sjplot r-universe repositoryhttps://sjplot.r-universe.devPackage updated in sjplotcranlike-server 0.18.21https://github.com/sjplot.png?size=400sjplot r-universe repositoryhttps://sjplot.r-universe.devMon, 12 Aug 2024 18:11:29 GMT[easystats] performance 0.12.2.11d.luedecke@uke.de (Daniel Lüdecke)Utilities for computing measures to assess model quality,
which are not directly provided by R's 'base' or 'stats'
packages. These include e.g. measures like r-squared,
intraclass correlation coefficient (Nakagawa, Johnson &
Schielzeth (2017) <doi:10.1098/rsif.2017.0213>), root mean
squared error or functions to check models for overdispersion,
singularity or zero-inflation and more. Functions apply to a
large variety of regression models, including generalized
linear models, mixed effects models and Bayesian models.
References: Lüdecke et al. (2021) <doi:10.21105/joss.03139>.https://github.com/r-universe/easystats/actions/runs/10357176296Mon, 12 Aug 2024 18:11:29 GMTperformance0.12.2.11successhttps://easystats.r-universe.devhttps://github.com/easystats/performance[easystats] insight 0.20.2.12d.luedecke@uke.de (Daniel Lüdecke)A tool to provide an easy, intuitive and consistent access
to information contained in various R models, like model
formulas, model terms, information about random effects, data
that was used to fit the model or data from response variables.
'insight' mainly revolves around two types of functions:
Functions that find (the names of) information, starting with
'find_', and functions that get the underlying data, starting
with 'get_'. The package has a consistent syntax and works with
many different model objects, where otherwise functions to
access these information are missing.https://github.com/r-universe/easystats/actions/runs/10285154655Wed, 07 Aug 2024 12:49:38 GMTinsight0.20.2.12successhttps://easystats.r-universe.devhttps://github.com/easystats/insightexport.Rmdexport.htmlExporting tables with captions and footers2021-10-22 09:23:052022-07-30 19:23:25display.Rmddisplay.htmlFormatting, printing and exporting tables2021-02-10 08:04:032023-09-23 17:09:16insight.Rmdinsight.htmlGetting Started with Accessing Model Information2019-01-31 08:04:192024-05-30 04:41:54[easystats] parameters 0.22.1.7d.luedecke@uke.de (Daniel Lüdecke)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).https://github.com/r-universe/easystats/actions/runs/10091407167Thu, 25 Jul 2024 08:34:47 GMTparameters0.22.1.7successhttps://easystats.r-universe.devhttps://github.com/easystats/parametersoverview_of_vignettes.Rmdoverview_of_vignettes.htmlOverview of Vignettes2021-02-16 21:48:282023-06-01 06:31:17[easystats] easystats 0.7.3d.luedecke@uke.de (Daniel Lüdecke)A meta-package that installs and loads a set of packages
from 'easystats' ecosystem in a single step. This collection of
packages provide a unifying and consistent framework for
statistical modeling, visualization, and reporting.
Additionally, it provides articles targeted at instructors for
teaching 'easystats', and a dashboard targeted at new R users
for easily conducting statistical analysis by accessing summary
results, model fit indices, and visualizations with minimal
programming.https://github.com/r-universe/easystats/actions/runs/10059715082Tue, 23 Jul 2024 13:05:46 GMTeasystats0.7.3successhttps://easystats.r-universe.devhttps://github.com/easystats/easystatsworkflow_performance.rmdworkflow_performance.htmlAnalytic workflow: Assessing model fit2024-03-27 14:46:192024-06-06 07:50:05citation.Rmdcitation.htmlCiting 'easystats'2021-06-20 13:36:202024-06-17 09:57:13conventions.Rmdconventions.htmlCoding style conventions2021-04-23 13:10:522022-08-11 15:40:57resources.Rmdresources.htmlLearning resources2021-04-23 16:10:002022-08-27 23:03:19list_of_functions.Rmdlist_of_functions.htmlList of functions2021-05-10 13:56:492024-06-09 19:27:00version_policy.Rmdversion_policy.htmlR version support2024-05-16 17:14:572024-05-24 12:37:14[strengejacke] ggeffects 1.7.0.4d.luedecke@uke.de (Daniel Lüdecke)Compute marginal effects and adjusted predictions from
statistical models and returns the result as tidy data frames.
These data frames are ready to use with the 'ggplot2'-package.
Effects and predictions can be calculated for many different
models. Interaction terms, splines and polynomial terms are
also supported. The main functions are ggpredict(), ggemmeans()
and ggeffect(). There is a generic plot()-method to plot the
results using 'ggplot2'.https://github.com/r-universe/strengejacke/actions/runs/9979539199Wed, 17 Jul 2024 18:23:52 GMTggeffects1.7.0.4successhttps://strengejacke.r-universe.devhttps://github.com/strengejacke/ggeffectscontent.Rmdcontent.htmlDocumentation of the ggeffects package2021-07-29 12:25:342024-06-07 09:26:32