The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Weighted significance tests have been re-designed. The functions weighted_ttest(), weighted_mannwhitney() and weighted_chisqtest() are no longer available. These are now re-implemented in t_test(), mann_whitney_test() and chi_squared_test(). If weights are required, the weights argument can be used. Furthermore, new functions for significance testing were added: kruskal_wallis_test() and wilcoxon_test().
means_by_group() and mean_n() were removed. The replacements are datawizard::means_by_group() and datawizard::row_means() (using the min_valid argument).
weighted_median(), weighted_sd() and weighted_mean() were removed. Their replacements are datawizard::weighted_median(), datawizard::weighted_sd() and datawizard::weighted_mean().
Package dependency was dramatically reduced. sjstats now requires much fewer and much more light-weight packages to work.
Some minor bugs were fixed.
sjstats is being re-structured, and many functions are re-implemented in new packages that are part of a new project called easystats.
Therefore, following functions are now defunct:
mediation(), , please use bayestestR::mediation().eta_sq(), please use effectsize::eta_squared().omega_sq(), please use effectsize::omega_squared().epsilon_sq(), please use effectsize::epsilon_squared().odds_to_rr(), please use effectsize::oddsratio_to_riskratio().std_beta(), please use effectsize::standardize_parameters().robust(), please use parameters::standard_error_robust().scale_weights(), , please use datawizard::rescale_weights().Improved printing for weighted_mannwhitney().
weighted_chisqtest() can now be computed for given probabilities.
means_by_group() now contains numeric values in the returned data frame. Value formatting is completely done insight the print-method.
Updated imports.
eta_sq()) now internally call the related functions from the effectsize package.chisq_gof().anova_stats() with incorrect effect sizes for certain Anova types (that included an intercept).sjstats is being re-structured, and many functions are re-implemented in new packages that are part of a new project called easystats.
Therefore, following functions are now deprecated:
cohens_f(), please use effectsize::cohens_f().std_beta(), please use effectsize::standardize_parameters().tidy_stan(), please use parameters::model_parameters().scale_weights(), please use parameters::rescale_weights().robust(), please use parameters::standard_error_robust().wtd_*() have been renamed to weighted_*().svy_md() was renamed to survey_median().mannwhitney() is an alias for mwu().means_by_group() is an alias for grpmean().sjstats is being re-structured, and many functions are re-implemented in new packages that are part of a new project called easystats. The aim of easystats is to provide a unifying and consistent framework to tame, discipline and harness the scary R statistics and their pesky models.
Therefore, following functions are now deprecated:
p_value(), please use parameters::p_value()se(), please use parameters::standard_error()design_effect() is an alias for deff().samplesize_mixed() is an alias for smpsize_lmm().crosstable_statistics() is an alias for xtab_statistics().svyglm.zip() to fit zero-inflated Poisson models for survey-designs.phi() and cramer() can now compute confidence intervals.tidy_stan() removes prior parameters from output.tidy_stan() now also prints the probability of direction.odds_to_rr().epsilon_sq(), to compute epsilon-squared effect-size.sjstats is being re-structured, and many functions are re-implemented in new packages that are part of a new project called easystats. The aim of easystats is to provide a unifying and consistent framework to tame, discipline and harness the scary R statistics and their pesky models.
Therefore, following functions are now deprecated:
link_inverse(), please use insight::link_inverse()model_family(), please use insight::model_info()model_frame(), please use insight::get_data()pred_vars(), please use insight::find_predictors()re_grp_var(), please use insight::find_random()grp_var(), please use insight::find_random()resp_val(), please use insight::get_response()resp_var(), please use insight::find_response()var_names(), please use insight::clean_names()overdisp(), please use performance::check_overdispersion()zero_count(), please use performance::check_zeroinflation()converge_ok(), please use performance::check_convergence()is_singular(), please use performance::check_singularity()reliab_test(), please use performance::item_reliability()split_half(), please use performance::item_split_half()predictive_accurarcy(), please use performance::performance_accuracy()cronb(), please use performance::cronbachs_alpha()difficulty(), please use performance::item_difficulty()mic(), please use performance::item_intercor()pca(), please use parameters::principal_components()pca_rotate(), please use parameters::principal_components()r2(), please use performance::r2()icc(), please use performance::icc()rmse(), please use performance::rmse()rse(), please use performance::rse()mse(), please use performance::mse()hdi(), please use bayestestR::hdi()cred_int(), please use bayestestR::ci()rope(), please use bayestestR::rope()n_eff(), please use bayestestR::effective_sample()equi_test(), please use bayestestR::equivalence_test()multicollin(), please use performance::check_collinearity()normality(), please use performance::check_normality()autocorrelation(), please use performance::check_autocorrelation()heteroskedastic(), please use performance::check_heteroscedasticity()outliers(), please use performance::check_outliers()eta_sq()) get a method-argument to define the