Package: parameters 0.23.0.11
Daniel Lüdecke
parameters: Processing of Model Parameters
Utilities for processing the parameters of various statistical models. Beyond computing p values, CIs, and other indices for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), this package implements features like bootstrapping or simulating of parameters and models, feature reduction (feature extraction and variable selection) as well as functions to describe data and variable characteristics (e.g. skewness, kurtosis, smoothness or distribution).
Authors:
parameters_0.23.0.11.tar.gz
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parameters.pdf |parameters.html✨
parameters/json (API)
NEWS
| # Install 'parameters' in R: |
| install.packages('parameters', repos = c('https://easystats.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/easystats/parameters/issues
- fish - Sample data set
- qol_cancer - Sample data set
betabootstrapciconfidence-intervalsdata-reductioneasystatsfafeature-extractionfeature-reductionhacktoberfestparameterspcapvaluesregression-modelsrobust-statisticsstandardizestandardized-estimatesstatistical-models
Last updated 2 days agofrom:643b477820. Checks:OK: 1 WARNING: 6. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 19 2024 |
| R-4.5-win | WARNING | Nov 19 2024 |
| R-4.5-linux | WARNING | Nov 19 2024 |
| R-4.4-win | WARNING | Nov 19 2024 |
| R-4.4-mac | WARNING | Nov 19 2024 |
| R-4.3-win | WARNING | Nov 19 2024 |
| R-4.3-mac | WARNING | Nov 19 2024 |
Exports:bootstrap_modelbootstrap_parameterscici_betwithinci_kenwardci_ml1ci_satterthwaiteclosest_componentcluster_analysiscluster_centerscluster_discriminationcluster_metacluster_performancecompare_modelscompare_parametersconfidence_curveconsonance_functionconvert_efa_to_cfadegrees_of_freedomdemeandescribe_distributiondisplaydofdof_betwithindof_kenwarddof_ml1dof_satterthwaitedominance_analysisefa_to_cfaequivalence_testfactor_analysisformat_df_adjustformat_orderformat_p_adjustformat_parametersget_scoreskurtosismodel_parametersn_clustersn_clusters_dbscann_clusters_elbown_clusters_gapn_clusters_hclustn_clusters_silhouetten_componentsn_factorsn_parametersp_calibratep_directionp_functionp_significancep_valuep_value_betwithinp_value_kenwardp_value_ml1p_value_satterthwaiteparametersparameters_typepool_parametersprincipal_componentsprint_htmlprint_mdprint_tablerandom_parametersreduce_datareduce_parametersrescale_weightsreshape_loadingsrotated_datase_kenwardse_satterthwaiteselect_parameterssimulate_modelsimulate_parametersskewnesssort_parametersstandard_errorstandardise_infostandardise_parametersstandardise_posteriorsstandardize_infostandardize_namesstandardize_parametersstandardize_posteriorssupported_modelsvisualisation_recipe
Dependencies:bayestestRdatawizardinsight
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Model bootstrapping | bootstrap_model bootstrap_model.default |
| Parameters bootstrapping | bootstrap_parameters bootstrap_parameters.default |
| Between-within approximation for SEs, CIs and p-values | ci_betwithin dof_betwithin p_value_betwithin |
| Kenward-Roger approximation for SEs, CIs and p-values | ci_kenward dof_kenward p_value_kenward se_kenward |
| "m-l-1" approximation for SEs, CIs and p-values | ci_ml1 dof_ml1 p_value_ml1 |
| Satterthwaite approximation for SEs, CIs and p-values | ci_satterthwaite dof_satterthwaite p_value_satterthwaite se_satterthwaite |
| Confidence Intervals (CI) | ci.default |
| Cluster Analysis | cluster_analysis |
| Find the cluster centers in your data | cluster_centers |
| Compute a linear discriminant analysis on classified cluster groups | cluster_discrimination |
| Metaclustering | cluster_meta |
| Performance of clustering models | cluster_performance cluster_performance.hclust |
| Compare model parameters of multiple models | compare_models compare_parameters |
| Conversion between EFA results and CFA structure | convert_efa_to_cfa convert_efa_to_cfa.fa efa_to_cfa |
| Degrees of Freedom (DoF) | degrees_of_freedom dof |
