Package index • neuropsytools

Data Conversion

Convert and standardise test scores, making them easier to interpret and compare.

convert_z(): Convert Z Score to Alternative Standardized Score.

convert_standard(): Convert Between Standardised Test Scores.

score_descriptor(): #' Generate Descriptive Labels for Standardized Test Scores

prevalence(): Calculate Prevalence

percentile(): Calculate the percentile of a value in a dataset

percentile_vector(): Calculate the percentiles of a vector of values

Dissociations

Analyse single-case dissociations to explore individual cognitive processes.

deficit(): Assessing For a Deficit in Test Score When Compared to a Control Sample.

deficit_bayes(): Bayesian Dissociation Single Case Analysis

discrep(): Assessing for a Frequentist Discrepancy Between Two Test Scores for an Individual Case.

dissociation(): Dissociation Analysis Between Two Test Scores

Supplementary Analyses

Extend your analysis with composite scores and discrepancy assessments.

composite(): Generate Composite Scores

discreps_from_mean(): Calculate score deviances and significance

sem_to_percentiles(): Calculate confidence intervals and percentile ranks

aborm_j_battery(): Calculate the percentage of the normative population with j or more abnormal test scores

mdi(): Calculate the Mahalanobis Distance Index (MDI)

Normative Data

Generate and apply normative data for contextualising test results.

cont_norms_single_pred(): Generate Regression Norms for a Single Variable

percentile_norms(): Calculate Percentile Norms

Error Estimation

Estimate error metrics to assess reliability and precision.

se_difference(): Standard Error of Difference Between Means

se_mean(): Standard Error of the Mean

se_measurement(): Standard Error of Measurement (SEM)

se_n1(): Standard Error for n1

se_prediction(): Standard Error of Prediction

Data Visualisation

Create visualisations like bell curves and forest plots to illustrate results.

plot_bar(): Plot a Bar Plot

plot_bell(): Plot Bell Curve

plot_forest(): Plot a Forest Plot

plot_change(): Plot Stability

plot_theme(): Custom ggplot2 theme

Serial Assessment

Analyse changes over time in longitudinal studies.

predict_t2(): Estimate Neuropsychological Test Scores Based on Earlier Administrations

reliable_change: Discrepncy Between Predicted and Observed Time 2 Test Scores (Reliable Change).

Miscellaneous & Intermediary Functions

Supplementary functions for intervals, corrections, and stability checks.

abnorm_ci_t(): Confidence intervals for the abnormality of a test score using a modified t-test.

sequen_bonf(): Sequential Bonferroni Correction

stability_reg_est(): Regression Based Estimate for Stability