for a Mixed-Effects ANOVA (Between X Within Design)
power_equivalence_c(): Power of the TOST or Noninferiority Test for a Linear Contrast
power_equivalence_md(): Power of the Two One-Sided Tests Procedure (TOST) for Equivalence
power_equivalence_md_plot(): Plot TOST Equivalence-Test Power Curves Over a Range of True Differences
power_density_equivalence_md(): Density Underlying the TOST Power Calculation
power_fisher_exact(): Power of Fisher's Exact Test (Noncentral Hypergeometric)

Design analysis and design utilities

design_consequences(): Consequences of a Design: Power, Sign and Magnitude Errors, and Expected Precision
design_effect(): Kish's Design Effect (DEFF), DEFT, and the Effective Sample Size
effects_coding(): Effects-Coding Contrast Matrix for a Factor
helmert_coding(): Helmert-Coding Contrast Matrix for a Factor
orthogonal_polynomial(): Orthogonal-Polynomial (Trend) Contrast Coefficients
is_orthogonal_set(): Check Whether a Set of Contrasts Is Mutually Orthogonal

Minimum risk and sequential estimation

mr_smd(): Minimum Risk Point Estimation of the Population Standardized Mean Difference
mr_cv(): Minimum Risk Point Estimation of the Population Coefficient of Variation
ss_seq_c(): Sequential Sample Size for a Fixed-Width Contrast Interval
ss_seq_c_sensitivity(): Monte Carlo Sensitivity of the Sequential Fixed-Width Procedure

Equivalence testing

equivalence_smd(): Equivalence Test for the Standardized Mean Difference via Two One-Sided Tests (TOST)
equivalence_r(): Equivalence Test for the Pearson Correlation via Two One-Sided Tests (TOST)
equivalence_c(): Equivalence and Noninferiority Tests for a Linear Contrast via Two One-Sided Tests (TOST)

Reliability and agreement

reliability(): Reliability Coefficient With a Confidence Interval (General Dispatch)
reliability_H(): Maximal Reliability Coefficient H (Hancock & Mueller, 2001)
reliability_alpha(): Coefficient Alpha With a Confidence Interval
reliability_kr20(): Kuder-Richardson Formula 20 (KR-20) With a Confidence Interval
reliability_omega(): Coefficient Omega (McDonald) With a Confidence Interval
reliability_omega_categorical(): Categorical Omega for Ordered-Categorical Items, With a Confidence Interval
icc(): Intraclass Correlation Coefficients With Confidence Intervals
icc_lmer(): Intraclass Correlation From a Fitted lme4 Mixed-Effects Model
R2_mixed_effects(): Marginal and Conditional \(R^2\) for a Mixed-Effects Model
R2_mixed_effects_decomposition(): R-Squared Measures for Mixed-Effects Models
regions_of_significance(): Regions of Significance for a Covariate by Group Interaction
cohen_kappa(): Cohen's Kappa Coefficient of Inter-Rater Agreement
fleiss_kappa(): Fleiss's Kappa for Inter-Rater Agreement Among Multiple Raters
krippendorff_alpha(): Krippendorff's \(\alpha\) Inter-Rater Agreement
gwet_ac(): Gwet's AC1 and AC2 Chance-Corrected Agreement Coefficients
lin_ccc(): Lin's Concordance Correlation Coefficient
limits_of_agreement() loa(): Limits of Agreement (Bland-Altman) With Confidence Intervals on the Limits
content_validity_index(): Content Validity Index From Expert Ratings

ANOVA, ANCOVA, and contrasts

ancova(): Analysis of Covariance (ANCOVA)
factorial_anova(): Between-Subjects Factorial ANOVA for Unbalanced Designs
anova_within(): One Way Within-Subjects ANOVA With Sphericity Diagnostics and Corrections
anova_within_two_way(): Two-Factor Within-Subjects ANOVA With Sphericity Adjustments
mixed_anova(): Mixed-Model ANOVA F-Ratios for One- and Two-Way Designs
manova_split_plot(): Mixed-Design Multivariate ANOVA With All Four Test Statistics
pairwise_within(): Paired Pairwise Comparisons With Multiple-Comparison Adjustment
simple_effects_AB(): Simple Effect F Tests for a Two-Factor Between-Subjects Design
contrast_test(): Tests One or More Contrasts of Group Means in a One-Way Design
contrast_adjusted(): Confidence Interval for a Contrast of Covariate-Adjusted Cell Means in a Factorial ANCOVA
adjusted_means(): Adjusted Cell and Marginal Means From a Fitted Linear Model
dunn_test(): Provides Dunn's Rank-Sum Test of All Pairwise Differences Following a Kruskal–Wallis Test
obrien_test(): O'Brien's Test for Homogeneity of Variance
mauchly_test(): Mauchly's Test of Sphericity for a One-Way Within-Subjects Design
epsilon_corrections(): Greenhouse-Geisser, Huynh-Feldt, and Lower-Bound Epsilon Corrections
welch_t(): Welch's Separate-Variance t Test
summary_t_test(): Two-Sample t Test From Summary Statistics
correlations_test(): Formatted Correlation Matrix With p-values and Confidence Intervals
randomization_test(): Randomization (Permutation) Test for Two Independent Groups
randomization_test_paired(): Paired-Samples Randomization (Sign-Flip) Test

