statpsych version
1.9.0 (Release date: 2026/01/01)
Changes:
New functions:
size.ci.sd – Computes sample size for a traditional standard
deviation confidence interval
size.ci.cronbach.prior – Computes sample size for a Cronbach
reliablity confidence interval using a planning value from a prior
study
size.ci.icc.prior – Computes sample size for an intraclass
correlation confidence interval using a planning value from a prior
study
size.ci.icc – Computes sample size for an intraclass correlation
confidence interval
ci.diversity – Computes estimates and confidence intervals for three
types of diversity indices
ci.mean.gen – Computes confidence intervals for three types of
generalized means
ci.sd – Computes the traditional confidence interval for a
population standard deviation
ci.lc.prop.scheffe – Computes a Scheffe confidence interval and
p-value for a linear contrast of population proportions
ci.lc.mean.scheffe – Computes a Scheffe confidence interval and
p-value for a linear contrast of population means
ci.icc – Computes intraclass reliability coefficients using mean
square estimates as input
ci.slope – Computes confidence interval for slope in a simple linear
model using summary statistics as input
pi.cronbach – Computes a prediction interval for a Cronbach
reliability in a future sample
References Manual
Most function descriptions now refer to a specific section in the
Bonett (2021) online text for additional details
Error Corrections:
size.ci.prop.prior – corrected a error in the sample size
computation
statpsych version
1.8.0 (Release date: 2025/06/06)
Changes:
New functions:
test.mono.median.bs – Hypothesis test of a monotonic trend in
medians for an ordered between-subjects factor
ci.slope.median.bs – Computes confidence interval for the slope of
medians in a one-factor experimental design with a quantitative
between-subjects factor
size.ci.cv – Computes sample size for a coefficient of variation
confidence interval in a 1-group design
size.ci.median – Computes sample size for a median confidence
interval in a 1-group design
size.ci.median2 – Computes sample size for a median difference
confidence interval in a 2-group design
size.ci.lc.median.bs – Computes sample size for a linear contrast of
medians confidence interval in a between-subjects design
size.ci.gen – Computes sample size for a confidence interval for any
type of parameter in a 1-group design
size.ci.gen2 – Computes sample size for a confidence interval for
the difference in any type of parameter in a 2-group design
size.test.gen – Computes sample size for a test of any type of
parameter in a 1-group design
size.test.gen2 – Computes sample size for a test of the difference
of any type of parameter in a 2-group design
Modifications:
ci.prop now adds an exact confidence interval
Deleted functions:
ci.mean1 has been replaced with ci.mean
ci.prop1 has been replaced with ci.prop
ci.median1 has been replaced with ci.median
ci.stdmean1 has been replaced with ci.stdmean
pi.var.upper has been replaced with pi.var
statpsych version
1.7.0 (Release date: 2024/12/17)
Changes:
New functions:
size.ci.oddsratio – Computes sample size for an odds ratio
confidence interval
size.ci.yule – Computes sample size for a Yule’s Q confidence
interval
size.ci.phi – Computes sample size for a phi correlation confidence
interval
size.ci.biphi – Computes sample size for a biserial-phi correlation
confidence interval
size.ci.ancova2 – Computes sample size for a 2-group ANCOVA
confidence interval
size.ci.slope.gen – Computes sample size for a slope coefficient
confidence interval in a general model
size.test.ancova2 – Computes sample size for a 2-group ANCOVA
hypothesis test
size.test.slope.gen – Computes sample size for a slope coefficient
hypothesis test in a general model
signal – Computes parameter estimates in a Yes/No signal detection
study
exp.slope – Computes confidence intervals for exp(B) and 100(exp(B)
- 1)%
ci.bayes.cor – Computes Bayesian credible interval for a Pearson or
partial correlation with a skeptical prior
ci.bayes.spcor – Computes Bayesian credible interval for a
semipartial correlation with a skeptical prior
pi.var – Computes one-sided or two-sided prediction limits for an
estimated variance in a future study (will replace pi.var.upper)
Modifications
size.ci.prop2 can now solve for equal or unequal sample sizes.
Requires a new argumnet, R, specifying the ratio of sample sizes and now
returns a 2-column matrix.
size.ci.ratio.prop2 can now solve for equal or unequal sample sizes.