| Print tables in different output formats | display.equivalence_test_lm display.parameters_efa display.parameters_efa_summary display.parameters_model display.parameters_sem print_table |
| Dominance Analysis | dominance_analysis |
| Equivalence test | equivalence_test.ggeffects equivalence_test.lm |
| Principal Component Analysis (PCA) and Factor Analysis (FA) | closest_component factor_analysis predict.parameters_efa principal_components print.parameters_efa rotated_data sort.parameters_efa |
| Sample data set | fish |
| Format the name of the degrees-of-freedom adjustment methods | format_df_adjust |
| Order (first, second, ...) formatting | format_order |
| Format the name of the p-value adjustment methods | format_p_adjust |
| Parameter names formatting | format_parameters format_parameters.default |
| Print comparisons of model parameters | format.compare_parameters print.compare_parameters print_html.compare_parameters print_md.compare_parameters |
| Print model parameters | format.parameters_model print.parameters_model print_html.parameters_model print_md.parameters_model summary.parameters_model |
| Get Scores from Principal Component Analysis (PCA) | get_scores |
| Model Parameters | model_parameters parameters |
| Parameters from ANOVAs | model_parameters.aov |
| Parameters from Bayesian Exploratory Factor Analysis | model_parameters.befa |
| Parameters from BayesFactor objects | model_parameters.BFBayesFactor |
| Parameters from Generalized Additive (Mixed) Models | model_parameters.cgam |
| Parameters from Bayesian Models | model_parameters.brmsfit model_parameters.data.frame |
| Parameters from (General) Linear Models | model_parameters.default |
| Parameters from Hypothesis Testing | model_parameters.glht |
| Parameters from special models | model_parameters.glimML |
| Parameters from Mixed Models | model_parameters.glmmTMB |
| Parameters from Cluster Models (k-means, ...) | model_parameters.hclust |
| Parameters from hypothesis tests | model_parameters.coeftest model_parameters.htest |
| Parameters from PCA, FA, CFA, SEM | model_parameters.lavaan model_parameters.principal |
| Parameters from multiply imputed repeated analyses | model_parameters.mira |
| Parameters from multinomial or cumulative link models | model_parameters.mlm |
| Parameters from Meta-Analysis | model_parameters.rma |
| Parameters from robust statistical objects in 'WRS2' | model_parameters.t1way |
| Parameters from Zero-Inflated Models | model_parameters.zcpglm |
| Find number of clusters in your data | n_clusters n_clusters_dbscan n_clusters_elbow n_clusters_gap n_clusters_hclust n_clusters_silhouette |
| Number of components/factors to retain in PCA/FA | n_components n_factors |
| Calculate calibrated p-values. | p_calibrate p_calibrate.default |
| Probability of Direction (pd) | p_direction.lm |
| p-value or consonance function | confidence_curve consonance_function p_function |
| Practical Significance (ps) | p_significance.lm |
| p-values | p_value p_value.default p_value.emmGrid |
| Type of model parameters | parameters_type |
| Pool Model Parameters | pool_parameters |
| Predict method for parameters_clusters objects | predict.parameters_clusters |
| Sample data set | qol_cancer |
| Summary information from random effects | random_parameters |
| Dimensionality reduction (DR) / Features Reduction | reduce_data reduce_parameters |
| Reshape loadings between wide/long formats | reshape_loadings reshape_loadings.data.frame reshape_loadings.parameters_efa |
| Automated selection of model parameters | select_parameters select_parameters.lm select_parameters.merMod |
| Simulated draws from model coefficients | simulate_model simulate_model.default |
| Simulate Model Parameters | simulate_parameters simulate_parameters.default |
| Sort parameters by coefficient values | sort_parameters sort_parameters.default |
| Standard Errors | standard_error standard_error.default standard_error.factor |
| Get Standardization Information | standardise_info standardize_info standardize_info.default |
| Parameters standardization | standardise_parameters standardise_posteriors standardize_parameters standardize_posteriors |