Meta-analysis and evidence synthesis

meta_contrast(): Contrast Among Study Effect Sizes (Rosenthal-Rubin)
meta_es(): Random Effects Meta-Analysis of Generic Effect Sizes
meta_r(): Random Effects Meta-Analysis of Correlations
meta_smd(): Random Effects Meta-Analysis of Standardized Mean Differences
combine_p(): Combine Independent P-Values Across Studies

Bayesian analyses

bayes_independent_t(): Bayesian Independent-Samples t Analysis
bayes_one_sample_t(): Bayesian One-Sample t Analysis
bayes_paired_t(): Bayesian Paired-Samples t Analysis

Mediation

mediate(): Mediation Analysis With Bootstrap Confidence Intervals
mediation_mbco(): Mediation Analysis via Model-Based Constrained Optimization

Longitudinal change

analysis_of_change(): Analysis of Change: Fit Change Models to One or Many Trajectories

Maximum likelihood regression (FIML)

mlmr(): Maximum Likelihood Multiple Regression
mlmr_mv(): Multivariate Maximum Likelihood Regression With Full Information Missing Data Handling
tidy(<mlmr_mv>) glance(<mlmr_mv>): A Multivariate FIML Regression Fit
anova(<mlmr_mv>): Compare Nested Multivariate FIML Regression Fits

Multivariate and latent variable methods

cfa_1(): One Factor Confirmatory Factor Analysis Model
cfa_2(): Two Factor Confirmatory Factor Analysis Model
cfa_k(): Multiple-Factor Confirmatory Factor Analysis Model
compare_cov_structures(): Likelihood-Ratio Comparison of Covariance Structures
cov_sem(): Model Implied Covariance Matrix From a Lavaan-Specified SEM
covmat_from_cfa(): Generate a Population Covariance Matrix From a One-Factor Confirmatory Factor Model
procrustes_phi(): Tucker's Congruence Coefficient \(\phi\) (Factor Similarity)
variance_components_mls(): Modified-Large-Sample Confidence Intervals on Variance Components
measurement_invariance(): Measurement Invariance Across Groups
htmt(): Heterotrait-Monotrait Ratio of Correlations (HTMT)
average_variance_extracted(): Average Variance Extracted (AVE)
bifactor_indices(): Bifactor Model Dimensionality and Reliability Indices
common_method_marker(): Marker-Variable Adjustment for Common Method Variance
common_method_single_factor(): Single-Common-Factor Screen for Common Method Variance
ecvi(): Expected Cross-Validation Index (ECVI) for a Covariance-Structure Model
simple_structure(): Quantify Simple Structure in a Factor Loading Matrix
measurement_alignment(): Approximate Measurement Invariance by Factor Alignment
dmacs(): The dMACS Effect Size of Measurement Noninvariance
irt_grm(): Graded Response Model for Ordered Categorical Items
irt_information(): Item and Test Information for the Graded Response Model

Critical values

cv_bonferroni_f(): Provides the Bonferroni-Adjusted Critical Value for an F Test of One of Several Contrasts
cv_bryant_paulson(): Provides the Critical Value for the Bryant–Paulson ANCOVA Multiple-Comparison Procedure
cv_chisq(): Provides the Critical Value(s) for a Chi Square Distribution
cv_dunnett(): Provides the Critical Value for Dunnett's Many-to-One Comparisons Procedure
cv_f(): Provides the Critical Value(s) for an F Distribution
cv_scheffe(): Provides the Critical Value for the Scheffé Procedure
cv_smm(): Provides the Critical Value of the Studentized Maximum Modulus Distribution
cv_t(): Provides the Critical Value(s) for a t-distribution
cv_tukey_hsd(): Provides the Critical Value for the Tukey Honestly Significant Difference (HSD) Test
cv_z(): Provides the Critical Value(s) for the Standard Normal Distribution (the z-distribution, With Mean 0 and Variance 1)
cv(): Coefficient of Variation (Biased or Unbiased Estimator)
pbryant_paulson() qbryant_paulson() dbryant_paulson(): The Bryant–Paulson Generalized Studentized Range Distribution

Parameterization conversions

convert_F_chisq() convert_chisq_F(): Convert Between an F Value and a Chi Square Value
convert_R2_f() convert_f_R2() convert_lambda_R2() convert_R2_lambda(): Convert Between F, \(R^2\), and Their Noncentral Parameters
convert_Z_r(): Convert Fisher's Z Into the Scale of a Correlation Coefficient (r)
convert_cor_cov(): Correlation Matrix to Covariance Matrix Conversion
convert_d_or() convert_or_d(): Convert Between the Standardized Mean Difference and the Odds Ratio
convert_d_r() convert_r_d(): Convert Between the Standardized Mean Difference and the Correlation
convert_r_Z(): Convert a Correlation Coefficient (r) Into the Scale of Fisher's Z
convert_delta_lambda() convert_lambda_delta(): Conversion Functions for Noncentral t-distribution
convert_z_normal(): Convert a Standard Normal z Value to the Corresponding Value on a Normal Distribution

Variance utilities

var_R2(): Variance of the Squared Multiple Correlation Coefficient
var_alpha(): Asymptotic Variance of Coefficient Alpha (Cronbach, Guttman)
var_cv(): Asymptotic Variance of the Coefficient of Variation
var_ete(): Variance of the Estimated Treatment Effect in Two-Group ANCOVA With Heterogeneous Slopes
var_icc(): Asymptotic Variance of the Intraclass Correlation Coefficient
var_indirect_effect(): Variance of the Mediated (Indirect) Effect \(ab\)
var_omega_squared(): Asymptotic Variance of Omega Squared (ANOVA Effect Size)
var_partial_r(): Asymptotic Variance of the Partial Correlation Coefficient
var_r(): Asymptotic Variance of the Pearson Correlation Coefficient
var_semipartial_r(): Asymptotic Variance of the Semipartial (Part) Correlation Coefficient
var_smd(): Variance of Cohen's d and Hedges' g
var_smd_trimmed(): Asymptotic Variance of the Robust Trimmed SMD

Noncentral distributions

moments_nc_chisq(): Moments of the Noncentral Chi Square Distribution
moments_ncf(): Moments of the Noncentral F Distribution
moments_nct(): Moments of the Noncentral t Distribution
conf_limits_nc_chisq(): Confidence Limits for the Noncentrality Parameter of a Noncentral Chi Square Distribution
conf_limits_ncf(): Confidence Limits for the Noncentrality Parameter of a Noncentral F-distribution
conf_limits_nct(): Confidence Limits for a Noncentrality Parameter From a t-distribution

Data simulation

simulate_ancova_data(): Simulate Data From a One-Covariate ANCOVA Model
simulate_ancova_factorial_data(): Simulate Data From a Factorial ANCOVA Design (up to Four Factors, Any Number of Covariates)
simulate_anova_data(): Simulate Data From a One-Way Fixed-Effects ANOVA Model
simulate_longitudinal_gompertz(): Simulate Data From a Gompertz Change (Growth) Model
simulate_longitudinal_logistic(): Simulate Data From a Logistic Change (Growth) Model
simulate_longitudinal_negative_exponential(): Simulate Data From a Negative Exponential (Asymptotic Regression) Change Model
simulate_longitudinal_polynomial(): Simulate Data From a Polynomial Change (Growth) Model
simulate_longitudinal_richards(): Simulate Data From a Richards Change (Growth) Model
simulate_regression_data(): Simulate Data From a Multivariate Normal Multiple-Regression Model

Plots

plot_R2(): Visualize the Proportion of Variance Explained (\(R^2\))
plot_cfa_k(): Plot the Estimates of a Multiple-Factor CFA
plot_ci(): Forest-Plot-Style Confidence Interval Display
plot_equivalence(): Plot Contrasts Against an Equivalence Region
plot_forest(): Forest Plot of Study Effect Sizes With the Pooled Estimate
plot_irt_information(): Plot an Item Response Theory Information Curve
plot_mediation_mbco(): Plot Conditional Effects From a Moderated Mediation Analysis
plot_randomization_test(): Plot the Randomization Distribution Behind a Randomization Test
plot_regions_of_significance(): Plot Regions of Significance for a Covariate by Group Interaction
plot_smd(): Visualize a Standardized Mean Difference With Overlapping Distributions
plot_trajectories(): Visualize Observed Individual Trajectories in a Longitudinal Data Set
plot_trajectories_fitted(): Plot Observed and Fitted Individual Trajectories From a Multilevel Model

Display helpers

format(<dmar_tbl>) print(<dmar_tbl>): Printing for DMAR Result Tables
format_p(): Format p-values for Display the DMAR Way
knit_print(<dmar_tbl>) as_kable() results_sentence(): Publication-Ready Display of DMAR Result Tables
print_anova(): Print a Model Comparison or ANOVA Table With DMAR p-value Formatting
print_summary(): Print a Model Summary With DMAR p-value Formatting
now(): A Friendly Date / Time Stamp and a Simple Stopwatch

Broom tidiers and model methods

tidy() and glance() views of DMAR results, and methods for mlmr fits.

anova(<mlmr>): Likelihood Ratio Test for Nested Mlmr Fits
tidy(<dmar_contrast_test>) glance(<dmar_contrast_test>) tidy(<dmar_ci_long>) glance(<dmar_ci_long>) tidy(<dmar_ci_anova>) glance(<dmar_ci_anova>) tidy(<dmar_post_hoc_ci>) glance(<dmar_post_hoc_ci>) tidy(<dmar_ss_power>) glance(<dmar_ss_power>) tidy(<dmar_ss_aipe>) glance(<dmar_ss_aipe>) tidy(<dmar_tbl>) glance(<dmar_tbl>) tidy(<dmar_content_validity>) glance(<dmar_content_validity>) tidy(<dmar_dmacs>) glance(<dmar_dmacs>) tidy(<dmar_measurement_invariance>) glance(<dmar_measurement_invariance>) tidy(<dmar_measurement_alignment>) glance(<dmar_measurement_alignment>) tidy(<dmar_ss_power_sensitivity>) glance(<dmar_ss_power_sensitivity>): Tidy and Glance Methods for DMAR Result Tables
tidy(<dmar_summary_t_test>) glance(<dmar_summary_t_test>): Broom-Style Tidy / Glance Methods for summary_t_test()
tidy(<dmar_R2_mixed_effects>) glance(<dmar_R2_mixed_effects>): Tidy / Glance Methods for R2_mixed_effects Output
tidy(<dmar_cfa_k>): Tidy a Multiple-Factor CFA Fit
tidy(<dmar_ci_R2>) glance(<dmar_ci_R2>): Tidy / Glance Methods for ci_R2 Output
tidy(<dmar_ci_smd>) glance(<dmar_ci_smd>): Tidy / Glance Methods for ci_smd Output
tidy(<dmar_mediation_mbco>): Tidy an MBCO Mediation Table
tidy(<dmar_reliability>): A Reliability Coefficient Estimate
tidy(<mlmr>): An Mlmr Fit
tidy(<mlmr_mv>) glance(<mlmr_mv>): A Multivariate FIML Regression Fit
tidy(<dmar_welch_t>) glance(<dmar_welch_t>): Broom-Style Tidy / Glance Methods for welch_t()
glance(<dmar_cfa_k>): Glance at a Multiple-Factor CFA Fit
glance(<dmar_mediation_mbco>): Glance at an MBCO Mediation Fit
glance(<dmar_reliability>): Glance at a Reliability Coefficient Estimate
glance(<mlmr>): Glance at an Mlmr Fit

Data sets

bessel_errors: Bessel's (1818) Grouped Frequency Distribution of Bradley's Astronomical Observation Errors
depression_bdi: Depression Treatment Study With a Pretest Covariate
diagnosis_agreement: Cohen's (1968) Psychiatric Diagnosis Agreement Table
drinks_trial: Community Reinforcement Approach Drinking Trial With Homeless Alcohol-Dependent Individuals (Smith, Meyers, & Delaney, 1998)
holzinger_swineford: Holzinger and Swineford (1939) Factor Analysis Study
prime_time_achievement: Indiana Prime Time Third Grade Achievement Evaluation Data
pygmalion: Pygmalion in the Classroom Teacher-Expectancy Data
teacher_expectancy: Teacher Expectancy Meta-Analysis Data (Raudenbush, 1984)
test_market: Controlled Test-Market Experiment (Bryant & Bruvold, 1980)

The package

DMAR-package DMAR dmar: Design, Measurement, and Analysis in R