Other packages > Find by keyword >

LearningStats  

Elemental Descriptive and Inferential Statistics
View on CRAN: Click here


Download and install LearningStats package within the R console
Install from CRAN:
install.packages("LearningStats")

Install from Github:
library("remotes")
install_github("cran/LearningStats")

Install by package version:
library("remotes")
install_version("LearningStats", "0.1.0")



Attach the package and use:
library("LearningStats")
Maintained by
María Isabel Borrajo-García
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2021-04-21
Latest Update: 2021-04-21
Description:
Provides tools to teach students elemental statistics. The main topics covered are descriptive statistics, probability models (discrete and continuous variables) and statistical inference (confidence intervals and hypothesis tests). One of the main advantages of this package is that allows the user to read quite a variety of types of data files with one unique command. Moreover it includes shortcuts to simple but up-to-now not in R descriptive features such a complete frequency table or an histogram with the optimal number of intervals. Related to model distributions (both discrete and continuous), the package allows the student to easy plot the mass/density function, distribution function and quantile function just detailing as input arguments the known population parameters. The inference related tools are basically confidence interval and hypothesis testing. Having defined independent commands for these two tools makes it easier for the student to understand what the software is performing, and it also helps the student to have a better knowledge on which specific tool they need to use in each situation. Moreover, the hypothesis testing commands provide not only the numeric result on the screen but also a very intuitive graph (which includes the statistic distribution, the observed value of the statistic, the rejection area and the p-value) that is very useful for the student to visualise the process. The regression section includes up to now, a simple linear model, with one single command the student can obtain the numeric summary as well as the corresponding diagram with the adjusted regression model and a legend with basic information (formula of the adjusted model and R-squared).
How to cite:
María Isabel Borrajo-García (2021). LearningStats: Elemental Descriptive and Inferential Statistics. R package version 0.1.0, https://cran.r-project.org/web/packages/LearningStats. Accessed 15 Sep. 2026.
Previous versions and publish date:
No previous versions
Other packages that cited LearningStats R package
View LearningStats citation profile
Other R packages that LearningStats depends, imports, suggests or enhances
Complete documentation for LearningStats
Functions, R codes and Examples using the LearningStats R package
Some associated functions: AproxBinomNorm . AproxBinomPois . AproxPoisNorm . BoxPlot . Histogram . LearningStats-package . Mean.CI . Mean.test . S2mu . Smu . diffmean.CI . diffmean.test . diffproportion.CI . diffproportion.test . diffvariance.CI . diffvariance.test . freq.pol . freq.table . indepchisq.test . plotBeta . plotBinom . plotChi . plotDUnif . plotExp . plotFS . plotGamma . plotHyper . plotNegBinom . plotNorm . plotPois . plotReg . plotTS . plotUnif . proportion.CI . proportion.test . read.data . sample.quantile . sample.sd . sample.var . sicri2018 . variance.CI . variance.test . 
Some associated R codes: AproxBinomNorm.R . AproxBinomPois.R . AproxPoisNorm.R . FSnedecor.R . Histogram.R . LearningStats-package.R . Mean.CI.R . Mean.sc.test.R . Mean.sigma.test.R . Mean.test.R . beta.R . binomial.R . boxplot.R . chi.R . diffmean.CI.R . diffmean.equal.test.R . diffmean.neq.test.R . diffmean.paired.test.R . diffmean.sigma.test.R . diffmean.test.R . diffproportion.CI.R . diffproportion.test.R . diffvariance.CI.R . diffvariance.mu.test.R . diffvariance.test.R . diffvariance.unknown.test.R . dunif.R . exponential.R . freq.pol.R . freq.table.R . gamma.R . globals.R . hipergeom.R . indepchisq.test.R . negativebinomial.R . normal.R . poisson.R . print.lstest.R . proportion.CI.R . proportion.test.R . read.data.R . regresion.R . s2mu.R . samplequant.R . samplesd.R . samplevar.R . sicri2018.data.R . smu.R . tStudent.R . unif.R . variance.CI.R . variance.hatmu.test.R . variance.mu.test.R . variance.test.R .  Full LearningStats package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

objectSignals  
Observer Pattern for S4
A mutable Signal object can report changes to its state, clients could register functions so that t ...
Download / Learn more Package Citations See dependency  
SiER  
Signal Extraction Approach for Sparse Multivariate Response Regression
Methods for regression with high-dimensional predictors andunivariate or maltivariate response varia ...
Download / Learn more Package Citations See dependency  
CTTinShiny  
Shiny Interface for the CTT Package
A Shiny interface developed in close coordination with the CTT package, providing a GUI that guides ...
Download / Learn more Package Citations See dependency  
RXshrink  
Maximum Likelihood Shrinkage using Generalized Ridge or Least Angle Regression
Functions are provided to calculate and display ridge TRACE Diagnostics for a variety of alternativ ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
AFR  
Toolkit for Regression Analysis of Kazakhstan Banking Sector Data
Tool is created for regression, prediction and forecast analysis of macroeconomic and credit data. ...
Download / Learn more Package Citations See dependency  

28,565

R Packages

239,283

Dependencies

75,677

Author Associations

28,566

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA