# broom v0.4.4

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## Convert Statistical Analysis Objects into Tidy Data Frames

Convert statistical analysis objects from R into tidy data frames,
so that they can more easily be combined, reshaped and otherwise processed
with tools like 'dplyr', 'tidyr' and 'ggplot2'. The package provides three
S3 generics: tidy, which summarizes a model's statistical findings such as
coefficients of a regression; augment, which adds columns to the original
data such as predictions, residuals and cluster assignments; and glance, which
provides a one-row summary of model-level statistics.

## Readme

# broom: let's tidy up a bit

The broom package takes the messy output of built-in functions in R, such as `lm`

, `nls`

, or `t.test`

, and turns them into tidy data frames.

The concept of "tidy data", as introduced by Hadley Wickham, offers a powerful framework for data manipulation and analysis. That paper makes a convincing statement of the problem this package tries to solve (emphasis mine):

While model inputs usually require tidy inputs, such attention to detail doesn't carry over to model outputs. Outputs such as predictions and estimated coefficients aren't always tidy. This makes it more difficult to combine results from multiple models.For example, in R, the default representation of model coefficients is not tidy because it does not have an explicit variable that records the variable name for each estimate, they are instead recorded as row names. In R, row names must be unique, so combining coefficients from many models (e.g., from bootstrap resamples, or subgroups) requires workarounds to avoid losing important information.This knocks you out of the flow of analysis and makes it harder to combine the results from multiple models. I'm not currently aware of any packages that resolve this problem.

broom is an attempt to bridge the gap from untidy outputs of predictions and estimations to the tidy data we want to work with. It centers around three S3 methods, each of which take common objects produced by R statistical functions (`lm`

, `t.test`

, `nls`

, etc) and convert them into a data frame. broom is particularly designed to work with Hadley's dplyr package (see the "broom and dplyr" vignette for more).

broom should be distinguished from packages like reshape2 and tidyr, which rearrange and reshape data frames into different forms. Those packages perform critical tasks in tidy data analysis but focus on manipulating data frames in one specific format into another. In contrast, broom is designed to take format that is *not* in a data frame (sometimes not anywhere close) and convert it to a tidy data frame.

Tidying model outputs is not an exact science, and it's based on a judgment of the kinds of values a data scientist typically wants out of a tidy analysis (for instance, estimates, test statistics, and p-values). You may lose some of the information in the original object that you wanted, or keep more information than you need. If you think the tidy output for a model should be changed, or if you're missing a tidying function for an S3 class that you'd like, I strongly encourage you to open an issue or a pull request.

## Installation and Documentation

The broom package is available on CRAN:

```
install.packages("broom")
```

You can also install the development version of the broom package using devtools:

```
library(devtools)
install_github("tidyverse/broom")
```

For additional documentation, please browse the vignettes:

```
browseVignettes(package="broom")
```

## Tidying functions

This package provides three S3 methods that do three distinct kinds of tidying.

`tidy`

: constructs a data frame that summarizes the model's statistical findings. This includes coefficients and p-values for each term in a regression, per-cluster information in clustering applications, or per-test information for`multtest`

functions.`augment`

: add columns to the original data that was modeled. This includes predictions, residuals, and cluster assignments.`glance`

: construct a concise*one-row*summary of the model. This typically contains values such as R^2, adjusted R^2, and residual standard error that are computed once for the entire model.

Note that some classes may have only one or two of these methods defined.

Consider as an illustrative example a linear fit on the built-in `mtcars`

dataset.

```
lmfit <- lm(mpg ~ wt, mtcars)
lmfit
```

```
##
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
##
## Coefficients:
## (Intercept) wt
## 37.285 -5.344
```

```
summary(lmfit)
```

```
##
## Call:
## lm(formula = mpg ~ wt, data = mtcars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5432 -2.3647 -0.1252 1.4096 6.8727
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 37.2851 1.8776 19.858 < 2e-16 ***
## wt -5.3445 0.5591 -9.559 1.29e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.046 on 30 degrees of freedom
## Multiple R-squared: 0.7528, Adjusted R-squared: 0.7446
## F-statistic: 91.38 on 1 and 30 DF, p-value: 1.294e-10
```

This summary output is useful enough if you just want to read it. However, converting it to a data frame that contains all the same information, so that you can combine it with other models or do further analysis, is not trivial. You have to do `coef(summary(lmfit))`

to get a matrix of coefficients, the terms are still stored in row names, and the column names are inconsistent with other packages (e.g. `Pr(>|t|)`

compared to `p.value`

).

