Support
Quality
Security
License
Reuse
Coming Soon for all Libraries!
Currently covering the most popular Java, JavaScript and Python libraries. See a SAMPLE HERE.
kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
default
© Contributors, 2021. Licensed under an [Apache-2](https://github.com/dmlc/xgboost/blob/master/LICENSE) license.
Contribute to XGBoost
---------------------
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.
Checkout the [Community Page](https://xgboost.ai/community).
Reference
---------
- Tianqi Chen and Carlos Guestrin. [XGBoost: A Scalable Tree Boosting System](http://arxiv.org/abs/1603.02754). In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington.
Sponsors
--------
Become a sponsor and get a logo here. See details at [Sponsoring the XGBoost Project](https://xgboost.ai/sponsors). The funds are used to defray the cost of continuous integration and testing infrastructure (https://xgboost-ci.net).
## Open Source Collective sponsors
[](#backers) [](#sponsors)
### Sponsors
[[Become a sponsor](https://opencollective.com/xgboost#sponsor)]
<!--<a href="https://opencollective.com/xgboost/sponsor/0/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/0/avatar.svg"></a>-->
<a href="https://www.nvidia.com/en-us/" target="_blank"><img src="https://raw.githubusercontent.com/xgboost-ai/xgboost-ai.github.io/master/images/sponsors/nvidia.jpg" alt="NVIDIA" width="72" height="72"></a>
<a href="https://opencollective.com/xgboost/sponsor/1/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/1/avatar.svg"></a>
<a href="https://opencollective.com/xgboost/sponsor/2/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/2/avatar.svg"></a>
<a href="https://opencollective.com/xgboost/sponsor/3/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/3/avatar.svg"></a>
<a href="https://opencollective.com/xgboost/sponsor/4/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/4/avatar.svg"></a>
<a href="https://opencollective.com/xgboost/sponsor/5/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/5/avatar.svg"></a>
<a href="https://opencollective.com/xgboost/sponsor/6/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/6/avatar.svg"></a>
<a href="https://opencollective.com/xgboost/sponsor/7/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/7/avatar.svg"></a>
<a href="https://opencollective.com/xgboost/sponsor/8/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/8/avatar.svg"></a>
<a href="https://opencollective.com/xgboost/sponsor/9/website" target="_blank"><img src="https://opencollective.com/xgboost/sponsor/9/avatar.svg"></a>
### Backers
[[Become a backer](https://opencollective.com/xgboost#backer)]
<a href="https://opencollective.com/xgboost#backers" target="_blank"><img src="https://opencollective.com/xgboost/backers.svg?width=890"></a>
## Other sponsors
The sponsors in this list are donating cloud hours in lieu of cash donation.
<a href="https://aws.amazon.com/" target="_blank"><img src="https://raw.githubusercontent.com/xgboost-ai/xgboost-ai.github.io/master/images/sponsors/aws.png" alt="Amazon Web Services" width="72" height="72"></a>
Error in .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = urlSuffix, : Unexpected CURL error: getaddrinfo() thread failed to start
Unexpected CURL error: Failed to connect to 127.0.0.1 port 54321: Connection reset by peer
$ brew edit curl # add --disable-socketpair to args list
$ brew install --build-from-source curl # using reinstall might be needed instead of install
$ export RCURL_PATH="usr/local/opt/curl@7.81.0" # can be found using `brew info curl`
$ export PATH="$RCURL_PATH/bin:$PATH" # for curl-config
$ export LDFLAGS="-L$RCURL_PATH//lib"
$ export CPPFLAGS="-I$RCURL_PATH/include"
$ export PKG_CONFIG_PATH="$RCURL_PATH/lib/pkgconfig"
$ R -e "chooseCRANmirror(graphics=FALSE, ind=1);install.packages('RCurl', type = 'source')"
$ R -e "RCurl::curlVersion()$version" # check if RCurl is using the proper version of curl
$ sudo apt install devscripts
$ # make sure source repositories are enabled (uncommented in /etc/apt/s
$ apt-get source curl
$ sudo apt-get build-dep curl
$ cd curl
$ nano debian/rules # add the --disable-socketpair configure option
$ dch -i # bump the version
$ debuild -us -uc -b # build the package
$ dpkg -i ../curl-some_version.dpkg
$ export PATH="$RCURL_PATH/bin:$PATH" # for curl-config
$ export LDFLAGS="-L$RCURL_PATH//lib"
$ export CPPFLAGS="-I$RCURL_PATH/include"
$ export PKG_CONFIG_PATH="$RCURL_PATH/lib/pkgconfig"
$ R -e "chooseCRANmirror(graphics=FALSE, ind=1);install.packages('RCurl', type = 'source')"
$ R -e "RCurl::curlVersion()$version" # check if RCurl is using the proper version of curl
-----------------------
Unexpected CURL error: Failed to connect to 127.0.0.1 port 54321: Connection reset by peer
$ brew edit curl # add --disable-socketpair to args list
$ brew install --build-from-source curl # using reinstall might be needed instead of install
$ export RCURL_PATH="usr/local/opt/curl@7.81.0" # can be found using `brew info curl`
$ export PATH="$RCURL_PATH/bin:$PATH" # for curl-config
$ export LDFLAGS="-L$RCURL_PATH//lib"
$ export CPPFLAGS="-I$RCURL_PATH/include"
$ export PKG_CONFIG_PATH="$RCURL_PATH/lib/pkgconfig"
$ R -e "chooseCRANmirror(graphics=FALSE, ind=1);install.packages('RCurl', type = 'source')"
$ R -e "RCurl::curlVersion()$version" # check if RCurl is using the proper version of curl
$ sudo apt install devscripts
$ # make sure source repositories are enabled (uncommented in /etc/apt/s
$ apt-get source curl
$ sudo apt-get build-dep curl
$ cd curl
$ nano debian/rules # add the --disable-socketpair configure option
$ dch -i # bump the version
$ debuild -us -uc -b # build the package
$ dpkg -i ../curl-some_version.dpkg
$ export PATH="$RCURL_PATH/bin:$PATH" # for curl-config
$ export LDFLAGS="-L$RCURL_PATH//lib"
$ export CPPFLAGS="-I$RCURL_PATH/include"
$ export PKG_CONFIG_PATH="$RCURL_PATH/lib/pkgconfig"
$ R -e "chooseCRANmirror(graphics=FALSE, ind=1);install.packages('RCurl', type = 'source')"
$ R -e "RCurl::curlVersion()$version" # check if RCurl is using the proper version of curl
-----------------------
Unexpected CURL error: Failed to connect to 127.0.0.1 port 54321: Connection reset by peer
$ brew edit curl # add --disable-socketpair to args list
$ brew install --build-from-source curl # using reinstall might be needed instead of install
$ export RCURL_PATH="usr/local/opt/curl@7.81.0" # can be found using `brew info curl`
$ export PATH="$RCURL_PATH/bin:$PATH" # for curl-config
$ export LDFLAGS="-L$RCURL_PATH//lib"
$ export CPPFLAGS="-I$RCURL_PATH/include"
$ export PKG_CONFIG_PATH="$RCURL_PATH/lib/pkgconfig"
$ R -e "chooseCRANmirror(graphics=FALSE, ind=1);install.packages('RCurl', type = 'source')"
$ R -e "RCurl::curlVersion()$version" # check if RCurl is using the proper version of curl
$ sudo apt install devscripts
$ # make sure source repositories are enabled (uncommented in /etc/apt/s
$ apt-get source curl
$ sudo apt-get build-dep curl
$ cd curl
$ nano debian/rules # add the --disable-socketpair configure option
$ dch -i # bump the version
$ debuild -us -uc -b # build the package
$ dpkg -i ../curl-some_version.dpkg
$ export PATH="$RCURL_PATH/bin:$PATH" # for curl-config
$ export LDFLAGS="-L$RCURL_PATH//lib"
$ export CPPFLAGS="-I$RCURL_PATH/include"
$ export PKG_CONFIG_PATH="$RCURL_PATH/lib/pkgconfig"
$ R -e "chooseCRANmirror(graphics=FALSE, ind=1);install.packages('RCurl', type = 'source')"
$ R -e "RCurl::curlVersion()$version" # check if RCurl is using the proper version of curl
how to properly initialize a child class of XGBRegressor?
class XGBoostQuantileRegressor(XGBRegressor):
def __init__(self, quant_alpha, max_depth=3, **kwargs):
self.quant_alpha = quant_alpha
super().__init__(max_depth=max_depth, **kwargs)
# other methods unchanged and omitted for brevity.
Jupyter shell commands in a function
def foo(astr):
!ls $astr
foo('*.py')
!ls *.py
-----------------------
def foo(astr):
!ls $astr
foo('*.py')
!ls *.py
-----------------------
from IPython import get_ipython
ipython = get_ipython()
code = ipython.transform_cell('!ls')
print(code)
exec(code)
exec(ipython.transform_cell('!ls'))
-----------------------
from IPython import get_ipython
ipython = get_ipython()
code = ipython.transform_cell('!ls')
print(code)
exec(code)
exec(ipython.transform_cell('!ls'))
-----------------------
from IPython import get_ipython
ipython = get_ipython()
code = ipython.transform_cell('!ls')
print(code)
exec(code)
exec(ipython.transform_cell('!ls'))
dask_xgboost.predict works but cannot be shown -Data must be 1-dimensional
Dask-XGBoost has been deprecated and is no longer maintained.
The functionality of this project has been included directly
in XGBoost. To use Dask and XGBoost together, please use
xgboost.dask instead
https://xgboost.readthedocs.io/en/latest/tutorials/dask.html.
# note the .dask
model_xgb = xgb.dask.DaskXGBRegressor(seed=42, verbose=True)
grid_search = GridSearchCV(model_xgb, params, cv=3, scoring='neg_mean_squared_error')
grid_search.fit(x_train, y_train)
#train data with best params
model_xgb.client = client
model_xgb.set_params(grid_search.best_params_)
model_xgb.fit(X_train, y_train, eval_set=[(X_test, y_test)])
-----------------------
Dask-XGBoost has been deprecated and is no longer maintained.
The functionality of this project has been included directly
in XGBoost. To use Dask and XGBoost together, please use
xgboost.dask instead
https://xgboost.readthedocs.io/en/latest/tutorials/dask.html.
# note the .dask
model_xgb = xgb.dask.DaskXGBRegressor(seed=42, verbose=True)
grid_search = GridSearchCV(model_xgb, params, cv=3, scoring='neg_mean_squared_error')
grid_search.fit(x_train, y_train)
#train data with best params
model_xgb.client = client
model_xgb.set_params(grid_search.best_params_)
model_xgb.fit(X_train, y_train, eval_set=[(X_test, y_test)])
Tuning XGBoost Hyperparameters with RandomizedSearchCV
hyperparameter_grid = {
'n_estimators': [100, 500, 900, 1100, 1500],
'max_depth': [2, 3, 5, 10, 15],
'learning_rate': [0.05, 0.1, 0.15, 0.20],
'min_child_weight': [1, 2, 3, 4]
}
hyperparameter_grid = {
'n_estimators': [100, 400, 800],
'max_depth': [3, 6, 9],
'learning_rate': [0.05, 0.1, 0.20],
'min_child_weight': [1, 10, 100]
}
hyperparameter_grid = {
'max_depth': [3, 6, 9],
'min_child_weight': [1, 10, 100]
}
-----------------------
hyperparameter_grid = {
'n_estimators': [100, 500, 900, 1100, 1500],
'max_depth': [2, 3, 5, 10, 15],
'learning_rate': [0.05, 0.1, 0.15, 0.20],
'min_child_weight': [1, 2, 3, 4]
}
hyperparameter_grid = {
'n_estimators': [100, 400, 800],
'max_depth': [3, 6, 9],
'learning_rate': [0.05, 0.1, 0.20],
'min_child_weight': [1, 10, 100]
}
hyperparameter_grid = {
'max_depth': [3, 6, 9],
'min_child_weight': [1, 10, 100]
}
-----------------------
hyperparameter_grid = {
'n_estimators': [100, 500, 900, 1100, 1500],
'max_depth': [2, 3, 5, 10, 15],
'learning_rate': [0.05, 0.1, 0.15, 0.20],
'min_child_weight': [1, 2, 3, 4]
}
hyperparameter_grid = {
'n_estimators': [100, 400, 800],
'max_depth': [3, 6, 9],
'learning_rate': [0.05, 0.1, 0.20],
'min_child_weight': [1, 10, 100]
}
hyperparameter_grid = {
'max_depth': [3, 6, 9],
'min_child_weight': [1, 10, 100]
}
How to get hyperparameters of xgb.train in python
import json
config = json.loads(bst.save_config())
config['learner']['gradient_booster']['updater']['grow_colmaker']['train_param']
-----------------------
import json
config = json.loads(bst.save_config())
config['learner']['gradient_booster']['updater']['grow_colmaker']['train_param']
FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan
{'eta ':[0.01, 0.05, 0.1, 0.2]},...
{'eta':[0.01, 0.05, 0.1, 0.2]},...
-----------------------
{'eta ':[0.01, 0.05, 0.1, 0.2]},...
{'eta':[0.01, 0.05, 0.1, 0.2]},...
-----------------------
grid_lr = {
'cls__class_weight': [None, 'balanced'],
'cls__C': [0, .001, .01, .1, 1]
}
I fail to run caret's nnet regression
library(tidyverse)
library(caret)
feature = data.frame(x = rnorm(100, 0, 1) %>% as.double()) # changed line
outcome = rnorm(100, 0, 1) %>% as.double()
CATE_model = caret::train(
x = feature, y = outcome, method = "nnet",
tuneGrid = expand.grid(size=c(1:3), decay=seq(0.1, 1, 0.1)),
weights = NULL, linout = TRUE
)
Can you find the mistake in my custom evaluation metric? XGBOOST R
eval_metric <- c()
for (i in 1:100) {
trained_models<-xgb.train(data=training_vectors,gamma=0,nrounds=i,max_depth=2,objective="binary:logistic", verbose = 0,feval=my_metric,watchlist = watchlist)
eval_metric[i] <- my_metric(predict(trained_models,testing_vectors), testing_vectors)$value
}
eval_metric
[1] 1.0833762 1.0332087 1.0702217 1.1165583 0.9980249 1.0447095 0.9964721 0.9674231 0.8648293
[10] 0.9044608 0.8724537 0.9304222 0.8491665 0.8829176 0.9304336 0.9221882 0.8533177 0.8376518
[19] 0.7965470 0.8284276 0.8067912 0.7947449 0.7577542 0.7864774 0.7560513 0.7355429 0.7609600
[28] 0.7640666 0.7101464 0.7291165 0.7655773 0.7347603 0.6886943 0.7110074 0.6942958 0.6838692
[37] 0.2975801 0.3121724 0.6874055 0.3178953 0.3018035 0.3133702 0.6857661 0.6927544 0.3043382
[46] 0.2982567 0.2908952 0.2772635 0.2722214 0.2677541 0.2610758 0.2715461 0.2818424 0.3041806
[55] 0.3227641 0.3138340 0.3105319 0.3045225 0.3009517 0.3114915 0.3061301 0.3169128 0.3118879
[64] 0.3083425 0.3185155 0.3115889 0.3202170 0.3141242 0.3115893 0.3265834 0.3178155 0.3211948
[73] 0.3145838 0.3232811 0.3168709 0.3215020 0.3140709 0.3214312 0.3146561 0.3219147 0.3156422
[82] 0.3099746 0.3176437 0.3261342 0.3212111 0.3146619 0.3215416 0.3296011 0.3362954 0.3328568
[91] 0.3266897 0.3216920 0.3297096 0.3246411 0.3192709 0.3235182 0.6988236 0.3270507 0.7030137
[100] 0.3299713
-----------------------
eval_metric <- c()
for (i in 1:100) {
trained_models<-xgb.train(data=training_vectors,gamma=0,nrounds=i,max_depth=2,objective="binary:logistic", verbose = 0,feval=my_metric,watchlist = watchlist)
eval_metric[i] <- my_metric(predict(trained_models,testing_vectors), testing_vectors)$value
}
eval_metric
[1] 1.0833762 1.0332087 1.0702217 1.1165583 0.9980249 1.0447095 0.9964721 0.9674231 0.8648293
[10] 0.9044608 0.8724537 0.9304222 0.8491665 0.8829176 0.9304336 0.9221882 0.8533177 0.8376518
[19] 0.7965470 0.8284276 0.8067912 0.7947449 0.7577542 0.7864774 0.7560513 0.7355429 0.7609600
[28] 0.7640666 0.7101464 0.7291165 0.7655773 0.7347603 0.6886943 0.7110074 0.6942958 0.6838692
[37] 0.2975801 0.3121724 0.6874055 0.3178953 0.3018035 0.3133702 0.6857661 0.6927544 0.3043382
[46] 0.2982567 0.2908952 0.2772635 0.2722214 0.2677541 0.2610758 0.2715461 0.2818424 0.3041806
