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

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”

statpsych 1.0.0 (Release date: 2021/09/09)

• Initial release