Instead, you can use the `tidy`

function, from the broom package, on the fit:

```
library(broom)
tidy(lmfit)
```

```
## term estimate std.error statistic p.value
## 1 (Intercept) 37.285126 1.877627 19.857575 8.241799e-19
## 2 wt -5.344472 0.559101 -9.559044 1.293959e-10
```

This gives you a data.frame representation. Note that the row names have been moved into a column called `term`

, and the column names are simple and consistent (and can be accessed using `$`

).

Instead of viewing the coefficients, you might be interested in the fitted values and residuals for each of the original points in the regression. For this, use `augment`

, which augments the original data with information from the model:

```
head(augment(lmfit))
```

```
## .rownames mpg wt .fitted .se.fit .resid .hat
## 1 Mazda RX4 21.0 2.620 23.28261 0.6335798 -2.2826106 0.04326896
## 2 Mazda RX4 Wag 21.0 2.875 21.91977 0.5714319 -0.9197704 0.03519677
## 3 Datsun 710 22.8 2.320 24.88595 0.7359177 -2.0859521 0.05837573
## 4 Hornet 4 Drive 21.4 3.215 20.10265 0.5384424 1.2973499 0.03125017
## 5 Hornet Sportabout 18.7 3.440 18.90014 0.5526562 -0.2001440 0.03292182
## 6 Valiant 18.1 3.460 18.79325 0.5552829 -0.6932545 0.03323551
## .sigma .cooksd .std.resid
## 1 3.067494 1.327407e-02 -0.76616765
## 2 3.093068 1.723963e-03 -0.30743051
## 3 3.072127 1.543937e-02 -0.70575249
## 4 3.088268 3.020558e-03 0.43275114
## 5 3.097722 7.599578e-05 -0.06681879
## 6 3.095184 9.210650e-04 -0.23148309
```

Note that each of the new columns begins with a `.`

(to avoid overwriting any of the original columns).

Finally, several summary statistics are computed for the entire regression, such as R^2 and the F-statistic. These can be accessed with the `glance`

function:

```
glance(lmfit)
```

```
## r.squared adj.r.squared sigma statistic p.value df logLik
## 1 0.7528328 0.7445939 3.045882 91.37533 1.293959e-10 2 -80.01471
## AIC BIC deviance df.residual
## 1 166.0294 170.4266 278.3219 30
```

This distinction between the `tidy`

, `augment`

and `glance`

functions is explored in a different context in the k-means vignette.

## Other Examples

### Generalized linear and non-linear models

These functions apply equally well to the output from `glm`

:

```
glmfit <- glm(am ~ wt, mtcars, family="binomial")
tidy(glmfit)
```

```
## term estimate std.error statistic p.value
## 1 (Intercept) 12.04037 4.509706 2.669879 0.007587858
## 2 wt -4.02397 1.436416 -2.801396 0.005088198
```

```
head(augment(glmfit))
```

```
## .rownames am wt .fitted .se.fit .resid .hat
## 1 Mazda RX4 1 2.620 1.4975684 0.9175750 0.6353854 0.12577908
## 2 Mazda RX4 Wag 1 2.875 0.4714561 0.6761141 0.9848344 0.10816226
## 3 Datsun 710 1 2.320 2.7047594 1.2799233 0.3598458 0.09628500
## 4 Hornet 4 Drive 0 3.215 -0.8966937 0.6012064 -0.8271767 0.07438175
## 5 Hornet Sportabout 0 3.440 -1.8020869 0.7486164 -0.5525972 0.06812194
## 6 Valiant 0 3.460 -1.8825663 0.7669573 -0.5323012 0.06744101
## .sigma .cooksd .std.resid
## 1 0.8033182 0.018405616 0.6795582
## 2 0.7897742 0.042434911 1.0428463
## 3 0.8101256 0.003942789 0.3785304
## 4 0.7973421 0.017706938 -0.8597702
## 5 0.8061915 0.006469973 -0.5724389
## 6 0.8067014 0.005901376 -0.5512128
```

```
glance(glmfit)
```

```
## null.deviance df.null logLik AIC BIC deviance df.residual
## 1 43.22973 31 -9.588042 23.17608 26.10756 19.17608 30
```

