Package index • rwf

Compute

compute_ability(): Compute subject ability for thurstonian models
compute_adjustment(): Compute multiple comparison alpha adjustments
compute_aggregate(): Aggregate descriptive statistics by group
compute_alpha_diagnostics(): Item-total correlations and alpha-if-item-removed diagnostics
compute_aov_es(): Effect Sizes for ANOVA Models
compute_cohens_d(): Compute Cohen's D Effect Size
compute_confidence_inteval(): Compute confidence interval
compute_crosstable(): Pairwise cross-tabulation of categorical variables
compute_descriptives(): Descriptive statistics
compute_dissatenuation(): Compute the disattenuation correction for measurement error
compute_dummy_comparisons(): Compute number of dummy comparisons
compute_efa_cfa(): Exploratory and confirmatory factor analysis for each dimension
compute_frequencies(): Frequency table for categorical variables
compute_friedman_test(): Friedman Test with Effect Size
compute_icc_thurstonian(): Compute item characteristic curves for thurstonian models
compute_info_1pl(): Compute item information for 1PL model
compute_info_2pl(): Compute item information for 2PL model
compute_info_3pl(): Compute item information for 3PL model
compute_kruskal_wallis_test(): Kruskal-Wallis Test with Effect Sizes
compute_kurtosis(): Compute kurtosis of a numeric vector
compute_map(): Simulate prior distribution
compute_mean_sd_alpha(): Mean and SD of scale scores
compute_moving_average(): Centered moving average
compute_one_way_test(): One-Way ANOVA with Effect Sizes and Power
compute_posthoc(): Tukey and Games-Howell Post Hoc Tests
compute_power_r(): Compute r power curve
compute_power_r_matrix(): Compute correlation matrix
compute_raw_alpha(): Cronbach's alpha (raw)
compute_residual_stats(): Residuals for matrices
compute_scores(): Compute subject ability for thurstonian models
compute_se_theta(): Compute the SE of theta
compute_shrout(): Shrout-Fleiss reliability coefficients
compute_skewness(): Compute skewness of a numeric vector
compute_solve(): Solve Linear Systems or Invert a Matrix (Gauss-Jordan)
compute_standard(): Compute standard scores from a numeric vector
compute_standard_error(): Compute the standard error of the mean
compute_text_similarity(): Text similarity measures
compute_tversky_index(): Compute the Tversky index
compute_unidimensional_ability(): Compute theta for unidimensional models
compute_unidimensional_theta(): Compute theta for unidimensional models
compute_wilcoxon_effect_size(): Compute Wilcoxon Effect Size
compute_y_logistic(): Compute y for logistic function

Excel output

excel_confusion_matrix(): Write matrix or dataframe to excel sheet
excel_critical_value(): Write a data frame to Excel with per-column conditional formatting thresholds
excel_generic_format(): Format an Excel worksheet with styles, comments, and frozen panes
excel_matrix(): Write a matrix or data frame to an Excel worksheet with optional conditional formatting

Plot

plot_acf(): Autocorrelation, autocovariance, and partial autocorrelation plot
plot_boxplot(): Side-by-side boxplots for all numeric columns
plot_cfa(): Batch-plot CFA across layouts and display modes
plot_cfa_gg(): Plot CFA model (semPlot-free)
plot_confusion(): Plot confusion matrix
plot_confusion_extended(): Plot a confusion matrix with the full set of derived measures
plot_corrplot(): Correlation matrix plots
plot_crosstable(): Bubble plots for pairwise cross-tabulations
plot_duplicate_y_axis(): Duplicate the y axis on the right side of a ggplot
plot_hinvert_title_grob(): Invert a title grob horizontally
plot_histogram(): Histograms per numeric column
plot_icc_thurstonian(): Plot thurstonian icc
plot_interaction(): Plot two-way interaction graphs for all IV pair and DV combinations
plot_irt_onefactor(): Return data for irt plots
plot_loadings(): Plot loadings
plot_logistic_model(): Logistic model plot
plot_mosaic(): Mosaic plots for pairwise categorical variables
plot_mtmm(): Multitrait-multimethod (MTMM) matrix plot
plot_multiplot(): Arrange multiple ggplot objects in a grid layout
plot_normality_diagnostics(): Normality diagnostic plots (histogram, density, boxplot, Q-Q)
plot_oneway(): Plot group means with error bars for all IV-DV combinations
plot_oneway_diagnostics(): Diagnostic plots for one-way ANOVA models
plot_outlier(): Dot plot of outliers by detection method
plot_qq(): Q-Q plots against the normal distribution
plot_response_frequencies(): Horizontal bar charts of response frequencies
plot_roc(): Plot Receiver Operating Characteristic (ROC) curve
plot_scatterplot(): Scatter plots for all variable pairs in a data frame
plot_scree(): Scree plot displaying the Kaiser and Jolife criteria for factor extraction
plot_separability(): Plot separability
plot_trees_xgboost(): Plot trees for xgboost::xgb.train
plot_ts(): Line plot for a time series

Report

report_alpha(): Cronbach's alpha reliability report for multiple scales
report_cfa(): Report
report_choric_serial(): Report polychoric tetrachoric polyserial biserial correlation
report_correlation(): Report correlation matrix
report_dataframe(): Write matrix or dataframe to excel sheet
report_efa(): Output EFA model
report_factorial_anova(): Factorial, Repeated Measures and Mixed ANOVA Report
report_hlr(): Report HLR
report_irt(): Output for irt model
report_lda(): Report for MASS::lda
report_logistic(): Report logistic regression
report_manova(): MANOVA Report
report_normality_tests(): Battery of normality tests
report_oneway(): One-Way ANOVA Report for Several Variables
report_pdf(): Save or display a list of plots as a multi-page PDF
report_regression(): Regression
report_ttests(): Run Pairwise t-tests and Return a Reporting Table
report_wtests(): Run Pairwise Wilcoxon Tests and Return a Reporting Table
report_xgboost(): Report for xgboost::xgb.train

Strings

str_aes(): Clean and format string aesthetics
str_count(): Count the number of pattern matches in a string
str_mgsub(): Apply gsub for multiple patterns with a single replacement
str_pad(): Pad a string to a minimum width
str_proper(): Convert a string to proper case
str_replace(): Replace the first pattern match in a string
str_replace_all(): Replace all pattern matches in a string
str_split(): Split a string vector into a data frame of parts
str_split_df(): Split a string column or row names in a data frame into separate columns
str_split_fixed(): Split strings into a fixed-width matrix of pieces
str_squish(): Remove leading, trailing, and internal extra whitespace
str_sub(): Extract n characters from the left or right of a string
str_trim_df(): Trim whitespace from all character cells in a data frame
str_wrap(): Wrap long strings to a specified line width

Generate

generate_comparisons_matrix(): Generate comparisons matrix
generate_correlation_matrix(): Generate a data frame with a predetermined correlation structure
generate_data(): Generate a data frame of random numbers
generate_factor(): Generate a data frame of random factor vectors
generate_matrix_A(): Generate Matrix A
generate_matrix_lambda_hat(): Generate matrix lambda for specified number of comparisons
generate_missing(): Introduce missing values into a vector or data frame
generate_multiple_response_vector(): Generate a multiple response vector
generate_string(): Generate random strings
generate_unique_comparisons_index(): Generate index for unique comparisons