Requires a new argument, R, specifying the ratio of sample sizes and now
returns a 2-column matrix.
size.test.cor2 can now solve for equal or unequal sample sizes.
Requires a new argument, R, specifying the ratio of sample sizes, and
returns a 2-column matrix
pi.cor now has options for one-sided and two-sided prediction
limits. It requires a new argument, type
pi.prop now has options for one-sided and two-sided prediction
limits. It requires a new argument, type
the definition of the subscripts in the ci.2x2.mean.mixed,
ci.2x2.median.mixed, and ci.2x2.stdmean.mixed functions have been
changed so that the first subscript now specifies factor A and the
second subscript specified factor B.
Error Corrections:
ci.2x2.stdmean.mixed – corrected an error in the standard error
computation
size.test.lc.ancova – corrected a minor error in the sample size
formula
statpsych version
1.6.0 (Release date: 2024/07/08)
Changes:
New functions:
ci.mean.fpc – Computes confidence interval for a mean with a finite
population correction
ci.prop.fpc – Computes confidence interval for a proportion with a
finite population correction
ci.poisson – Computes confidence interval for a Poisson rate
ci.ratio.poisson2 – Computes confidence interval for a ratio of
Poisson rates in a 2-group design
ci.bscor – Computes confidence interval for a biserial
correlation
pi.cor – Computes prediction interval for an estimated
correlation
pi.prop – Computes prediction interval for an estimated
proportion
test.cor – Hypothesis test for a Pearson or partial correlation (for
zero or non-zero null hypotheses)
test.spear – Hypothesis test for a Spearman correlation (for zero or
non-zero null hypotheses)
test.cor2 – Hypothesis test for a 2-group Pearson or partial
correlation difference
test.spear2 – Hypothesis test for a 2-group Spearman correlation
difference
test.mean – Hypothesis test for a mean using summary
information
size.ci.cor2 – Computes sample size for a 2-group Pearson
correlation difference confidence interval
size.ci.spear2 – Computes sample size for a 2-group Spearman
correlation difference confidence interval
size.ci.tetra – Computes sample size for a tetrachoric correlation
confidence interval
size.ci.mean.prior – Computes sample size for a mean confidence
interval using a planning value from a prior study
size.ci.prop.prior – Computes sample size for a proportion
confidence interval using a planning value from a prior study
size.ci.cor.prior – Computes sample size for a correlation
confidence interval using a planning value from a prior study
adj.se – Computes adjusted standard errors for slope coefficients in
an exploratory analysis
fitindices – Computes four SEM fit indices
Modifications:
ci.var.upper now computes an exact upper limit rather than an
approximate upper limit
power computations are now more accurate for very small effect sizes
in the power.cor, power.cor2, power.lc.meanc.bs, power.mean,
power.mean2, power.mean.ps, power.prop, power.pro2, and power.prop.ps
functions
size.test.prop and size.test.prop2 now assume the test statistic
will use a continuity correction
one-group function names that end with a “1” have been renamed and
now exclude the “1” (for naming consistency and to avoid confusion with
lower case L).
