Octave Statistics Package Download [UPDATED]

The statistics package is part of the Octave Packages. Since version 1.5.0, the statistics package requires Octave version 6.1 or higher. From Octave v7.2 or later, you can install the latest statistics package (currently 1.5.3) with the following command:

The following sections provide an overview of the functions available in the statistics package sorted alphabetically and arranged in groups similarly to the package's INDEX file. the TODO subsections are only informative of the current development plans for the forthcoming releases and they are not intended for reporting bugs, missing features or incompatibilities. Please report these in the statistics repository at GitHub.

Octave Statistics Package Download

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The following table lists the cdf, icdf, pdf, and random functions available in the statistics package. Since version 1.5.3, all CDFs support the "upper" option for evaluating the complement of the respective CDF.

I've started learning MATLAB trough Octave. I wanted to use statistics functions and for that I found out that I need package statistics of Octave. So basically I'm totally new to all of these stuffs. I tried out downloading it using Macport and Octave pkg install code, but couldn't do it.

Octave has support for various statistical methods. The emphasis is on basicdescriptive statistics, but the Octave Forge statistics package includesprobability distributions, statistical tests, random number generation, andmuch more.

The --norc will prevent autoloading of packages. I don't remember all the details, but I don't think you can remove that option as the task is running as user Jobe, who for security reasons has no home directory.

Your hint about removing --norc was one part of the solution, and an important one. Let me detail here how I got statistics to load automatically in CodeRunner, just in case someone else is looking for this:

I have been using the statistics package for quite a while now and am quite grateful for it. Recently, I upgraded my OS (Kubuntu, from 22.04 to 22.10) and thus reinstalled all my octave and related packages. Octave version is now 7.2.

var and std functions in statistics 1.5.2 allow for the 'omitnan' option. Please, use these instead of nanvar and nanstd, which were removed. As previously mentioned, if required they will be reinstated in the forthcoming release at the end of January so they can properly work with the updated functions.

There is an ongoing effort to include these functions with the 'omitnan' flag (among other options) into core Octave 8, so it would be best to start migrating your code to use these updated functions.

Download Source Package octave-statistics: [octave-statistics_1.4.1-3.dsc] [octave-statistics_1.4.1.orig.tar.gz] [octave-statistics_1.4.1-3.debian.tar.xz] Maintainer: Ubuntu MOTU Developers (Mail Archive)Please consider filing a bug or asking a question via Launchpad before contacting the maintainer directly.

Other Packages Related to octave-statistics depends recommends suggests enhances dep:octave (>= 5.1.0) GNU Octave language for numerical computations dep:octave-io (>= 1.0.18) input/output data functions for Octave Download octave-statistics Download for all available architectures ArchitecturePackage SizeInstalled SizeFiles all183.3 kB1,468.0 kB [list of files] This page is also available in the following languages:

Just at a quick glance when I run with dev octave 7, I see warnings for:

The '.+' operator was deprecated in version 7 and will not be allowed in a future version of Octave; please use '+' instead;

I think it would probably be better to get these fixed as Octave-7.x will be out soon and this warning may confuse users.

But OK, statistics isn't quite the only package where this syntax is used, though.

Added BCa intervals for all types of bootstrap sampling (ordinary, weighted, block, cluster etc.). Bug fix for cluster bootstrap resampling. Added parallel computing capabilities for Octave. Subfunctions moved into function files in iboot package.

If bootstrap iteration is requested, bootstat returns: boostat statistics from both 1st and 2nd bootstrap; the double bootstrap bias and bias-corrected statistic in S; and, the double boostrap corrected standard error of the statistic in S.

The distribution functions like normcdf are missing from Octave. This seems to be a repackaging bug. The distributions where recently moved from the base to the statistics package. Apparently, the package versions in the repository are out of sync. I installed all octave packages, so it seems to be really missing and not hidden in a rare package.