method for computing confidence intervals from bootstrapping.smpsize_lmm() could result in negative sample-size recommendations. This was fixed, and a warning is now shown indicating that the parameters for the power-calculation should be modified.r in mwu() if group-factor contained more than two groups.model_family(), link_inverse() or model_frame(): MixMod (package GLMMadaptive), MCMCglmm, mlogit and gmnl.cred_int(), to compute uncertainty intervals of Bayesian models. Mimics the behaviour and style of hdi() and is thus a convenient complement to functions like posterior_interval().equi_test() now finds better defaults for models with binomial outcome (like logistic regression models).r2() for mixed models now also should work properly for mixed models fitted with rstanarm.anova_stats() and alike (e.g. eta_sq()) now all preserve original term names.model_family() now returns $is_count = TRUE, when model is a count-model, and $is_beta = TRUE for models with beta-family.pred_vars() checks that return value has only unique values.pred_vars() gets a zi-argument to return the variables from a model’s zero-inflation-formula.wtd_sd() and wtd_mean() when weight was NULL (which usually shoudln’t be the case anyway).deparse(), cutting off very long formulas in various functions.dplyr::n(), to meet forthcoming changes in dplyr 0.8.0.boot_ci() gets a ci.lvl-argument.rotation-argument in pca_rotate() now supports all rotations from psych::principal().pred_vars() gets a fe.only-argument to return only fixed effects terms from mixed models, and a disp-argument to return the variables from a model’s dispersion-formula.icc() for Bayesian models gets a adjusted-argument, to calculate adjusted and conditional ICC (however, only for Gaussian models).icc() for non-Gaussian Bayes-models, a message is printed that recommends setting argument ppd to TRUE.resp_val() and resp_var() now also work for brms-models with additional response information (like trial() in formula).resp_var() gets a combine-argument, to return either the name of the matrix-column or the original variable names for matrix-columns.model_frame() now also returns the original variables for matrix-column-variables.model_frame() now also returns the variable from the dispersion-formula of glmmTMB-models.model_family() and link_inverse() now supports glmmPQL, felm and lm_robust-models.anova_stats() and alike (omeqa_sq() etc.) now support gam-models from package gam.p_value() now supports objects of class svyolr.se() and get_re_var() for objects returned by icc().icc() for Stan-models.var_names() did not clear terms with log-log transformation, e.g. log(log(y)).model_frame() for models with splines with only one column.r2() and icc(), also by adding more references.re_grp_var() to find group factors of random effects in mixed models.omega_sq() and eta_sq() give more informative messages when using non-supported objects.r2() and icc() give more informative warnings and messages.tidy_stan() supports printing simplex parameters of monotonic effects of brms models.grpmean() and mwu() get a file and encoding argument, to save the HTML output as file.model_frame() now correctly names the offset-columns for terms provided as offset-argument (i.e. for models where the offset was not specified inside the formula).weights-argument in grpmean() when variable name was passed as character vector.r2() for glmmTMB models with ar1 random effects structure.wtd_chisqtest() to compute a weighted Chi-squared test.wtd_median() to compute the weighted median of variables.wtd_cor() to compute weighted correlation coefficients of variables.mediation() can now cope with models from different families, e.g. if the moderator or outcome is binary, while the treatment-effect is continuous.model_frame(), link_inverse(), pred_vars(), resp_var(), resp_val(), r2() and model_family() now support clm2-objects from package ordinal.anova_stats() gives a more informative message for non-supported models or ANOVA-options.model_family() and link_inverse() for models fitted with pscl::hurdle() or pscl::zeroinfl().grpmean() for grouped data frames, when grouping variable was an unlabelled factor.model_frame() for coxph-models with polynomial or spline-terms.mediation() for logical variables.wtd_ttest() to compute a weighted t-test.wtd_mwu() to compute a weighted Mann-Whitney-U or Kruskal-Wallis test.robust() was revised, getting more arguments to specify different types of covariance-matrix estimation, and handling these more flexible.print()-method for tidy_stan() for brmsfit-objects with categorical-families.se() now also computes standard errors for relative frequencies (proportions) of a vector.r2() now also computes r-squared values for glmmTMB-models from genpois-families.r2() gives more precise warnings for non-supported model-families.xtab_statistics() gets a weights-argument, to compute measures of association for contingency tables for weighted data.statistics-argument in xtab_statistics() gets a "fisher"-option, to force Fisher’s Exact Test to be used.icc() for generalized linear mixed models with Poisson or negative binomial families.icc() gets an adjusted-argument, to calculate the adjusted and conditional ICC for mixed models.weight.by is now deprecated and renamed into weights.grpmean() now also adjusts the n-columm for weighted data.icc(), re_var() and get_re_var() now correctly compute the random-effect-variances for models with multiple random slopes per random effect term (e.g., (1 + rs1 + rs2 | grp)).tidy_stan(), mcse(), hdi() and n_eff() for stan_polr()-models.equi_test() did not work for intercept-only models.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.