[55] 0.3227641 0.3138340 0.3105319 0.3045225 0.3009517 0.3114915 0.3061301 0.3169128 0.3118879
[64] 0.3083425 0.3185155 0.3115889 0.3202170 0.3141242 0.3115893 0.3265834 0.3178155 0.3211948
[73] 0.3145838 0.3232811 0.3168709 0.3215020 0.3140709 0.3214312 0.3146561 0.3219147 0.3156422
[82] 0.3099746 0.3176437 0.3261342 0.3212111 0.3146619 0.3215416 0.3296011 0.3362954 0.3328568
[91] 0.3266897 0.3216920 0.3297096 0.3246411 0.3192709 0.3235182 0.6988236 0.3270507 0.7030137
[100] 0.3299713
GridSearchCV not choosing the best hyperparameters for xgboost
from sklearn.model_selection import KFold
seed_cv = 123 # any random value here
kf = KFold(n_splits=5, random_state=seed_cv)
grid_xgb_reg=GridSearchCV(xgb_reg,
param_grid=params,
scoring=scorer,
cv=kf, # <- change here
n_jobs=-1)
seed_xgb = 456 # any random value here (can even be the same with seed_cv)
xgb_reg = xgb.XGBRegressor(random_state=seed_xgb)
-----------------------
from sklearn.model_selection import KFold
seed_cv = 123 # any random value here
kf = KFold(n_splits=5, random_state=seed_cv)
grid_xgb_reg=GridSearchCV(xgb_reg,
param_grid=params,
scoring=scorer,
cv=kf, # <- change here
n_jobs=-1)
seed_xgb = 456 # any random value here (can even be the same with seed_cv)
xgb_reg = xgb.XGBRegressor(random_state=seed_xgb)
QUESTION
Error in .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = urlSuffix, : Unexpected CURL error: getaddrinfo() thread failed to start
Asked 2022-Jan-27 at 19:14I am experiencing a persistent error while trying to use H2O's h2o.automl
function. I am trying to repeatedly run this model. It seems to completely fail after 5 or 10 runs.
Error in .h2o.__checkConnectionHealth() :
H2O connection has been severed. Cannot connect to instance at http://localhost:54321/
getaddrinfo() thread failed to start
In addition: There were 13 warnings (use warnings() to see them)
Error in .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = urlSuffix, :
Unexpected CURL error: getaddrinfo() thread failed to start
I have updated java in response to: https://h2o-release.s3.amazonaws.com/h2o/rel-wolpert/4/docs-website/h2o-docs/faq/r.html (even though I am using a linux virtual machine).
I have added a h2o.removeall()
and gc()
in response to R h2o server CURL error, kind of repeatable
I have not attempted any changes regarding memory because my cluster has 16+ GB and the highest reading I have seen is 1.6 GiB in RStudio.
H2O is running in R/Rstudio Server on an Ubuntu 20.04 virtual machine. Could the virtual box software be blocking something?
The details on my H2O cluster are below:
openjdk version "11.0.11" 2021-04-20
OpenJDK Runtime Environment (build 11.0.11+9-Ubuntu-0ubuntu2.20.04)
OpenJDK 64-Bit Server VM (build 11.0.11+9-Ubuntu-0ubuntu2.20.04, mixed mode, sharing)
Starting H2O JVM and connecting: ... Connection successful!
