Introduction to effectcheck

library(effectcheck)
#> ===========================================================================
#>   effectcheck v0.7.17 - DEVELOPMENT VERSION
#> ===========================================================================
#>   WARNING: This package is under heavy development and has NOT yet been
#>   fully validated. Results should be independently verified before use in
#>   any consequential context. Use is at your sole responsibility.
#> 
#>   Report issues & help verify: https://github.com/giladfeldman/escicheck
#>   Contact: Gilad Feldman <giladfel@gmail.com>
#> ===========================================================================

# Check a single APA-style result
result <- check_text("t(28) = 2.21, p = .035, d = 0.80")
print(result)
#> 
#> === EffectCheck Results ===
#> 
#> Total statistics found: 1
#>   PASS: 1 | WARN: 0 | ERROR: 0 | INSUFFICIENT: 0
#> 
#> Test types: t(1)
#> Uncertainty: high(1)
#> 
#> Use print(x, short = FALSE) for detailed results.
#> Use summary(x) for comprehensive statistics.

text <- "
Study 1 found a significant effect, t(45) = 3.12, p = .003, d = 0.91.
The ANOVA revealed a main effect, F(2, 87) = 5.44, p = .006.
The correlation was significant, r(48) = .42, p = .003.
"

results <- check_text(text)
summary(results)
#> 
#> ========================================
#>      EffectCheck Summary Report
#> ========================================
#> 
#> Version: 0.7.17
#> Generated: 2026-10-08 14:38:47.903223
#> 
#> Total statistics analyzed: 3
#> 
#> --- Status Distribution ---
#>   OK                 1 ( 33.3%) ##########
#>   PASS               2 ( 66.7%) ####################
#> 
#> --- Test Types ---
#>   F             1
#>   r             1
#>   t             1
#> 
#> --- Uncertainty Levels ---
#>   high          1
#>   low           1
#>   medium        1
#> 
#> --- Design Inferred ---
#>   independent        1
#>   unclear            2
#> 
#> --- Key Metrics ---
#>   Error rate:             0.0%
#>   Decision error rate:    0.0% (0 cases)
#>   Mean effect delta:    0.0002
#>   Median effect delta:  0.0002
#>   Max effect delta:     0.0004
#> 
#> ========================================

# Filter by status
errors <- get_errors(results)
warnings <- get_warnings(results)

# Filter by test type
t_tests <- filter_by_test_type(results, "t")

# Get counts
count_by(results, "test_type")
#>   test_type count percent
#> 1         F     1    33.3
#> 2         r     1    33.3
#> 3         t     1    33.3

result <- check_text("t(28) = 2.21, p = .035, d = 0.80")

# See all computed variants for the first result
format_variants(result, 1)
#> [1] "Same-Type Variants:\n  d_ind_equalN = 0.807 (Independent samples, equal group sizes assumed)\n  d_ind_min = 2.248 (Independent samples, extreme imbalance (1 vs N-1))\n  d_ind_max = 2.248 (Independent samples, extreme imbalance (N-1 vs 1))\n  dz = 0.4104 (Paired/within-subjects design)\n  dav = 0.4346 (Paired design, average SD method)\n  drm = 0.3887 (Repeated measures design)\n  g_ind = 0.7852 (Independent samples, bias-corrected)\n\nAlternative Suggestions:\n  g_ind = 0.7852 [Hedges' g]\n      Bias-corrected version of d, recommended for small samples\n  g_ind_Nm1 = 0.7859 [Hedges' g (J with N-1)]\n      Software convention: J(N-1) vs J(N-2)\n  r = 0.3854 [Correlation (r)]\n      Alternative way to express t-test effect size, useful for meta-analysis\n  partial_eta2_from_t = 0.1485\n      Equivalent to partial eta-squared from F(1,df) = t-squared\n  omega2_from_t = 0.1146\n      Population-level effect size from t-test\n  R2_from_t = 0.1485\n      R-squared for single-predictor model (= eta2)\n  dz = 0.4104 [Cohen's dz]\n      If this is actually a paired/within-subjects design\n  dav = 0.4346 [Cohen's dav]\n      If this is actually a paired/within-subjects design\n  drm = 0.3887 [Cohen's drm]\n      If this is actually a paired/within-subjects design\n  gz = 0.3993 [Hedges' gz (paired)]\n      If this is actually a paired/within-subjects design\n  gav = 0.4229 [Hedges' gav (paired, average SD)]\n      If this is actually a paired/within-subjects design\n  grm = 0.3782 [Hedges' grm (repeated measures)]\n      If this is actually a paired/within-subjects design"

# Get metadata about a specific variant type
get_variant_metadata("d_ind")
#> $name
#> [1] "Cohen's d (independent)"
#> 
#> $assumptions
#> [1] "Independent samples, known group sizes"
#> 
#> $when_to_use
#> [1] "Between-subjects design with known n1 and n2"
#> 
#> $formula
#> [1] "d = t * sqrt(1/n1 + 1/n2)"

paper_text <- paste(readLines("paper.txt", warn = FALSE), collapse = "\n")
result <- check_text(paper_text)

results <- check_text("t(28) = 2.21, p = .035, d = 0.80")

# HTML report
generate_report(results, out = file.path(tempdir(), "report.html"))

# CSV/JSON export
export_csv(results, out = file.path(tempdir(), "results.csv"))
export_json(results, out = file.path(tempdir(), "results.json"))

comp <- compare_with_statcheck("t(28) = 2.21, p = .035, d = 0.80")
print(comp)