Other

c_bind(): Column-bind data frames or vectors of unequal lengths
call_to_string(): Convert a model call to a compact string
cdf(): Check dataframe
cdff(): Check dataframe (optimised)
cfa_icc_index(): index of items to convert from lavaan to thurstonian order for analysis
change_data_type(): Convert column data types in a data frame
check_heywood(): Check for Heywood Cases and Related SEM Estimation Problems
clear_stopwords(): Remove stopwods
clear_text(): Clear text
combine_files(): Combine all files in a directory into a single file
comparison_combinations(): All pairwise column name combinations
confusion(): Create a confusion matrix from observed and predicted vectors
confusion_matrix_percent(): Confusion matrix with row and column percent
convert_excel_unix_timestamp(): Convert UNIX EXCEL timestamp
data_frame_index(): dataframe index
decompose_datetime(): Decompose datetime objects to dataframe collumns
deg2rad(): Convert degrees to radians
detach_package(): Detach and unload a package
display_upper_lower_triangle(): Combine the upper triangle of one matrix with the lower triangle of another
drop_levels(): Drop unused factor levels and collapse rare levels into "Other"
dummy_arrange(): Dummy-code a multiple response vector into a binary data frame
dotnames(): Get the names of objects passed through dots
padNA(): Pad a data frame to a target number of rows with NAs
environment_options(): Load environment options
extract_components(): Extract and plot variance components from a mixed model
extract_tirt_params(): Extract Thurstonian IRT Parameters from a lavaan-Fitted Model
fixed(): Mark a pattern as a fixed string
flatten_list(): Flatten a two-dimensional list into a data frame
get_script_directory(): Get script directory
getfwp(): Get the file path of the currently running script
icc_cfa(): Select responses for each dimension
increase_index(): index dataframe picks
install_all_packages(): Install all missing CRAN packages
install_load(): Install and load multiple packages
k_fold(): K-Fold train test sampling
k_sample(): Train test sampling
key_to_cfa_model(): Convert a key list to a lavaan CFA model string
matrix_triangle(): Keep the lower or upper triangle of a matrix
min_max_index(): Indices of the minimum and maximum values in a vector
model_loadings(): Pattern and structure matrix
name_triplet_pairs(): Create Pair Labels from Consecutive Triplets of Items
off_diagonal_index(): Row and column indices around the diagonal of a square matrix
outlier_summary(): Percentage of outliers at three z-score thresholds
output_compare_model_logistic(): Compare logistic regression models models
output_separator(): Print a formatted console output block with separators
proportion_accurate(): Proportion overall accuracy of a confusion matrix
questions_by_keys(): Convert a key vector to a list of question indices by dimension
questions_dimensions_dataframe(): Build a question-to-dimension mapping table
rad2deg(): Convert radians to degrees
rank3_to_triplets(): Convert thurstonian binary triplets to scale
rank_df_to_binary(): Convert scale to thurstonian binary with n items per block and n blocks
rank_to_binary(): Convert scale to thurstonian binary with n items per ranking block
rbind_all(): Row-bind two data frames with different column sets
recode_scale_dummy(): Scale and dummy code
remove_nc(): Replace and remove non-computable values
remove_outliers(): Replace outliers with NA using IQR fences
remove_user_packages(): Remove all user-installed packages
replace_na_with_previous(): Last observation carried forward (LOCF) imputation
response_dimension(): index parameter and items relative to their dimensions
response_frequency(): Response frequency table for ordinal or Likert-scale variables
result_confusion_performance(): Plot performance of confusion matrix for different cut off points
round_dataframe(): Round numeric columns in a data frame
score_tirt(): Score Multiple Thurstonian IRT Response Patterns (MAP / EBM)
score_tirt_pattern(): Score a Single Thurstonian IRT Response Pattern (MAP / EBM)
simulate_cfa_fit(): Simulate CFA model fit across sample sizes
simulate_correlation_from_sample(): Simulate data preserving the correlation structure of an input data frame
stat_word_char(): Text similarity measures
swap(): Reverse-score a numeric vector
symmetric_matrix(): Make a matrix symmetric by mirroring one triangle
tag_pos(): Part of speech tagging
ts_smoothing(): Time series smoothing with multiple bandwidth levels
write_txt(): Print an object and optionally save output to a log file

Datasets

df_admission: Admission Data
df_automotive_data: Automotive Data
df_blood_pressure: Blood Pressure Data
df_co2: Carbon Dioxide Uptake in Grass Plants
df_crop_yield: Crop Yield Data
df_difficile: Difficile Data
df_insurance: Insurance Data
df_ocean: Big Five Personality Test Dataset
df_personality: Big Five Inventory (BFI-44) Personality Dataset
df_responses: Young People Survey Responses
df_responses_state: Responses State Data
df_sexual_comp: Sexual Compatibility Data
df_titanic: Titanic Dataset