Saravanan666/pypi-org huggingface dataset:
Package: olivos
OlivOS青果核心交互栈Witness Union / 见证联合文档·论坛·下载·赞助兼容排名不分先后概述OlivOS 青果核心交互栈，一个将各类涉及异步文本流交互的场景（即时通讯、直播弹幕、网络聊天室、静态命令行应用程序）转换到统一框架，基于统一流量管理、负载均衡、业务处理机制进行服务，以期在这些交互逻辑与功能需求类似的场景获得更加灵活的部署方式、更加有效的开发模式以及更加合理的资源调度。方针技术路线上，基于Python、采用模块化框架、进程分离、消息队列通信、前后端分离、响应式布局、渐进式渲染的基本思路，实现一个高并发性、高可靠性、高适应性的整体方案。开始使用发布版本Windows我们推荐Windows用户直接使用经过二进制编译的发布版本，你可以在这里获取最新版本：releases源码版本请确保你已经安装了Python3以及git，当前我们主要使用3.7.5以及3.11.0两个版本的Python。首先，克隆本项目的源码gitclonehttps://github.com/OlivOS-Team/OlivOScdOlivOS安装依赖环境pipinstall-rrequirements310_pure.txt对于3.7.5版本的Python，有单独提供的依赖描述文件pipinstall-rrequirements_pure.txt启动环境python main.pyPypi版本OlivOS的核心组件部分已经被充分集成，并作为Pypi库进行了发布，你同样也可以使用pip来获取这个版本并将OlivOS作为库加以使用。要获取最新版本的OlivOS库，你需要pipinstallolivos以下是一个简单的OlivOS启动示例代码importOlivOSif__name__=='__main__':OlivOSTarget=OlivOS.bootAPI.Entity()OlivOSTarget.start()插件请参考插件默认模板OlivOSPluginTemplate和官方文档进行插件开发。许可证Copyright (C) 2019-2021 lunzhiPenxil and OlivOS Team and contributors.

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU Affero General Public License as
published by the Free Software Foundation, either version 3 of the
License, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU Affero General Public License for more details.

You should have received a copy of the GNU Affero General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.OlivOS采用AGPLv3协议开源。为了整个社区的良性发展，我们强烈建议您做到以下几点：间接接触（包括但不限于使用Http API或 跨进程技术）到OlivOS的软件使用AGPLv3开源OlivOS 的形象图及项目图标都拥有著作权保护。在未经过允许的情况下，任何人都不可以使用形象图和图标，或本文初的有关 OlivOS 名称来历的介绍原文，用于商业用途或是放置在项目首页，或其他未许可的行为。衍生软件需声明引用若引用 OlivOS 发布的软件包而不修改 OlivOS，则衍生项目需在描述的任意部位提及使用 OlivOS。若修改 OlivOS 源代码再发布，或参考 OlivOS 内部实现发布另一个项目，则衍生项目必须在文章首部或 'OlivOS' 相关内容首次出现的位置明确声明来源于本仓库 (https://github.com/OlivOS-Team/OlivOS)。不得扭曲或隐藏免费且开源的事实。
Package: olivos-nb2
OlivOS.nb2NoneBot2的OlivOS兼容层插件注意，本兼容层无法获得 API 的返回值！安装从 PyPI 安装（推荐）使用 nb-clinbplugininstallOlivOS.nb2使用 poetrypoetryaddOlivOS.nb2使用 pippipinstallOlivOS.nb2从 GitHub 安装（不推荐）使用 poetrypoetryaddgit+https://github.com/nonepkg/OlivOS.nb2.git使用 pippipinstallgit+https://github.com/nonepkg/OlivOS.nb2.git使用目前只有 OneBot V11（CQHTTP）平台的兼容层，其他平台待添加（Telegram 平台实验中）。OlivOS 插件请放入./data/OlivOS/app/。插件兼容名单这里的插件指 OlivOS 插件。未在此名单并不意味着无法加载，只是加载后可能出现未知问题。OlivOS-Team/OlivaDiceCoreOlivOS-Team/OlivaDiceJoyOlivOS-Team/OlivaDiceLoggerFishroud/OlivaBilibiliPlugin已证实不兼容lunzhiPenxil/OlivOSOnebotV11使用 ThreadOlivOS-Team/OlivaDiceMaster使用 Thread
Package: oll
oll-pythonThis is a Python binding of the OLL library for machine learning.Currently, OLL 0.03 supports following binary classification algorithms:PerceptronAveraged PerceptronPassive Agressive (PA, PA-I, PA-II)ALMA (modified slightly from original)Confidence Weighted Linear-Classification.For details of oll, see:http://code.google.com/p/ollInstallation$ pip install ollOLL library is bundled, so you don’t need to install it separately.Usageimportoll# You can choose algorithms in# "P" -> Perceptron,# "AP" -> Averaged Perceptron,# "PA" -> Passive Agressive,# "PA1" -> Passive Agressive-I,# "PA2" -> Passive Agressive-II,# "PAK" -> Kernelized