ci.mape2 has been renamed ci.ratio.mape2, and ci.cod2 has been
renamed ci.ratio.cod2
The ci.phi function now uses a Fisher transformation for improved
coverage probability performance
Deletions:
size.test.slope.gen removed
statpsych version
1.5.0 (Release date: 2023/12/11)
Changes:
New functions:
ci.cv1 – Computes confidence interval for a coefficient of
variation
ci.ratio.cv2 – Computes confidence interval for a ratio of
coefficients of variation
ci.pv – Computes confidence intervals for positive and negative
predictive values with retrospective sampling
ci.2x2.stdmean.ws – Computes confidence intervals of standardized
effects in a 2x2 within-subjects design
ci.2x2.stdmean.mixed – Computes confidence intervals of standardized
effects in a 2x2 mixed design
ci.2x2.median.ws – Computes confidence intervals of effects in a 2x2
within-subjects design for medians
ci.2x2.median.mixed – Computes confidence intervals of effects in a
2x2 mixed design
for medians
spearmanbrown – Computes the reliability of a scale with r2
measurements given the reliability of a scale with r1 measurements
size.ci.spear – Computes the sample size requirement for a Spearman
correlation confidence interval
size.ci.pbcor – Computes the sample size requirement for a
point-biserial correlation confidence interval
size.ci.mape1 – Computes the sample size requirement for a mean
absolute prediction error confidence interval
Error Corrections:
ci.cramer – corrected an error in the CI computation
ci.lc.stdmean.ws – corrected an error in the standard error
computation
Modifications:
both biased and bias adjusted estimates are now reported in
ci.stdmean1, ci.stdmean2, ci.stdmean.ps, ci.stdmean.strat, and
ci.2x2.stdmean.bs
ci.mape has been renamed ci.mape1
statpsych version
1.4.0 (Release date: 2023/06/26)
Changes:
New functions:
power.prop1 – Computes power for 1-sample test of proportion for a
planned sample size
power.prop2 – Computes power for 2-sample test of proportion for
planned sample sizes
power.prop.ps – Computes power for paired-samples test of proportion
for a planned sample size
power.mean1 – Computes power for 1-sample t-test for a planned
sample size
power.mean2 – Computes power for 2-sample t-test for planned sample
sizes
power.mean.ps – Computes power for paired-samples t-test for a
planned sample size
power.lc.mean.bs – Computes power of a test for a linear contrast of
means for planned sample sizes in a between-subjects design
power.cor1 – Computes power for 1-sample test of correlation for a
planned sample size
power.cor2 – Computes power for 2-sample test of correlations for
planned sample sizes
ci.cqv1 – Computes confidence interval for a population coefficient
of qualitative variation
ci.prop1.inv – Computes confidence interval for a population
proportion using inverse
sampling
ci.prop2.inv – Computes confidence interval for a difference in
population proportions using inverse sampling
ci.agree.3rater – Computes confidence intervals for a 3-rater design
with dichotomous ratings
ci.ratio.sd2 – Computes robust confidence interval for ratio of
standard deviations in a 2-group design
size.test.cor2 – Computes sample size for a test of equal Pearson or
partial correlation in a 2-group design
size.test.cronbach2 – Computes sample size to test equality of
Cronbach reliability
coefficients in a 2-group design
size.ci.cronbach2 – Computes sample size for a 2-group Cronbach
reliability difference confidence interval
size.ci.etasqr – Computes sample size for an eta-squared confidence
interval
size.ci.indirect – Computes sample size for an indirect effect
confidence interval
ci.mape2 – Computes confidence interval for a ratio of mean absolute
prediction errors in a 2-group design
ci.rel2 – Computes confidence interval for a 2-group reliability
difference
ci.cronbach2 – Computes confidence interval for a difference in
Cronbach reliabilities in
a 2-group design
ci.2x2.stdmean.bs – Computes confidence intervals of standardized
effects in a 2x2 between-subjects design for means
ci.2x2.median.bs – Computes confidence intervals of effects in a 2x2
between-subjects design for medians
pi.var.upper – Computes upper prediction limit for an estimated
variance
ci.bayes.normal – Computes Bayesian credible interval for any
parameter estimator with a normal sampling distributuion using a Normal
prior distribution
ci.bayes.prop1 – Computes Bayesian credible interval for a single
proportion using a Beta prior distribution
Modifications:
Corrected Example output in ci.reliability and ci.prop.ps
SE added to output in: ci.cronbach, ci.oddsratio, ci.yule,
ci.etasqr, ci.rsqr, ci.spear2, ci.cor2, ci.cor.dep, ci.cod1, ci.mad1,
ci.mape, ci.agree2, ci.pbcor, and ci.tetra
Improved accuracy in size.ci.rsqr
Three generalized Yule coefficients added to ci.yule
The ci.prop.ps, ci.ratio.prop.ps, and ci.2x2.prop.mixed functions
now define proportions for the y = 1 category rather than the y = 0
category.