Note that the statistics computed by `glance`

are different for `glm`

objects than for `lm`

(e.g. deviance rather than R^2):

These functions also work on other fits, such as nonlinear models (`nls`

):

```
nlsfit <- nls(mpg ~ k / wt + b, mtcars, start=list(k=1, b=0))
tidy(nlsfit)
```

```
## term estimate std.error statistic p.value
## 1 k 45.829488 4.249155 10.785554 7.639162e-12
## 2 b 4.386254 1.536418 2.854858 7.737378e-03
```

```
head(augment(nlsfit, mtcars))
```

```
## .rownames mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
## .fitted .resid
## 1 21.87843 -0.8784251
## 2 20.32695 0.6730544
## 3 24.14034 -1.3403437
## 4 18.64115 2.7588507
## 5 17.70878 0.9912203
## 6 17.63177 0.4682291
```

```
glance(nlsfit)
```

```
## sigma isConv finTol logLik AIC BIC deviance
## 1 2.77405 TRUE 2.87694e-08 -77.02329 160.0466 164.4438 230.8606
## df.residual
## 1 30
```

### Hypothesis testing

The `tidy`

function can also be applied to `htest`

objects, such as those output by popular built-in functions like `t.test`

, `cor.test`

, and `wilcox.test`

.

```
tt <- t.test(wt ~ am, mtcars)
tidy(tt)
```

```
## estimate estimate1 estimate2 statistic p.value parameter conf.low
## 1 1.357895 3.768895 2.411 5.493905 6.27202e-06 29.23352 0.8525632
## conf.high method alternative
## 1 1.863226 Welch Two Sample t-test two.sided
```

Some cases might have fewer columns (for example, no confidence interval):

```
wt <- wilcox.test(wt ~ am, mtcars)
tidy(wt)
```

```
## statistic p.value method
## 1 230.5 4.347026e-05 Wilcoxon rank sum test with continuity correction
## alternative
## 1 two.sided
```

Since the `tidy`

output is already only one row, `glance`

returns the same output:

```
glance(tt)
```

```
## estimate estimate1 estimate2 statistic p.value parameter conf.low
## 1 1.357895 3.768895 2.411 5.493905 6.27202e-06 29.23352 0.8525632
## conf.high method alternative
## 1 1.863226 Welch Two Sample t-test two.sided
```

```
glance(wt)
```

```
## statistic p.value method
## 1 230.5 4.347026e-05 Wilcoxon rank sum test with continuity correction
## alternative
## 1 two.sided
```

There is no `augment`

function for `htest`

objects, since there is no meaningful sense in which a hypothesis test produces output about each initial data point.

### Available Tidiers

Currently broom provides tidying methods for many S3 objects from the built-in stats package, including

`lm`

`glm`

`htest`

`anova`

`nls`

`kmeans`

`manova`

`TukeyHSD`

`arima`

It also provides methods for S3 objects in popular third-party packages, including

`lme4`

`glmnet`

`boot`

`gam`

`survival`

`lfe`

`zoo`

`multcomp`

`sp`

`maps`

A full list of the `tidy`

, `augment`

and `glance`

methods available for each class is as follows:

Class | `tidy` |
`glance` |
`augment` |
---|---|---|---|

aareg | x | x | |

acf | x | ||

anova | x | ||

aov | x | ||

aovlist | x | ||

Arima | x | x | |

betareg | x | x | x |

biglm | x | x | |

binDesign | x | x | |

binWidth | x | ||

boot | x | ||

brmsfit | x | ||

btergm | x | ||

cch | x | x | |

character | x | ||

cld | x | ||

coeftest | x | ||

confint.glht | x | ||

coxph | x | x | x |

cv.glmnet | x | x | |

data.frame | x | x | x |

default | x | x | x |

density | x | ||

dgCMatrix | x | ||

dgTMatrix | x | ||

dist | x | ||

ergm | x | x | |

felm | x | x | x |

fitdistr | x | x | |

ftable | x | ||

gam | x | x | |

gamlss | x | ||

geeglm | x | ||

glht | x | ||

glmnet | x | x | |

glmRob | x | x | x |

gmm | x | x | |

htest | x | x | |

kappa | x | ||

kde | x | ||

kmeans | x | x | x |

Line | x | ||

Lines | x | ||

list | x | x | |

lm | x | x | x |

lme | x | x | x |

lmodel2 | x | x | |

lmRob | x | x | x |

logical | x | ||

lsmobj | x | ||

manova | x | ||

map | x | ||

matrix | x | x | |

Mclust | x | x | x |

merMod | x | x | x |

mle2 | x | ||

multinom | x | x | |

nlrq | x | x | x |

nls | x | x | x |

NULL | x | x | x |

numeric | x | ||

pairwise.htest | x | ||

plm | x | x | x |

poLCA | x | x | x |

Polygon | x | ||

Polygons | x | ||

power.htest | x | ||

prcomp | x | x | |

pyears | x | x | |

rcorr | x | ||

ref.grid | x | ||

ridgelm | x | x | |

rjags | x | ||

roc | x | ||

rowwise_df | x | x | x |

rq | x | x | x |

rqs | x | x | x |

sparseMatrix | x | ||

SpatialLinesDataFrame | x | ||

SpatialPolygons | x | ||

SpatialPolygonsDataFrame | x | ||

spec | x | ||

stanfit | x | ||

stanreg | x | x | |

summary.glht | x | ||

summary.lm | x | x | |

summaryDefault | x | x | |

survexp | x | x | |

survfit | x | x | |

survreg | x | x | x |

table | x | ||

tbl_df | x | x | x |

ts | x | ||

TukeyHSD | x | ||

zoo | x |

## Conventions

In order to maintain consistency, we attempt to follow some conventions regarding the structure of returned data.

### All functions

- The output of the
`tidy`

,`augment`

and`glance`

functions is*always*a data frame. - The output never has rownames. This ensures that you can combine it with other tidy outputs without fear of losing information (since rownames in R cannot contain duplicates).
- Some column names are kept consistent, so that they can be combined across different models and so that you know what to expect (in contrast to asking "is it
`pval`

or`PValue`

?" every time). The examples below are not all the possible column names, nor will all tidy output contain all or even any of these columns.

### tidy functions

- Each row in a
`tidy`

output typically represents some well-defined concept, such as one term in a regression, one test, or one cluster/class. This meaning varies across models but is usually self-evident. The one thing each row cannot represent is a point in the initial data (for that, use the`augment`

method). - Common column names include:
`term`

: the term in a regression or model that is being estimated.`p.value`

: this spelling was chosen (over common alternatives such as`pvalue`

,`PValue`

, or`pval`

) to be consistent with functions in R's built-in`stats`

package`statistic`

a test statistic, usually the one used to compute the p-value. Combining these across many sub-groups is a reliable way to perform (e.g.) bootstrap hypothesis testing`estimate`

estimate of an effect size, slope, or other value`std.error`

standard error`conf.low`

the low end of a confidence interval on the`estimate`

`conf.high`

the high end of a confidence interval on the`estimate`

`df`

degrees of freedom

### augment functions

`augment(model, data)`

adds columns to the original data.- If the
`data`

argument is missing,`augment`

attempts to reconstruct the data from the model (note that this may not always be possible, and usually won't contain columns not used in the model).

- If the
- Each row in an
`augment`

output matches the corresponding row in the original data. - If the original data contained rownames,
`augment`

turns them into a column called`.rownames`

. - Newly added column names begin with
`.`

to avoid overwriting columns in the original data. - Common column names include:
`.fitted`

: the predicted values, on the same scale as the data.`.resid`

: residuals: the actual y values minus the fitted values`.cluster`

: cluster assignments

### glance functions

`glance`

always returns a one-row data frame.- The only exception is that
`glance(NULL)`

returns an empty data frame.