Passive Agressive,# "CW" -> Confidence Weighted Linear-Classification,# "AL" -> ALMAo=oll.oll("CW",C=1.0,bias=0.0)o.add({0:1.0,1:2.0,2:-1.0},1)# traino.classify({0:1.0,1:1.0})# predicto.save('oll.model')o.load('oll.model')# scikit-learn like fit/predict interfaceimportnumpyasnparray=np.array([[1,2,-1],[0,0,1]])o.fit(array,[1,-1])o.predict(np.array([[1,2,-1],[0,0,1]]))# => [1, -1]fromscipy.sparseimportcsr_matrixmatrix=csr_matrix([[1,2,-1],[0,0,1]])o.fit(matrix,[1,-1])o.predict(matrix)# => [1, -1]# Multi label classificationimporttimeimportollfromsklearn.multiclassimportOutputCodeClassifierfromsklearnimportdatasets,cross_validation,metricsdataset=datasets.load_digits()ALGORITHMS=("P","AP","PA","PA1","PA2","PAK","CW","AL")foralgorithminALGORITHMS:print(algorithm)occ_predicts=[]expected=[]start=time.time()for(train_idx,test_idx)incross_validation.StratifiedKFold(dataset.target,n_folds=10,shuffle=True):clf=OutputCodeClassifier(oll.oll(algorithm))clf.fit(dataset.data[train_idx],dataset.target[train_idx])occ_predicts+=list(clf.predict(dataset.data[test_idx]))expected+=list(dataset.target[test_idx])print('Elapsed time:%s'%(time.time()-start))print('Accuracy',metrics.accuracy_score(expected,occ_predicts))# => P# => Elapsed time: 109.82188701629639# => Accuracy 0.770172509738# => AP# => Elapsed time: 111.42936396598816# => Accuracy 0.760155815248# => PA# => Elapsed time: 110.95964503288269# => Accuracy 0.74735670562# => PA1# => Elapsed time: 111.39844799041748# => Accuracy 0.806343906511# => PA2# => Elapsed time: 115.12716913223267# => Accuracy 0.766277128548# => PAK# => Elapsed time: 119.53838682174683# => Accuracy 0.77796327212# => CW# => Elapsed time: 121.20785689353943# => Accuracy 0.771285475793# => AL# => Elapsed time: 116.52497220039368# => Accuracy 0.785754034502NoteThis module requires C++ compiler to build.oll.cpp & oll.hpp : Copyright (c) 2011, Daisuke Okanoharaoll_swig_wrap.cxx is generated based on ‘oll_swig.i’ in oll-ruby (https://github.com/syou6162/oll-ruby)LicenseNew BSD License.CHANGES0.2.1 (2017-6-30)Multi label clasification (using scikit-learn)Support Python 3.60.2 (2016-11-26)scikit-learn like fit/predict interfaces are availableSetting C and bias parameters is available in initializationSupport Python 3.5Unsupport Python 2.6 and 3.30.1.2 (2015-01-11)Support testFile methoddocstrings are available0.1.1 (2014-03-29)Compatibility some compilers0.1 (2013-10-11)Initial release.
Package: ollachat
Ollachat 🚀NOTE: package name has been chagned fromst_ollamatoollachatin v1.5TL;DRA minimal Streamlit Chatbot GUI for Ollama modelsA Streamlit chatbot app that integrates with the Ollama LLMs. Includes chat history; and each model has its own chat log.Getting startedInstall the pacakge (in virtualenv recommended)pipinstallollachatStart the appollachatDemoshttps://github.com/iamaziz/st_ollama/assets/3298308/7e0d4cc2-ded3-4c24-aa29-bf3927aa0ec9show model details (release1.0.4)https://github.com/iamaziz/st_ollama/assets/3298308/4471ffe5-8a10-4c86-bb01-e8470ff93d28NotesOllamamust be installed.Supports macOS and Linux.
