Package index • wisclabmisc

Analyses 🔬

fit_beta_gamlss() fit_beta_gamlss_se() predict_beta_gamlss() optimize_beta_gamlss_slope() uniroot_beta_gamlss(): Fit a beta regression model (for intelligibility)

fit_gen_gamma_gamlss() fit_gen_gamma_gamlss_se() predict_gen_gamma_gamlss(): Fit a generalized gamma regression model (for speaking rate)

GAMLSS helpers ⛑️

mem_gamlss(): Fit a gamlss model but store user data

check_model_centiles() check_computed_centiles(): Compute the percentage of points under each centile line

predict_centiles() pivot_centiles_longer(): Predict and tidy centiles from a GAMLSS model

ROC statistics 🥅

compute_smooth_density_roc(): Create an ROC curve from smoothed densities

compute_empirical_roc(): Create an ROC curve from observed data

compute_predictive_value_from_rates(): Compute positive and negative predictive value

compute_sens_spec_from_ecdf(): Compute sensitivity and specificity scores from (weighted) observed data

trapezoid_auc() partial_trapezoid_auc(): Compute AUCs using the trapezoid method

Other statistics 🔦

fit_kmeans(): Run (scaled) k-means on a dataset.

info_surprisal() info_entropy() info_cross_entropy() info_kl_divergence() info_kl_divergence_matrix(): Compute entropy and related measures

logitnorm_mean(): Compute the mean of logit-normal distribution(s)

check_model_centiles() check_computed_centiles(): Compute the percentage of points under each centile line

WiscLab data prepartion 🧹

format_year_month_age(): Convert age in months to years;months

chrono_age(): Compute chronological age in months

impute_values_by_length(): Staged imputation

tocs_item() tocs_type() tocs_length(): Extract the TOCS details from a string (usually a filename)

weight_lengths_with_ordinal_model(): Weight utterance lengths by using an ordinal regression model

Other functions 📌

brms_args_create(): Set default arguments for brms model fitting

compute_overlap_rate(): Compute overlap rate for (phoneme alignment) intervals

file_replace_name() file_rename_with(): Rename file basenames using functions

join_to_split(): Join data onto resampled IDs

Datasets 🗺️

data_acq_consonants data_acq_vowels: Acquisition and developmental descriptions of consonants and vowels

data_example_intelligibility_by_length: Simulated intelligibility scores by utterance length

data_fake_intelligibility: Fake intelligibility data

data_fake_rates: Fake speaking rate data

data_features_consonants data_features_vowels: Phonetic features of consonants and vowels