Package: certestats 1.23.1

Matthijs S. Berends

certestats: A Certe R Package for Statistical Modelling

A Certe R Package for early-warning, applying statistical modelling (such as creating machine learning models), QC rules and distribution analysis. This package is part of the 'certedata' universe.

Authors:Matthijs S. Berends [aut, cre], Erwin Dijkstra [aut], Certe Medical Diagnostics & Advice Foundation [cph, fnd]

certestats_1.23.1.tar.gz
certestats_1.23.1.zip(r-4.5)certestats_1.23.1.zip(r-4.4)certestats_1.23.1.zip(r-4.3)
certestats_1.23.1.tgz(r-4.4-any)certestats_1.23.1.tgz(r-4.3-any)
certestats_1.23.1.tar.gz(r-4.5-noble)certestats_1.23.1.tar.gz(r-4.4-noble)
certestats_1.23.1.tgz(r-4.4-emscripten)certestats_1.23.1.tgz(r-4.3-emscripten)
certestats.pdf |certestats.html✨
certestats/json (API)

# Install 'certestats' in R:

install.packages('certestats', repos = c('https://certe-medical-epidemiology.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/certe-medical-epidemiology/certestats/issues

Datasets:

esbl_tests - Example Data Set with ESBL Test Outcomes

On CRAN:

statistics

3.26 score 1 packages 1 scripts 106 exports 103 dependencies

Last updated 19 days agofrom:ab9d2635bd. Checks:OK: 5 NOTE: 2. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Nov 05 2024
R-4.5-win	NOTE	Nov 05 2024
R-4.5-linux	NOTE	Nov 05 2024
R-4.4-win	OK	Nov 05 2024
R-4.4-mac	OK	Nov 05 2024
R-4.3-win	OK	Nov 05 2024
R-4.3-mac	OK	Nov 05 2024

Exports:all any apply_model_to as.binary autoplot centre_mean check_testing_predictions ci confusion_matrix correlation_plot cqv cv deciles early_warning_biomarker early_warning_cluster everything ewma feature_importance_plot feature_importances fivenum gain_plot get_accuracy get_coefficients get_kappa get_metrics get_mice get_model_variables get_original_data get_recipe get_roc_data get_rows_testing get_rows_training get_specification get_variable_weights has_cluster_after has_cluster_before has_clusters has_ongoing_cluster impute IQR is_imputed is.binary mae mape max mean mean_geometric mean_harmonic median metrics midhinge min ml_decision_trees ml_linear_regression ml_logistic_regression ml_nearest_neighbour ml_neural_network ml_random_forest ml_xg_boost moving_average moving_fn moving_Q1 moving_Q3 moving_sum mse n_clusters normalise normality normalize percentiles pmax pmin prod qc_rule_text qc_rule1 qc_rule2 qc_rule3 qc_rule4 qc_rule5 qc_rule6 qc_rule7 qc_rule8 qc_test quantile range regression remove_outliers rmse roc_plot row_function rr_ewma scale_sd sd se sum sum_of_squares tree_plot try_binary tune_parameters var weighted_fn weighted_mean weighted_median weighted_Q1 weighted_Q3 z_score

Dependencies:AMR backports bit bit64 broom certestyle class cleaner cli clipr clock codetools colorspace cpp11 crayon data.table diagram dials DiceDesign digest doFuture dplyr evaluate fansi farver foreach furrr future future.apply generics ggplot2 globals glue gower GPfit gtable hardhat highr hms ipred isoband iterators KernSmooth knitr labeling lattice lava lhs lifecycle listenv lubridate magrittr MASS Matrix mgcv modelenv munsell nlme nnet numDeriv parallelly parsnip pillar pkgconfig prettyunits prodlim progress progressr purrr R6 RColorBrewer Rcpp readr recipes rlang rpart rsample rstudioapi scales sfd shape slider SQUAREM stringi stringr survival tibble tidyr tidyselect timechange timeDate tune tzdb utf8 vctrs viridisLite vroom warp withr workflows xfun yaml yardstick

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Work with Binary Columns	as.binary is.binary try_binary
Confusion Matrix Metrics	confusion_matrix confusion_matrix.default
Different Means	different_means mean_geometric mean_harmonic
Distribution Metrics	centre_mean ci cqv cv deciles distribution_metrics ewma mae mape midhinge mse normalise normalize percentiles rmse rr_ewma scale_sd se sum_of_squares z_score
Early Warning for Biomarkers	early_warning_biomarker
Early Warning for Disease Clusters	early_warning_cluster has_clusters has_cluster_after has_cluster_before has_ongoing_cluster n_clusters
Example Data Set with ESBL Test Outcomes	esbl_tests
Impute: Filling Missing Values	get_mice impute is_imputed
Create a Machine Learning (ML) Model	apply_model_to autoplot.certestats_feature_importances autoplot.certestats_ml autoplot.certestats_tuning check_testing_predictions confusion_matrix.certestats_ml correlation_plot feature_importances feature_importance_plot gain_plot get_accuracy get_coefficients get_kappa get_metrics get_model_variables get_original_data get_recipe get_roc_data get_rows_testing get_rows_training get_specification get_variable_weights machine_learning ml_decision_trees ml_linear_regression ml_logistic_regression ml_nearest_neighbour ml_neural_network ml_random_forest ml_xg_boost predict.certestats_ml roc_plot tree_plot tune_parameters
Mathematical Functions With Global 'na.rm'	all any fivenum IQR math_functions max mean median min pmax pmin prod quantile range sd sum var
Moving Average	moving_average moving_fn moving_Q1 moving_Q3 moving_sum
Normality Analysis	normality
Quality Control (QC) Rules	qc_rule1 qc_rule2 qc_rule3 qc_rule4 qc_rule5 qc_rule6 qc_rule7 qc_rule8 qc_rules qc_rule_text qc_test
Fast Regression Models	autoplot.certestats_reg plot.certestats_reg regression regression.data.frame regression.default
Remove Outliers	remove_outliers
Apply Function per Row	row_function
Weighted Mean	weighted_fn weighted_mean weighted_median weighted_Q1 weighted_Q3