Package: statim 0.1.0.9000

Joshua Marie

statim: A Declarative Interface for Statistical Inference

A declarative interface for statistical inference built on the S7 object system. The layouts of the statistical inference are described using expressive variable mappers, null hypotheses are stated as mathematical expressions over population parameters, and estimation is deferred until explicitly requested. Supports estimation method involving hypothesis testing and model-based inference with an extensible variant system for registering custom estimation methods.

Authors:Joshua Marie [aut, cre], Antoine Soetewey [aut]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
statim/json (API)

# Install 'statim' in R:

install.packages('statim', repos = c('https://s7-stats.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/s7-stats/statim/issues

Pkgdown/docs site:https://s7-stats.github.io

On CRAN:

inferential-statistics s7 statistics tidy

5.69 score 5 stars 11 scripts 241 downloads 72 exports 14 dependencies

Last updated from:0760a9b902. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel	OK	198
source / vignettes	OK	226
linux-release	OK	183
macos-release	OK	162
macos-oldrel	OK	211
windows-devel	OK	143
windows-release	OK	118
windows-oldrel	OK	127
wasm-release	OK	157

Exports:%<-%%by%add_stat_define add_variant agendas anova auto_gauge auto_predict auto_tidy baseline check_param_nodes check_x_and_given claim_contrast_coefs claim_scalar class_corr_two class_glm_object class_lm_object class_p_test class_stat_infer class_ttest_one class_ttest_pairwise class_ttest_two class_var_inform conclude COR_TEST define_model display gauge GLM HTEST_FN inlines LINEAR_REG making_gauge making_predict making_tidy map_claim method_gauge method_predict method_tidy MODEL_FN model_infer_define model_processor MU on P_TEST pairwise param_obj PI predict prepare prepare_model prepare_test prop purge_stat_defines rel remove_stat_define remove_variant RHO STAT_CONSTRUCTOR stat_define state_null T_TEST test_define tidy validate_claim_vars validate_one_param_node var_id var_id_info variant via write_models x_by

Dependencies:cli glue lifecycle magrittr pillar pkgconfig rlang S7 tabstats tibble tidyselect utf8 vctrs withr

Getting started with statim

Rendered fromstatim.Rmdusingknitr::rmarkdown

Last update: 2026-07-23
Started: 2026-06-04

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add or remove stat_define implementations on a test or model function	add-stat-define add_stat_define remove_stat_define
Add or remove variant implementations on a test or model function	add-variant add_variant remove_variant
Collect implementations for a statistical procedure	agendas
ANOVA table for linear model comparisons	anova anova-mod
Automatically gauge effect size from a statistical result	auto_gauge
Automatically predict from a statistical result	auto_predict
Automatically tidy a statistical result	auto_tidy
Declare the canonical implementation of a test or model	baseline
Extract contrast coefficients from a null claim	claim_contrast_coefs
Extract a scalar hypothesis value from a null claim	claim_scalar
Validate hypothesis parameter references against a model's declared variables	check_param_nodes check_x_and_given claim-vars-validators validate_claim_vars validate_one_param_node
Structured result container for two-sample t-tests	class_corr_two
Structured result container for GLM fits	class_glm_object
Structured result container for linear model fits	class_lm_object
Structured result container for proportion tests	class_p_test
Base class for all statistical result objects	class_stat_infer
Structured result container for one-sample t-tests	class_ttest_one
Structured result container for pairwise t-tests	class_ttest_pairwise
Structured result container for two-sample t-tests	class_ttest_two
Output class for Variable Mapper metadata	class_var_inform
Execute a lazy pipeline	conclude
Correlation Test	COR_TEST
Display individual results	display
Effect size for a concluded statistical result	gauge
Generalized linear model	GLM
Build a hypothesis test function	HTEST_FN
Inline multiple expressions in a Variable Mapper	inlines
Define a layout supplied by a Variable Mapper	define_model layout-define-base
Linear regression	LINEAR_REG
Declare a gauge method for a stat and model type	making_gauge
Declare a predict method for a stat and model type	making_predict
Declare tidy methods for a stat and model type	making_tidy
Build a claim parser from named resolver functions	map_claim
Declare gauge methods for a stat result	method_gauge
Declare predict methods for a stat result	method_predict
Declare tidy methods for a stat result	method_tidy
Build a model inference function	MODEL_FN
Model evaluator	model-processor model_processor
Apply a method_tidy to a making_tidy target	%<-% modifying-assignment
Mean of a variable, optionally conditioned on a subgroup	MU
State a null hypothesis in the pipeline	null-hyp state_null
Specify variables for independent testing	on
Proportion Test	P_TEST
Define all pairwise variable combinations	pairwise
Base class for population parameters	param_obj
Proportion of a variable, optionally conditioned on a subgroup	PI
Predict from a concluded statistical result	predict
Prepare a lazy inference pipeline	prepare
Lazily prepare a model inference	prepare-model prepare_model
Lazily prepare a single test	prepare-test prepare_test
Define a proportion test model	prop
Purge all package-scoped stat_define registrations for a package	purge_stat_defines
Describe the relationship between two variables	rel
Population correlation between two variables	RHO
Main foundation for inferential statistics	STAT_CONSTRUCTOR
Define a statistical procedure implementation	model_infer_define stat-infer-definer stat_define test_define
T-Test	T_TEST
Tidy a concluded statistical result	tidy
Base class for Variable Mapper objects	var_id
Extract metadata from a Variable Mapper	var_id_info
Declare an alternative implementation of a test or model	variant
Recalibrate the method variant	via
Write multiple model definitions from a data frame	write_models
Compare a variable by group	%by% x_by