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116 changes: 58 additions & 58 deletions DESCRIPTION
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Package: sjstats
Type: Package
Encoding: UTF-8
Title: Collection of Convenient Functions for Common Statistical Computations
Version: 0.9.0.9000
Date: 2017-03-05
Author: Daniel Lüdecke <d.luedecke@uke.de>
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>
Description: Collection of convenient functions for common statistical computations,
which are not directly provided by R's base or stats packages.
This package aims at providing, first, shortcuts for statistical
measures, which otherwise could only be calculated with additional
effort (like standard errors or root mean squared errors). Second,
these shortcut functions are generic (if appropriate), and can be
applied not only to vectors, but also to other objects as well
(e.g., the Coefficient of Variation can be computed for vectors,
linear models, or linear mixed models; the r2()-function returns
the r-squared value for 'lm', 'glm', 'merMod' or 'lme' objects).
The focus of most functions lies on summary statistics or fit
measures for regression models, including generalized linear
models and mixed effects models. However, some of the functions
also deal with other statistical measures, like Cronbach's Alpha,
Cramer's V, Phi etc.
License: GPL-3
Depends:
R (>= 3.2),
stats,
utils
Imports:
broom,
coin,
dplyr (>= 0.5.0),
lme4 (>= 1.1-12),
lmtest (>= 0.9-34),
MASS,
Matrix,
modelr,
nlme,
purrr (>= 0.2.2),
sandwich (>= 2.3-4),
sjmisc (>= 2.3.1),
tidyr (>= 0.6.1),
tibble (>= 1.3.0)
Suggests:
AER,
arm,
car,
ggplot2,
graphics,
Hmisc,
lmerTest,
pbkrtest (>= 0.4-7),
pROC,
pwr,
survey
URL: https://github.com/strengejacke/sjstats
BugReports: https://github.com/strengejacke/sjstats/issues
RoxygenNote: 6.0.1
Package: sjstats
Type: Package
Encoding: UTF-8
Title: Collection of Convenient Functions for Common Statistical Computations
Version: 0.10.0
Date: 2017-04-10
Author: Daniel Lüdecke <d.luedecke@uke.de>
Maintainer: Daniel Lüdecke <d.luedecke@uke.de>
Description: Collection of convenient functions for common statistical computations,
which are not directly provided by R's base or stats packages.
This package aims at providing, first, shortcuts for statistical
measures, which otherwise could only be calculated with additional
effort (like standard errors or root mean squared errors). Second,
these shortcut functions are generic (if appropriate), and can be
applied not only to vectors, but also to other objects as well
(e.g., the Coefficient of Variation can be computed for vectors,
linear models, or linear mixed models; the r2()-function returns
the r-squared value for 'lm', 'glm', 'merMod' or 'lme' objects).
The focus of most functions lies on summary statistics or fit
measures for regression models, including generalized linear
models and mixed effects models. However, some of the functions
also deal with other statistical measures, like Cronbach's Alpha,
Cramer's V, Phi etc.
License: GPL-3
Depends:
R (>= 3.2),
stats,
utils
Imports:
broom,
coin,
dplyr (>= 0.5.0),
lme4 (>= 1.1-12),
lmtest (>= 0.9-34),
MASS,
Matrix,
modelr,
nlme,
purrr (>= 0.2.2),
sandwich (>= 2.3-4),
sjmisc (>= 2.4.0),
tidyr (>= 0.6.1),
tibble (>= 1.3.0)
Suggests:
AER,
arm,
car,
ggplot2,
graphics,
Hmisc,
lmerTest,
pbkrtest (>= 0.4-7),
pROC,
pwr,
survey
URL: https://github.com/strengejacke/sjstats
BugReports: https://github.com/strengejacke/sjstats/issues
RoxygenNote: 6.0.1
248 changes: 124 additions & 124 deletions NEWS
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Version 0.9.0.9000
------------------------------------------------------------------------------
New functions:
* `cv_error()` and `cv_compare()` to compute the root mean squared error for test and training data from cross-validation.
* `props()` to calculate proportions in a vector, supporting multiple logical statements.
* `or_to_rr()` to convert odds ratio estimates into risk ratio estimates.
* `mn()`, `md()` and `sm()` to calculate mean, median or sum of a vector, but using `na.rm = TRUE` as default.
* S3-generics for `svyglm.nb`-models: `family()`, `print()`, `formula()`, `model.frame()` and `predict()`.
Bug fixes:
* Fixed error in computation of `mse()`.
Version 0.9.0
------------------------------------------------------------------------------
General:
* Functions `std()` and `center()` were removed and are now in the sjmisc-package (https://cran.r-project.org/package=sjmisc).
New functions:
* `svyglm.nb()` to compute survey-weighted negative binomial regressions.
* `xtab_statistics()` to compute various measures of assiciation for contingency tables.
* Added S3-`model.frame()`-function for `gee`-models.
Changes to functions:
* `se()` gets a `type`-argument, which applies to generalized linear mixed models. You can now choose to compute either standard errors with delta-method approximation for fixed effects only, or standard errors for joint random and fixed effects.
Bug fixes:
* `prop()` did not work for non-labelled data frames when used with grouped data frames.
Version 0.8.0
------------------------------------------------------------------------------
New functions:
* `svy()` to compute robust standard errors for weighted models, adjusting the residual degrees of freedom to simulate sampling weights.
* `zero_count()` to check whether a poisson-model is over- or underfitting zero-counts in the outcome.
* `pred_accuracy()` to calculate accuracy of predictions from model fit.
* `outliers()` to detect outliers in (generalized) linear models.
* `heteroskedastic()` to check linear models for (non-)constant error variance.
* `autocorrelation()` to check linear models for auto-correlated residuals.
* `normality()` to check whether residuals in linear models are normally distributed or not.
* `multicollin()` to check predictors in a model for multicollinearity.
* `check_assumptions()` to run a set of model assumption checks.
Changes to functions:
* `prop()` no longer works within dplyr's `summarise()` function. Instead, when now used with grouped data frames, a summary of proportions is directly returned as tibble.
* `se()` now computes adjusted standard errors for generalized linear (mixed) models, using the Taylor series-based delta method.
Version 0.7.1
------------------------------------------------------------------------------
General:
* Package depends on R-version >= 3.3.
Changes to functions:
* `prop()` gets a `digits`-argument to round the return value to a specific number of decimal places.
Version 0.7.0
------------------------------------------------------------------------------
General:
* Largely revised the documentation.
New functions:
* `prop()` to calculate proportion of values in a vector.
* `mse()` to calculate the mean square error for models.
* `robust()` to calculate robust standard errors and confidence intervals for regression models, returned as tidy data frame.
Version 0.6.0
------------------------------------------------------------------------------
New functions:
* `split_half()` to compute the split-half-reliability of tests or questionnaires.
* `sd_pop()` and `var_pop()` to compute population variance and population standard deviation.
Changes to functions:
* `se()` now also computes the standard error from estimates (regression coefficients) and p-values.
Version 0.5.0
------------------------------------------------------------------------------
New functions:
* Added S3-`print`-method for `mwu()`-function.
* `get_model_pval()` to return a tidy data frame (tibble) of model term names, p-values and standard errors from various regression model types.
* `se_ybar()` to compute standard error of sample mean for mixed models, considering the effect of clustering on the standard error.
* `std()` and `center()` to standardize and center variables, supporting the pipe-operator.
Changes to functions:
* `se()` now also computes the standard error for intraclass correlation coefficients, as returned by the `icc()`-function.
* `std_beta()` now always returns a tidy data frame (tibble) with model term names, standardized estimate, standard error and confidence intervals.
* `r2()` now also computes alternative omega-squared-statistics, if null model is given.
Version 0.4.0
------------------------------------------------------------------------------
New functions:
* `inequ_trend()` to calculate proportional change of absolute and relative inequalities between two status groups for a vector of given prevalence rates.
Changes to functions:
* `bootstrap()` is now much more memory efficient due to use of pointers.
* `boot_ci()`, `boot_se()` and `boot_p()` now accept multiple variables as input.
* `resp_val()` now also applies to models fitted with `nlme::lme()`.
Version 0.3.0
------------------------------------------------------------------------------
General:
* Removed non-necessary checks for package-availability.
New functions:
* `bootstrap()` to generate bootstrap replicates of data frames.
* `boot_ci()` to compute confidence intervals from bootstrapped values.
* `pred_vars()` to get the names of predictor variables from fitted models.
* `resp_var()` to get the name of the response variable from fitted models.
* `resp_val()` to get the values of the response vector from fitted models.
Version 0.2.0
------------------------------------------------------------------------------
New functions:
* Added functions `weight()` and `weight2()` to weight vectors.
* Added functions `wtd_sd()` and `wtd_se()` to compute weighted standard deviations and standard errors.
* Added function `merMod_p()` to compute p-values for merMod-objects.
Changes to functions:
* `r2()` now supports `plm` objects.
Bug fixes:
* Fixed typo in print-method for `icc()`.
Version 0.1.0
------------------------------------------------------------------------------
General:
* Initial release on CRAN.
Version 0.10.0
------------------------------------------------------------------------------
New functions:
* `cv_error()` and `cv_compare()` to compute the root mean squared error for test and training data from cross-validation.
* `props()` to calculate proportions in a vector, supporting multiple logical statements.
* `or_to_rr()` to convert odds ratio estimates into risk ratio estimates.
* `mn()`, `md()` and `sm()` to calculate mean, median or sum of a vector, but using `na.rm = TRUE` as default.
* S3-generics for `svyglm.nb`-models: `family()`, `print()`, `formula()`, `model.frame()` and `predict()`.