Package: ollala
OllalaA simple, friendly Ollama HTTP client.GoalsFocus on implementing Ollama APIs as-is without additional abstractionsProvide additional, optional abtractions to make it simpler to useNextTest / Fix remaining enpointsError handling + more testsImplement an abstraction for a stateful conversationFutureConsider async APIs with httpxConsider adding streaming support for responsesChoresSetup Github Actions for linting, testing, publishing to PyPIGraphics, icons and aesthetics
Package: ollama
Ollama Python LibraryThe Ollama Python library provides the easiest way to integrate Python 3.8+ projects withOllama.InstallpipinstallollamaUsageimportollamaresponse=ollama.chat(model='llama2',messages=[{'role':'user','content':'Why is the sky blue?',},])print(response['message']['content'])Streaming responsesResponse streaming can be enabled by settingstream=True, modifying function calls to return a Python generator where each part is an object in the stream.importollamastream=ollama.chat(model='llama2',messages=[{'role':'user','content':'Why is the sky blue?'}],stream=True,)forchunkinstream:print(chunk['message']['content'],end='',flush=True)APIThe Ollama Python library's API is designed around theOllama REST APIChatollama.chat(model='llama2',messages=[{'role':'user','content':'Why is the sky blue?'}])Generateollama.generate(model='llama2',prompt='Why is the sky blue?')Listollama.list()Showollama.show('llama2')Createmodelfile='''FROM llama2SYSTEM You are mario from super mario bros.'''ollama.create(model='example',modelfile=modelfile)Copyollama.copy('llama2','user/llama2')Deleteollama.delete('llama2')Pullollama.pull('llama2')Pushollama.push('user/llama2')Embeddingsollama.embeddings(model='llama2',prompt='They sky is blue because of rayleigh scattering')Custom clientA custom client can be created with the following fields:host: The Ollama host to connect totimeout: The timeout for requestsfromollamaimportClientclient=Client(host='http://localhost:11434')response=client.chat(model='llama2',messages=[{'role':'user','content':'Why is the sky blue?',},])Async clientimportasynciofromollamaimportAsyncClientasyncdefchat():message={'role':'user','content':'Why is the sky blue?'}response=awaitAsyncClient().chat(model='llama2',messages=[message])asyncio.run(chat())Settingstream=Truemodifies functions to return a Python asynchronous generator:importasynciofromollamaimportAsyncClientasyncdefchat():message={'role':'user','content':'Why is the sky blue?'}asyncforpartinawaitAsyncClient().chat(model='llama2',messages=[message],stream=True):print(part['message']['content'],end='',flush=True)asyncio.run(chat())ErrorsErrors are raised if requests return an error status or if an error is detected while streaming.model='does-not-yet-exist'try:ollama.chat(model)exceptollama.ResponseErrorase:print('Error:',e.error)ife.status_code==404:ollama.pull(model)
Package: ollamagent
OllamAgentOllamAgentis a wrapper aroundn Ollama API. Ollama is a mini framework for consuming, managing and maintaining SOTA (State of the Art) Large Language Models that have been Quantaized and optimized for deployment on edge devices, there is no need for 'OpenAI' or 'HuggingFace' API keys, or even 'API_KEY' for that matter, you can run these models within your own devices ensuring data privacy, security and most importantly accesibility to this cutting edge technology for people with limited resources.