In this chapter a few MATLAB/Octave commands for statistics are listed and elementary sample codes are given. This should help you to get started using Octave/MATLAB for statistical problems. The short notes you are looking at right now should serve as a starting point and will not replace reading and understanding the built-in documentation of Octave and/or MATLAB. For users of MATLAB is is assumed that the statistics toolbox is available. For users of Octave is is assumed that the statistics package is available and loaded.

Packages of Octave packages have their own naming scheme. They should take into account the upstream name of the package. This makes a package name format of octave-$NAME. When in doubt, use the name of the module that you type to import it in octave.

Limitations in the pkg function of octave (pkg.m) means that versioning of octave packages requires that the package version must have a MAJOR.MINOR.MICRO format. Failing to use this format results in octave not recognising binary package components in %prefix/libexec.

GNU Octave, any version ranging from 5.2.0 to 8.3.0, with the statisticspackage from Octave-Forge. Note however that the Dynare installer forWindows requires a more specific version of Octave, as indicated on thedownload page.

On Debian, Ubuntu and Linux Mint, the Dynare package can be installed with:apt install dynare. This will give a fully-functional Dynare installationusable with Octave. If you have MATLAB installed, you should also do: aptinstall dynare-matlab (under Debian, this package is in the contribsection). Documentation can be installed with apt install dynare-doc. Thestatus of those packages can be checked at those pages:

One principal goal of descriptive statistics is to represent the essence of a large data set concisely. Octave provides the mean, median, and mode functionswhich all summarize a data set with just a single number corresponding to the central tendency of the data.

The Octave Forge site ( ) has a list of useful community based libraries that can be added. To load a custom library, first install any dependencies then use the Octave pkg (package) command to install and load new library. For example to load the Symbolic library:

Many MATLAB codes run with very little or no modification under Octave, a free interactive data analysis software package with syntax and functionality that are similar to MATLAB's. Since using Octave is not constrained by license issues, we encourage MATLAB users to try it, particularly those who have long-running MATLAB jobs. Depending on compute intensity, Octave usually runs slower than MATLAB but it may be suitable for most data analysis work and you won't risk having jobs killed because of a lack of licenses.

Figure 8. Overview of CoSMoMVPA architecture. Data collected (and optionally, preprocessed) in the fMRI or M/EEG modalities from a wide range of neuroimaging analysis packages (A) can be imported and exported (B) to a uniform CoSMoMVPA dataset structure (C). CoSMoMVPA provides several measures (D), including classification analysis [with various classifiers (E) and partitioning schemes (F)], correlation analysis, representational similarity analysis, and the time generalization method [which uses another measure for all possible pairs of time points for training and test sets (G)]. Any measure can be applied directly (@) to the input dataset (C), which results in another dataset (H). CoSMoMVPA also provides various neighborhood definitions across voxel, surface nodes, time point, and frequency bin dimensions (I); these can be combined by crossing multiple dimensions (J). By combining a measure (D) with a neighborhood (I,J), a searchlight can be applied to the input dataset (C) resulting in another dataset representing a searchlight map (H). Neighborhoods are also used for Threshold-Free Cluster Enhancement multiple comparison correction. The result dataset from a measure, a searchlight analysis using a measure and neighborhood, or multiple comparison correction is a dataset (H) that, depending on the input and analysis parameters, can have various feature dimensions which can represent, for example, volumetric fMRI, surface-based fMRI, M/EEG topologies, M/EEG time series, M/EEG space by time data, or localized time generalization maps (K). Result datasets can be converted back (B) for visualization or further analysis in a wide variety of neuroimaging analysis packages (A).

After a few years in industry, Robert W. Hayden (bob@statland.org) taught mathematics at colleges and universities for 32 years and statistics for 20 years. In 2005 he retired from full-time classroom work. He now teaches statistics online at statistics.com and does summer workshops for high school teachers of Advanced Placement Statistics. He contributed the chapter on evaluating introductory statistics textbooks to the MAA's Teaching Statistics. e24fc04721