statpsych v1.3.0
(Release date: 2023/01/01)
Changes:
New functions:
ci.theil – Theil-Sen estimate and confidence interval for slope
sim.ci.median2 – Simulates confidence interval coverage probability
for a median difference in a two-group design
sim.ci.median.ps – Simulates confidence interval coverage
probability for a median difference in a paired design
sim.ci.stdmean2 – Simulates confidence interval coverage probability
for a standardized mean difference in a two-group design
pi.score.ps – Prediction interval for difference of scores in a
2-level within-subjects experiment
Updated outputs:
ci.cod1 – first column is ‘Estimate’, no longer ‘COD’
ci.cod2 – first column is ‘Estimate’, no longer ‘COD1’
ci.cramer – first column is ‘Estimate’, no longer ‘Cramer’s V’
ci.lc.stdmean.bs – now returns 3 rows, adding sample size for group
1 standardizer
ci.lc.stdmean.ws – now returns two rows, one for each
standardizer
ci.mad1 – first column is ‘Estimate’, no longer ‘MAD’
ci.mape – first column is ‘Estimate’, no longer ‘MAPE’
size.ci.lc.stdmean.bs – now returns two rows, one for each
standardizer
size.ci.lc.stdmean.ws – now returns two rows, one for each
standardizer
size.ci.stdmean2 – now returns two rows, one for each
standardizer
size.ci.stdmean.ps – now returns two rows, one for each
standardizer
ci.mann – now returns a confidence interval for P(y1 > y2) rather
than P(y1 < y2).
statpsych v1.2.0
(Release date: 2022/08/15)
Changes:
New functions:
ci.cramer - Confidence interval for Cramer’s V
ci.2x2.mean.bs - Confidence intervals for effects in a 2x2
between-subjects design for means
ci.2x2.mean.ws - Confidence intervals for effects in a 2x2
within-subjects design for means
ci.2x2.mean.mixed - Confidence intervals for effects in a 2x2 mixed
design for means
ci.2x2.prop.bs - Confidence intervals for effects in a 2x2
between-subjects design for proportions
ci.2x2.prop.mixed - Confidence intervals for effects in a 2x2 mixed
design for proportions
sim.ci.mean1 – Simulation of confidence interval for a mean
sim.ci.mean2 – Simulation of confidence interval for mean difference
in a two-group design
sim.ci.mean.ps – Simulation of confidence interval for mean
difference in a paired-samples design
sim.ci.median1 – Simulation of confidence interval for a single
median
sim.ci.cor – Simulation of confidence interval for a Pearson
correlation
sim.ci.spear – Simulation of confidence interval for a Spearman
correlation
Modifications:
The ci.prop.ps function now outputs an adjusted point estimate of
the proportion difference, as stated in the documentation, rather than
an unadjusted estimate
The ci.cor, ci.cor2, and ci.cor.dep functions now uses a bias
adjustment to reduce the bias of the Fisher transformed
correlations
The ci.median1 function now uses the same standard error formula as
the ci.median2, ci.ratio.median2, and ci.median.ps functions
Error Correction:
ci.indirect – Corrected an error in the standard error
computation
statpsych v1.1.0
(Release date: 2022/06/30)
Changes:
New functions:
ci.agree2 - Confidence interval for G-index difference in a 2-group
design
ci.cod2 - Confidence interval for a ratio of dispersion coefficients
in a 2-group
ci.etasqr - Confidence interval for eta-squared
ci.lc.gen.bs - Confidence interval for a linear contrast of
parameters in a between-subjects design
ci.lc.glm - Confidence interval for a linear contrast of general
linear model parameters
ci.reliability - Confidence interval for a reliability
coefficient
ci.rsqr - Confidence interval for squared multiple correlation
ci.sign1 - Confidence interval for the parameter of the one-sample
sign test
ci.slope.mean.bs - Confidence interval for the slope of means in a
single-factor design with a quantitative between-subjects factor
test.kurtosis - Computes Monte Carlo p-value for test of excess
kurtosis
test.skew - Computes Monte Carlo p-value for test of skewness
test.mono.mean.bs - Test of a monotonic trend in means for an
ordered between-subjects factor
test.mono.prop.bs - Test of monotonic trend in proportions for an
ordered between-subjects
etasqr.gen.2way - Computes generalized eta-squared estimates in a
two-factor design
Updated documentation for consistency
Changed arguments for some functions for consistency
size.test.cronbach now takes (alpha, pow, rel, r, h) rather than
(alpha, pow, rel, a, h)
ci.cronbach now takes (alpha, rel, r, n) rather than (alpha, rel, a,
n)
Changed some of the column names in returned matrixes for
consistency:
ci.median.ps, the last column is now “COV” rather than “cov”