It implements an Agent | Tools mini-framework that simplifies the ussage of the full set of features of the supported models (Currently only Mistral7B is supported).InstallationpipinstallollamagentUsagefromollamagentimportAPIClient,Agent,Tool# Implementing an integration with a third party APIclassMyAPI(APIClient):base_url="https://api.myapi.com"headers={"Authorization":"Bearer MY_API_KEY"}asyncdefmy_api_method(self,data):returnawaitself.post("/my-api-method",data)# Implementing a toolclassMyTool(Tool):data:dict@propertydefapi(self):returnMyAPI()asyncdefrun(self):returnawaitself.api.my_api_method(self.data)# Instantiating the agentagent=Agent(tools=[MyTool])# Interacting with the agentasyncdefmain():print(awaitagent.chat(message="Hello! Are you there?"))>>>"Hello! I'm here, how can I help you?"print(awaitagent.run_tool("MyTool",data={"key":"value"}))>>>{"response":"from","my":"api"}asyncio.run(main())
Package: ollama-haystack
ollama-haystackTable of ContentsInstallationLicenseInstallationpip install ollama-haystackLicenseollama-haystackis distributed under the terms of theApache-2.0license.TestingTo run tests first start a Docker container running Ollama and pull a model for integration testing
It's recommended to use the smallest model possible for testing purposes - seehttps://ollama.ai/libraryfor a list that Ollama supportddocker run -d -p 11434:11434 --name ollama ollama/ollama:latestdocker exec ollama ollama pull <your model here>Then run tests:hatch run testThe default model used here isorca-mini
Package: ollehtv
Python library for controlling an Olleh TV set-top-boxes.Installation$ pip install ollehtvIf you also wish to run the development tests you will need to do$ pip install -r requirements-dev.txtConfigurationIn order to control your STB you must also authenticate a remote using
the Olleh playtv mobile app. Once you have authenticated the app, you
will need toproxyan app API request to get
theDEVICE_IDandSVC_PWvalues for your device.mitmproxy screenshotExample>>> import ollehtv
>>> device_id = "xxxxxxxx-xxxx-xxxx-xxxx-xxxxxxxxxxxx"
>>> svc_pw = "abcdef1234567890"
>>> otv = ollehtv.OllehTV(device_id, svc_pw)
>>> otv.turn_on()
>>> otv.unmute()
>>> otv.change_channel(5)
>>> otv.input_button(ollehtv.OllehTVButton.IJEON)This example session turns on a STB, unmutes it, switches it to channel
5 and then switches it back to the previous channel.
Package: olles-little-data-and-model-pack
Package to Test Time Series ModelsIn the end will provide quick access to standard datasets and basic time series models for benchmarking.Example code is found at the bottom of the readme file.DatasetsAll datasets are in list and pandas format.Univariate Seasonal DataMonthly Flight Passenger DemandClassic dataset with flight passangers over time from the R data package.flight_list = the data in a list formatflight_pandas = the data in pandas dataframe formatUnivariate Non-Linear DatasetDaily Gold PricesLink:Kaggle Gold datasetgold_list = the data in a list formatgold_pandas = the data in a pandas df formatModels (WIP)Classic Time Series to benchmark more ambitious models.Naive (give in the lag)ARIMAHolt WinterExample Codeimport pandas as pdflight_listflight_pandas.head()gold_listgold_pandas.head()
Package: olliepy
OlliePy - An alternative approach for data scienceOlliePyis a python package which can help data scientists in
exploring their data and evaluating and analysing their machine learning experiments by
utilising the power and structure of modern web applications.
The data scientist only needs to provide the data and any required
information and OlliePy will generate the rest.DocumentationGet started by following theguideInstallationpip install -U olliepy
Package: oll-tuf
DisclaimerThis is a fork ofPython-TUF, maintained by the Open Law Library and
utilized byThe Archive Framework (TAF). The fork was created to make TUF's updater more
flexible, as TAF requires reading content of metadata and target files from bare Git repositories. Although these updates were
never merged upstream, this use case was taken into consideration during the rework of Python TUF's updater, and we encourage anyone
in need of the same flexibility to use the latest version of Python TUF. This fork will reach its end-of-life as soon as TAF's
codebase is updated, seeing that it currently relies on code that was removed from the newer versions of Python TUF.For more information, please contactinfo@openlawlib.org
Package: oLog
oLogoLog is a log module for Python3
To use oLog, you must call the class of the log type of your choice.Types of logs availablesign_color_log - log with colored sign ([!] [x] [?] [*])sign_log - log with sign ([!] [x] [?] [*]) not coloredcolor_log - log without colored signs. The signs will be replaced by: [ERROR], [WARNING]simple_log - log without colorless signs. The signs will be replaced by: [ERROR], [WARNING]UseImport oLog:importologYou must therefore create a journal object:# For the newspaper with sign:log=olog.sign_log('Server')# Replace server with the name of your application# For the newspaper without colored sign:log=olog.color_log('Server')# Replace server with the name of your application# For the newspaper without signs:log=olog.simple_log('Server')# Replace server with the name of your applicationThen, to view a log, you need to do this:log.error('An error has occurred')You can replace "error" with:
info, valid and warningIn developmentWe are in the process of developing a log to file system.