- The only exception is that
- We avoid including arguments that were
*given*to the modeling function. For example, a`glm`

glance output does not need to contain a field for`family`

, since that is decided by the user calling`glm`

rather than the modeling function itself. - Common column names include:
`r.squared`

the fraction of variance explained by the model`adj.r.squared`

R^2 adjusted based on the degrees of freedom`sigma`

the square root of the estimated variance of the residuals

### Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

## Functions in broom

Name | Description | |

augment_columns | add fitted values, residuals, and other common outputs to an augment call | |

betareg_tidiers | Tidy betareg objects from the betareg package | |

binDesign_tidiers | Tidy a binDesign object | |

auc_tidiers | Tidiers for objects from the AUC package | |

bootstrap | Set up bootstrap replicates of a dplyr operation | |

anova_tidiers | Tidying methods for anova and AOV objects | |

brms_tidiers | Tidying methods for a brms model | |

aareg_tidiers | Tidiers for aareg objects | |

Arima_tidiers | Tidying methods for ARIMA modeling of time series | |

binWidth_tidiers | Tidy a binWidth object | |

biglm_tidiers | Tidiers for biglm and bigglm object | |

augment | Augment data according to a tidied model | |

broom | Convert Statistical Analysis Objects into Tidy Data Frames | |

boot_tidiers | Tidying methods for bootstrap computations | |

confint_tidy | Calculate confidence interval as a tidy data frame | |

acf_tidiers | Tidying method for the acf function | |

cch_tidiers | tidiers for case-cohort data | |

compact | Remove NULL items in a vector or list | |

gmm_tidiers | Tidying methods for generalized method of moments "gmm" objects | |

btergm_tidiers | Tidying method for a bootstrapped temporal exponential random graph model | |

geeglm_tidiers | Tidying methods for generalized estimating equations models | |

htest_tidiers | Tidying methods for an htest object | |

glance | Construct a single row summary "glance" of a model, fit, or other object | |

coxph_tidiers | Tidiers for coxph object | |

emmeans_tidiers | Tidy estimated marginal means (least-squares means) objects from the emmeans and lsmeans packages | |

cv.glmnet_tidiers | Tidiers for glmnet cross-validation objects | |

ergm_tidiers | Tidying methods for an exponential random graph model | |

confint.geeglm | Confidence interval for geeglm objects | |

fitdistr_tidiers | Tidying methods for fitdistr objects from the MASS package | |

inflate | Expand a dataset to include all factorial combinations of one or more variables | |

felm_tidiers | Tidying methods for models with multiple group fixed effects | |

data.frame_tidiers | Tidiers for data.frame objects | |

insert_NAs | insert a row of NAs into a data frame wherever another data frame has NAs | |

fix_data_frame | Ensure an object is a data frame, with rownames moved into a column | |

finish_glance | Add logLik, AIC, BIC, and other common measurements to a glance of a prediction | |

mclust_tidiers | Tidying methods for Mclust objects | |

loess_tidiers | Augmenting methods for loess models | |

decompose_tidiers | Tidying methods for seasonal decompositions | |

glm_tidiers | Tidying methods for a glm object | |

mcmc_tidiers | Tidying methods for MCMC (Stan, JAGS, etc.) fits | |

matrix_tidiers | Tidiers for matrix objects | |

gam_tidiers | Tidying methods for a generalized additive model (gam) | |

glmnet_tidiers | Tidiers for LASSO or elasticnet regularized fits | |

list_tidiers | Tidiers for return values from functions that aren't S3 objects | |

nlme_tidiers | Tidying methods for mixed effects models | |

lm_tidiers | Tidying methods for a linear model | |

nls_tidiers | Tidying methods for a nonlinear model | |

gamlss_tidiers | Tidying methods for gamlss objects | |

lme4_tidiers | Tidying methods for mixed effects models | |

rcorr_tidiers | Tidying methods for rcorr objects | |

ivreg_tidiers | Tidiers for ivreg models | |

lmodel2_tidiers | Tidiers for linear model II objects from the lmodel2 package | |

ridgelm_tidiers | Tidying methods for ridgelm objects from the MASS package | |

rowwise_df_tidiers | Tidying methods for rowwise_dfs from dplyr, for tidying each row and recombining the results | |

kappa_tidiers | Tidy a kappa object from a Cohen's kappa calculation | |

optim_tidiers | Tidiers for lists returned from optim | |

rq_tidiers | Tidying methods for quantile regression models | |

process_rq | Helper function for tidy.rq and tidy.rqs | |

mle2_tidiers | Tidy mle2 maximum likelihood objects | |

orcutt_tidiers | Tidiers for Cochrane Orcutt object | |

pyears_tidiers | Tidy person-year summaries | |

muhaz_tidiers | Tidying methods for kernel based hazard rate estimates | |

plm_tidiers | Tidiers for panel regression linear models | |

xyz_tidiers | Tidiers for x, y, z lists suitable for persp, image, etc. | |

sparse_tidiers | Tidy a sparseMatrix object from the Matrix package | |

prcomp_tidiers | Tidying methods for principal components analysis via prcomp | |

