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kruskal_wallis_test().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.