Package: olografos
ΟλογράφωςΜετατροπή αριθμών (συμπεριλαμβανομέων και δεκαδικών) σε μορφή ολογράφως, με δυνατότητα επιλογής γένους και κλίσης. Παρέχεται και η δυνατότητα για μετατροπή ποσών ευρώ σε μορφή ολογράφως.Converts Numbers (including decimal points) into words. It also converts the numbers into words for currency.ΕγκατάστασηpipinstallolografosΧρήσηΕισαγωγήfromolografosimportolografosprint(olografos(123456789))# Output: εκατόν εικοσιτρία εκατομμύρια τετρακόσιες πενηνταέξι χιλιάδες επτακόσια ογδονταεννέαΕπιλογέςlexis=olografos(123,{"currency":False"klisi":'onomastiki'"genos":'oudetero'})letlexis=olografos(123)//lexis=εκατόνείκοσιτρίαlexis=olografos(123.45)//lexis=εκατόνείκοσιτρίακαισαράνταπέντεεκατοστάΓια μετατροπή σε ευρώmoney=olografos(452,{"currency":True})//money=τετρακόσιαπενήνταδύοευρώmoney=olografos(452.36,{"currency":True})//money=τετρακόσιαπενήνταδύοευρώκαιτριάνταέξιλεπτάΑλλαγή γένου και κλίσηςlexis=olografos(452,{"klisi":"aitiatiki","genos":"thyliko"})//lexis=τετρακοσίεςπενήνταδύοΕκατοστάlexis=olografos(1.57,{"klisi":"geniki"});//lexis=ενόςκαιπενηνταεπτάεκατοστώνOptionsΕπιλογήTypeDefaultDescr,{options}iptioncurrencybooleanFalseΜετατροπή του αριθμόυ σε μορφή ολογράφως ευρώklisi'onomastiki''geniki''aitiatiki''onomastiki'Η κλίση στην οποία θα γραφεί ο αριθμόςgenos'arseniko''thyliko''oudetero''oudetero'Το γένος στο οποίο θα γραφεί ο αριθμός
Package: olop-ShadowLugia650
OLOP: One-Line & Obfuscated PythonThis repository contains useful python modules for one-line and obfuscated python.pip install olop-ShadowLugia650Wiki:FAQandOLP Tips and TricksOne-Line python (olp)These files are located withinsrc/olpolp.py: primitives within olp to be imported for olp scripts__import__("olp").olp.primitivesincludes: one-line while loop, one-line do while loop, inheritanceoldec.py: a one-line python decoder file which can be called from a python script. Common Usage:globals().update(oldec=__import__("olp").oldec)oroldec.beautify(oldec.parse_recursive("SOME_OLP_STRING"))or, for non olp programmersfromolpimportoldeccode=oldec.beautify(oldec.parse_recursive("SOME_OLP_STRING"))NOTE: oldec.py cannot parse all arbitrary one-line python files, as they can take numerous different forms (such asandOLP vsorOLP), and it looks for specific aspects within some one-line python programs (it doesn't work on itself either)oldec_cmd.py: a one-line python decoding command line utility, for decoding olp outside of a python script. Common Usage:python oldec_cmd.py <olp_file.py> -r -bpython oldec_cmd.pycan also be used to see usage information.Obfuscated PythonPrototyping this is still a work in progress.