R is connected to the H2O cluster:
H2O cluster uptime: 1 seconds 896 milliseconds
H2O cluster timezone: America/Chicago
H2O data parsing timezone: UTC
H2O cluster version: 3.35.0.2
H2O cluster version age: 19 hours and 24 minutes
H2O cluster name: H2O_started_from_R_jholderieath_glq667
H2O cluster total nodes: 1
H2O cluster total memory: 19.84 GB
H2O cluster total cores: 12
H2O cluster allowed cores: 12
H2O cluster healthy: TRUE
H2O Connection ip: localhost
H2O Connection port: 54321
H2O Connection proxy: NA
H2O Internal Security: FALSE
H2O API Extensions: Amazon S3, XGBoost, Algos, AutoML, Core V3, TargetEncoder, Core V4
R Version: R version 4.1.1 (2021-08-10)
ANSWER
Answered 2022-Jan-27 at 19:14I think I also experienced this issue, although on macOS 12.1. I tried to debug it and found out that sometimes I also get another error:
Unexpected CURL error: Failed to connect to 127.0.0.1 port 54321: Connection reset by peer
I found out that this issue appears only when I have RCurl
compiled against curl
7.68.0 and above.
Downgrading to curl
7.67.0 resolved the issue for me but then I got some issues with RStudio (Segmentation Fault) so I looked into the issue little further.
And I found out that compiling a recent version of curl
with --disable-socketpair
solved it for me as well.
I was monitoring open files and sockets (lsof
) and it seems to me that R
process runs out of sockets it can create and RCurl
then fails with one of those errors. Running gc()
in R frequently helps (I called it after every single request) but still the minimum number of open sockets after gc()
is slowly but monotonically increasing which leads me to believe there might be some leak. I reported this as a possible bug to the RCurl maintainers.
For anybody using macOS and homebrew this can be accomplished by running the following:
$ brew edit curl # add --disable-socketpair to args list
$ brew install --build-from-source curl # using reinstall might be needed instead of install
$ export RCURL_PATH="usr/local/opt/curl@7.81.0" # can be found using `brew info curl`
$ export PATH="$RCURL_PATH/bin:$PATH" # for curl-config
$ export LDFLAGS="-L$RCURL_PATH//lib"
$ export CPPFLAGS="-I$RCURL_PATH/include"
$ export PKG_CONFIG_PATH="$RCURL_PATH/lib/pkgconfig"
$ R -e "chooseCRANmirror(graphics=FALSE, ind=1);install.packages('RCurl', type = 'source')"
$ R -e "RCurl::curlVersion()$version" # check if RCurl is using the proper version of curl
Looking at the curl
version in ubuntu 20.04 which is 7.68.0 (according to https://packages.ubuntu.com/focal/curl) I think you won't be able to use the following as the --disable-socketpair
was added in curl
7.73.0 but since you are using a virtual machine it might be easier to just use ubuntu 18.04 since it's still supported and is using old enough curl
version (7.58.0).
I haven't used ubuntu for a while but at least I can provide some pseudo-code that should do the same:
$ sudo apt install devscripts
$ # make sure source repositories are enabled (uncommented in /etc/apt/s
$ apt-get source curl
$ sudo apt-get build-dep curl
$ cd curl
$ nano debian/rules # add the --disable-socketpair configure option
$ dch -i # bump the version
$ debuild -us -uc -b # build the package
$ dpkg -i ../curl-some_version.dpkg
$ export PATH="$RCURL_PATH/bin:$PATH" # for curl-config
$ export LDFLAGS="-L$RCURL_PATH//lib"
$ export CPPFLAGS="-I$RCURL_PATH/include"
$ export PKG_CONFIG_PATH="$RCURL_PATH/lib/pkgconfig"
$ R -e "chooseCRANmirror(graphics=FALSE, ind=1);install.packages('RCurl', type = 'source')"
$ R -e "RCurl::curlVersion()$version" # check if RCurl is using the proper version of curl
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
No vulnerabilities reported
Save this library and start creating your kit
Save this library and start creating your kit