Bug fixes:
* Fixed error in computation of `mse()`.

Version 0.9.0
------------------------------------------------------------------------------
General:
* Functions `std()` and `center()` were removed and are now in the sjmisc-package (https://cran.r-project.org/package=sjmisc).

New functions:
* `svyglm.nb()` to compute survey-weighted negative binomial regressions.
* `xtab_statistics()` to compute various measures of assiciation for contingency tables.
* Added S3-`model.frame()`-function for `gee`-models.

Changes to functions:
* `se()` gets a `type`-argument, which applies to generalized linear mixed models. You can now choose to compute either standard errors with delta-method approximation for fixed effects only, or standard errors for joint random and fixed effects.

Bug fixes:
* `prop()` did not work for non-labelled data frames when used with grouped data frames.

Version 0.8.0
------------------------------------------------------------------------------
New functions:
* `svy()` to compute robust standard errors for weighted models, adjusting the residual degrees of freedom to simulate sampling weights.
* `zero_count()` to check whether a poisson-model is over- or underfitting zero-counts in the outcome.
* `pred_accuracy()` to calculate accuracy of predictions from model fit.
* `outliers()` to detect outliers in (generalized) linear models.
* `heteroskedastic()` to check linear models for (non-)constant error variance.
* `autocorrelation()` to check linear models for auto-correlated residuals.
* `normality()` to check whether residuals in linear models are normally distributed or not.
* `multicollin()` to check predictors in a model for multicollinearity.
* `check_assumptions()` to run a set of model assumption checks.

Changes to functions:
* `prop()` no longer works within dplyr's `summarise()` function. Instead, when now used with grouped data frames, a summary of proportions is directly returned as tibble.
* `se()` now computes adjusted standard errors for generalized linear (mixed) models, using the Taylor series-based delta method.

Version 0.7.1
------------------------------------------------------------------------------
General:
* Package depends on R-version >= 3.3.

Changes to functions:
* `prop()` gets a `digits`-argument to round the return value to a specific number of decimal places.

Version 0.7.0
------------------------------------------------------------------------------
General:
* Largely revised the documentation.

New functions:
* `prop()` to calculate proportion of values in a vector.
* `mse()` to calculate the mean square error for models.
* `robust()` to calculate robust standard errors and confidence intervals for regression models, returned as tidy data frame.

Version 0.6.0
------------------------------------------------------------------------------
New functions:
* `split_half()` to compute the split-half-reliability of tests or questionnaires.
* `sd_pop()` and `var_pop()` to compute population variance and population standard deviation.

Changes to functions:
* `se()` now also computes the standard error from estimates (regression coefficients) and p-values.

Version 0.5.0
------------------------------------------------------------------------------
New functions:
* Added S3-`print`-method for `mwu()`-function.
* `get_model_pval()` to return a tidy data frame (tibble) of model term names, p-values and standard errors from various regression model types.
* `se_ybar()` to compute standard error of sample mean for mixed models, considering the effect of clustering on the standard error.
* `std()` and `center()` to standardize and center variables, supporting the pipe-operator.

Changes to functions:
* `se()` now also computes the standard error for intraclass correlation coefficients, as returned by the `icc()`-function.
* `std_beta()` now always returns a tidy data frame (tibble) with model term names, standardized estimate, standard error and confidence intervals.
* `r2()` now also computes alternative omega-squared-statistics, if null model is given.

Version 0.4.0
------------------------------------------------------------------------------
New functions:
* `inequ_trend()` to calculate proportional change of absolute and relative inequalities between two status groups for a vector of given prevalence rates.

Changes to functions:
* `bootstrap()` is now much more memory efficient due to use of pointers.
* `boot_ci()`, `boot_se()` and `boot_p()` now accept multiple variables as input.
* `resp_val()` now also applies to models fitted with `nlme::lme()`.

Version 0.3.0
------------------------------------------------------------------------------
General:
* Removed non-necessary checks for package-availability.

New functions:
* `bootstrap()` to generate bootstrap replicates of data frames.
* `boot_ci()` to compute confidence intervals from bootstrapped values.
* `pred_vars()` to get the names of predictor variables from fitted models.
* `resp_var()` to get the name of the response variable from fitted models.
* `resp_val()` to get the values of the response vector from fitted models.

Version 0.2.0
------------------------------------------------------------------------------
New functions:
* Added functions `weight()` and `weight2()` to weight vectors.
* Added functions `wtd_sd()` and `wtd_se()` to compute weighted standard deviations and standard errors.
* Added function `merMod_p()` to compute p-values for merMod-objects.

Changes to functions:
* `r2()` now supports `plm` objects.

Bug fixes:
* Fixed typo in print-method for `icc()`.

Version 0.1.0
------------------------------------------------------------------------------
General:
* Initial release on CRAN.
2 changes: 1 addition & 1 deletion NEWS.md
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# sjstats 0.9.0.9000
# sjstats 0.10.0

## New functions

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