zoo_tidiers | Tidying methods for a zoo object | |

speedlm_tidiers | Tidying methods for a speedlm model | |

poLCA_tidiers | Tidiers for poLCA objects | |

tidy | Tidy the result of a test into a summary data.frame | |

summary_tidiers | Tidiers for summaryDefault objects | |

process_ergm | helper function to process a tidied ergm object | |

tidy.TukeyHSD | tidy a TukeyHSD object | |

rlm_tidiers | Tidying methods for an rlm (robust linear model) object | |

survdiff_tidiers | Tidiers for Tests of Differences between Survival Curves | |

svd_tidiers | Tidying methods for singular value decomposition | |

tidy.map | Tidy method for map objects. | |

kde_tidiers | Tidy a kernel density estimate object from the ks package | |

robust_tidiers | Tidiers for lmRob and glmRob objects | |

tidy.pairwise.htest | tidy a pairwise hypothesis test | |

kmeans_tidiers | Tidying methods for kmeans objects | |

smooth.spline_tidiers | tidying methods for smooth.spline objects | |

survfit_tidiers | tidy survival curve fits | |

tidy.power.htest | tidy a power.htest | |

multcomp_tidiers | tidying methods for objects produced by multcomp | |

sp_tidiers | tidying methods for classes from the sp package. | |

survreg_tidiers | Tidiers for a parametric regression survival model | |

multinom_tidiers | Tidying methods for multinomial logistic regression models | |

tidy.spec | tidy a spec objet | |

tidy.ftable | tidy an ftable object | |

process_geeglm | helper function to process a tidied geeglm object | |

tidy.table | tidy a table object | |

tidy.manova | tidy a MANOVA object | |

sexpfit_tidiers | Tidy an expected survival curve | |

process_lm | helper function to process a tidied lm object | |

tidy.ts | tidy a ts timeseries object | |

rstanarm_tidiers | Tidying methods for an rstanarm model | |

tidy.coeftest | Tidying methods for coeftest objects | |

tidy.NULL | tidy on a NULL input | |

tidy.default | Default tidying method | |

tidy.density | tidy a density objet | |

tidy.dist | Tidy a distance matrix | |

unrowname | strip rownames from an object | |

tidy.numeric | Tidy atomic vectors | |

No Results! |

## Vignettes of broom

Name | ||

bootstrapping.Rmd | ||

broom.Rmd | ||

broom_and_dplyr.Rmd | ||

kmeans.Rmd | ||

No Results! |

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## Details

Type | Package |

Date | 2018-03-12 |

URL | http://github.com/tidyverse/broom |

BugReports | http://github.com/tidyverse/broom/issues |

VignetteBuilder | knitr |

License | MIT + file LICENSE |

RoxygenNote | 6.0.1 |

NeedsCompilation | no |

Packaged | 2018-03-28 22:56:14 UTC; drobinson |

Repository | CRAN |

Date/Publication | 2018-03-29 15:39:33 UTC |

suggests | AER , akima , AUC , bbmle , betareg , biglm , binGroup , boot , brms , btergm , coda , covr , emmeans , ergm , gam , gamlss , geepack , ggplot2 , glmnet , gmm , Hmisc , knitr , ks , Lahman , lfe , lme4 , lmodel2 , lmtest , lsmeans , maps , maptools , MASS , Matrix , mclust , mgcv , muhaz , multcomp , network , nnet , orcutt , plm , poLCA , purrr , rgeos , robust , rstan , rstanarm , sp , speedglm , statnet.common , survival , testthat , tibble , xergm , zoo |

imports | dplyr , methods , nlme , plyr , psych , reshape2 , stringr , tidyr |

Contributors | Benjamin Nutter, Luciano Selzer, Matthieu Gomez, Francois Briatte, Matthew Lincoln, Boris Demeshev, Jonah Gabry, Hadley Wickham, Jeffrey B. Arnold, Dieter Menne, Ben Bolker, Gavin Simpson, Luke Johnston, Jens Preussner, Jay Hesselberth, Derek Chiu |

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