Package: oloren
Oloren Orchestrator - Python Extension LibraryInstallationpipinstallolorenA requirement is that you use Python version > 3.7 as the code uses dataclasses which were introduced in that version.If for some reason you want to use it with Python 3.6 you can install the backport of dataclasses for Python 3.6 with:pipinstalldataclassesGetting StartedCheck out this minimal example to get started:importolorenasoloimportpandasaspd@olo.register(description="Basic math operations on two numbers.")defoperation(operation=olo.Choice(["Add","Subtract","Multiply","Divide"]),a=olo.Num(),b=olo.Num()):ifoperation=="Add":returna+belifoperation=="Subtract":returna-belifoperation=="Multiply":returna*belifoperation=="Divide":returna/b@olo.register(description="Convert CSV file to JSON")defdataframe_to_json(csv_file=olo.File()):returnpd.read_csv(csv_file).to_json()@olo.register(num_outputs=2)deftwooutputs(s=olo.String(),num=olo.Num()):returns,numif__name__=="__main__":olo.run("starterextension")The key requirements are that each argument of your function has a default value that is set to one of the special
Oloren types. These types subclass their relevant returned data types (e.g. string, int, float) so your autocomplete
will work as normal.Documentationhttps://oloren-ai.github.io/python-extension-lib/index.html
Package: olp
Object-level permissions (OLP)Django has supported object-level permissions since Django 1.2, but it
requires a custom authentication backend to implement it.  There aretons of implementationsout there, but most of them are meant to handle specific situations.FeaturesLike most other implementations, OLP is meant to handle a few specific cases
which we encountered.Different types of “groups”Django has a concept of “users” and “groups” which the authentication system
is built upon.  These groups only consist of a single model and cannot be
built upon to have different, distinct types.  OLP allows you to have multiple
models contribute to an individual user’s permissions, even on the object
level.The different models can have independent permissions, and do not need to be
directly attached to a user.  If they are attached to a user (and specified
in the settings), they can contribute to a user’s overall permissions.Direct integrationOLP is directly integrated with Django’s authentication backends.  While it
is not required to use OLP, it is recommended as it makes it easier to use
OLP.Normalization of methodsOnly the user and groups models havehas_permfunctions, and that becomes a
quick limitation when working with other models.  OLP patches the other
models (as specified in the settings) and gives themhas_permmethods
which act the same way as the ones given to users and groups.In order to make assigning and removing permissions as easy as checking them,
OLP will also patch all of the models withassign_permandremove_permmethods.SettingsOLP determines the settings from the Django settings file using the keyOLP_SETTINGS.  This must be a dictionary which contains the keymodels,
as shown below.OLP_SETTINGS={"models":(),}Themodelskey should contain a tuple of tuples containing the string path
to the model and the queryset filter used to filter by a user.Exampleexample/settings.pyOLP_SETTINGS={"models":(("django.contrib.auth.models.Group","users"),),}example/models.pyfromdjango.dbimportmodelsclassApple(models.Model):owner=models.ForeignKey("auth.User")classMeta:permissions=(("can_see_apple","User can see the apple."),)python shell>>>fromolp.utilsimportpatch_models>>>patch_models()>>>fromexample.modelsimportApple>>>fromdjango.contrib.authimportGroup,User>>>user=User.objects.all()[0]>>>group=Group.objects.all()[0]>>>apple=Apple(owner=user)>>>apple.save()>>>user.has_perm("example.can_see_apple",apple)False>>>user.assign_perm("can_see_apple",apple)True>>>user.has_perm("example.can_see_apple",apple)True>>>user.remove_perm("example.can_see_apple",apple)True>>>user.has_perm("example.can_see_apple",apple)False>>>group.has_perm("example.can_see_apple",apple)False>>>group.assign_perm("example.can_see_apple",apple)True>>>group.has_perm("example.can_see_apple",apple)True>>>user.has_perm("example.can_see_apple",apple)True
Package: olr
The olr function runs all the possible combinations of linear regressions with all of the
dependent variables against the independent variable and returns the statistical summary
of either the greatest adjusted R-squared or R-squared term. R-squared increases
with the addition of an explanatory variable whether it is 'significant' or not, thus this was developed to eliminate that conundrum.
Adjusted R-squared is preferred to overcome this phenomenon, but each combination will still produce different results and this will
return the best one.dataset = pd.read_csv('C:\Rstuff\olr\inst\extdata\oildata.csv')responseName = dataset[['OilPrices']]predictorNames = dataset[['SP500', 'RigCount', 'API', 'Field_Production', 'RefinerNetInput', 'OperableCapacity', 'Imports', 'StocksExcludingSPR']]The TRUE or FALSE in the olr function, specifies either the adjusted R-squared or the R-squared regression summary, respectfully.When responseName and predictorNames are None (NULL), then the first column in the dataset is set as the responseName and the remaining columns are the predictorNames.Adjusted R-squaredolr(datasetname, resvarname = None, expvarnames = None, adjr2 = "True")R-squaredolr(datasetname, resvarname = None, expvarnames = None, adjr2 = "False")list of summariesolrmodels(datasetname, resvarname = None, expvarnames = None)list of formulasolrformulas(datasetname, resvarname = None, expvarnames = None)list of forumlas with the dependant variables in ascending orderolrformulasorder(datasetname, resvarname = None, expvarnames = None)the list of adjusted R-squared termsadjr2list(datasetname, resvarname = None, expvarnames = None)the list of R-squared termsr2list(datasetname, resvarname = None, expvarnames = None)An R version of this package olr is available on CRAN.