Package index • scan

Coding and manipulation of single-case data frames

scdf(): Single case data frame

add_l2(): Add level-2 data

as_scdf(): as_scdf

as.data.frame(<scdf>): Creating a long format data frame from several single-case data frames (scdf).

add_dummy_variables(): Add Dummy Variables for Piecewise Linear Models

combine() c(<scdf>): Combine single-case data frames

na.omit(<scdf>): scdf objects Removes any row with a missing value

fetch(): Fetches elements from scan objects Getter function for scan objects

coef(<sc_plm>): Extract coefficients from plm/hplm objects

hplm() print(<sc_hplm>) export(<sc_hplm>) coef(<sc_hplm>): Hierarchical piecewise linear model / piecewise regression

fill_missing(): Replacing missing measurement times in single-case data

sample_names(): Samples random names

select_cases(): Select a subset of cases

select_phases(): Select and combine phases for overlap analyses

set_vars() set_dvar() set_mvar() set_pvar(): Set analysis variables in an scdf

subset(<scdf>): Subset cases, rows, and variables

moving_median() moving_mean() local_regression() set_na_at() center_at() first_of() across_cases() all_cases() rowwise() transform(<scdf>): Transform every single case of a single case data frame

rescale(): Rescales values of an scdf file

is.scdf(): scdf objects Tests for objects of type "scdf"

Shiny apps

shinyscan(): A Shiny app for scan

import_scdf(): Import scdf – RStudio Addin

Export and import

convert(): Convert

read_scdf(): Load single-case data from files

write_scdf(): Data output

Single-case data analysis

anova(<sc_plm>) anova(<sc_hplm>) anova(<sc_mplm>): ANOVA Table for Piecewise Linear Models

autocorr(): Autocorrelation for single-case data

batch_apply(): Apply a function to each element in an scdf.

cdc(): Conservative Dual-Criterion Method

corrected_tau(): Baseline corrected tau

describe(): Descriptive statistics for single-case data

hplm() print(<sc_hplm>) export(<sc_hplm>) coef(<sc_hplm>): Hierarchical piecewise linear model / piecewise regression

mplm() print(<sc_mplm>) export(<sc_mplm>): Multivariate Piecewise linear model / piecewise regression

bplm() print(<sc_bplm>) export(<sc_bplm>): Bayesian Piecewise Linear Model

nap(): Nonoverlap of all Pairs

outlier(): Handling outliers in single-case data

overlap(): Overlap indices for single-case data

ird() print(<sc_ird>): IRD - Improvement rate difference

pand() print(<sc_pand>) export(<sc_pand>): Percentage of all non-overlapping data

pem(): Percent exceeding the median

pet(): Percent exceeding the trend

plm() print(<sc_plm>) export(<sc_plm>): Piecewise linear model / piecewise regression

pnd(): Percentage of non-overlapping data

power_test(): Empirical power analysis for single-case data

rand_test(): Randomization Tests for single-case data

rci(): Reliable change index

smd(): Standardized mean differences

between_smd() print(<sc_bcsmd>) export(<sc_bcsmd>): Between-Case Standardized Mean Difference

tau_u() print(<sc_tauu>) export(<sc_tauu>): Tau-U for single-case data

trend(): Trend analysis for single-cases data

Building graphs, tables etc.

between_smd() print(<sc_bcsmd>) export(<sc_bcsmd>): Between-Case Standardized Mean Difference

bplm() print(<sc_bplm>) export(<sc_bplm>): Bayesian Piecewise Linear Model

export(): Export scan objects to html or latex

hplm() print(<sc_hplm>) export(<sc_hplm>) coef(<sc_hplm>): Hierarchical piecewise linear model / piecewise regression

mplm() print(<sc_mplm>) export(<sc_mplm>): Multivariate Piecewise linear model / piecewise regression

pand() print(<sc_pand>) export(<sc_pand>): Percentage of all non-overlapping data

plm() print(<sc_plm>) export(<sc_plm>): Piecewise linear model / piecewise regression

tau_u() print(<sc_tauu>) export(<sc_tauu>): Tau-U for single-case data

ird() print(<sc_ird>): IRD - Improvement rate difference

print(<scdf>): Print an scdf

plot(<scdf>) plotSC(): (Deprecated) Plot single-case data

style_plot(): (Deprecated) Create styles for single-case data plots

plot_rand(): Plot random distribution

summary(<scdf>): Summary function for an scdf

Simulating data

design(): Generate a single-case design matrix

estimate_design(): Estimate single-case design

random_scdf(): Single-case data generator