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statistics (26)



Apophenia
'Apophenia' is a statistical library for C. It provides functions on the same level as those of the typical stats package (OLS, probit, singular value decomposition, &c.) but doesn't tie the user to an ad hoc language or environment. It uses the GNU Scientific Library for number crunching and SQLite for data management, so the library itself focuses on model estimation and quickly processing data.

Autoclass
AutoClass solves the problem of automatic discovery of classes in data (sometimes called clustering or unsupervised learning), as distinct from the generation of class descriptions from labeled examples (called supervised learning). It aims to discover the 'natural' classes in the data. AutoClass is applicable to observations of things that can be described by a set of attributes, without referring to other things. The data values corresponding to each attribute are limited to be either numbers or the elements of a fixed set of symbols. With numeric data, a measurement error must be provided.

Bc Heckert gnu.small.png
'Bc' is an arbitrary precision numeric processing language. Its syntax is similar to C, but differs in many substantial areas. It supports interactive execution of statements. 'Bc' is a utility included in the POSIX P1003.2/D11 draft standard. This version does not use the historical method of having bc be a compiler for the dc calculator (the POSIX document doesn't specify how bc must be implemented). This version has a single executable that both compiles the language and runs the resulting 'byte code.' The byte code is not the dc language.

Cl-ana
cl-ana is a library of modular utilities for reasonably high performance data analysis & visualization using Common Lisp. (Reasonably means I have to be able to use it for analyzing particle accelerator data). The library is made of various sublibraries and is designed in a very bottom-up way so that if you don't care about some feature you don't have to load it.

The functionality support so far are

  • Tabular data analysis: Read-write of large datasets stored in HDF5 files are supported, along with ntuple datasets, CSVs, and in-memory data tables. Users can add their own table types by defining 4 methods and extending the table CLOS type.
  • Histograms: Binned data analysis is supported with both contiguous and sparse histogram types; functional interface is provided via map (which allows reduce/fold) and filter.
  • Plotting: Uses gnuplot for plotting dataset samples, plain-old lisp functions, histograms, strings-as-formulae, and anything else the user wishes to add via methods on a couple of generics.
  • Fitting: Uses GSL for non-linear least squares fitting. Uses plain-old lisp functions as the fit functions and can fit against dataset samples, histograms, and whatever the user adds.
  • Generic mathematics: CL doesn't provide extendable math functions, so cl-ana provides these as well as a convenient mechanism (a single function) for using these functions instead of the non-extendable versions. Already included are error propogation and quantities (values with units, e.g. 5 meters) as well as a GNU Octave-style handling of sequences (e.g. (+ (1 2) (3 4)) --> (4 6)).

Dap Heckert gnu.small.png
Dap is a small statistics and graphics package, based on C, that provides core methods of data management, analysis, and graphics commonly used in statistical consulting practice. Anyone familiar with basic C syntax can learn Dap quickly and easily from the manual and the examples in it. Advanced features of C are not necessary, although they are available. As of Version 3.0, Dap can read SBS programs, thereby freeing the user from having to learn any C at all to run straightforward analyses. The manual contains a brief introduction to the C syntax needed for C-style programming for Dap. Because Dap processes files one line at a time, rather than reading entire files into memory, it can be, and has been, used on data sets that have very many lines and/or very many variables.

Data Frame
In the R language, a dataframe object is a way to group tabular data. The functions in this package allow the manipulation of data in a similar way in Octave. Dataframe objects in Octave can be created in a variety of ways (from other objects or from tabular data in a file) and then can be accessed either as matrix or by column name.

This Octave add-on package is part of the Octave-Forge project.

DataStatix
DataStatix is a free software for GNU/Linux and Windows useful to manage data of every kind (although it has been written to manage biomedical data), to create descriptive statistics and graphs and to export items easily to R environment or to other statistic softwares. In order to handle properly big amount of data and many concurrent users, DataStatix works with MySql database and it has been developed and tested with MySql community edition 5.5. Some features of the software are: users management (create, delete, modify password) within the software; different users levels of data access (administrator, default, read only); user defined templates (models) of data, to create new databases easily; importation and esportation of data in CSV format (used also by Calc and Excel); updating of existing data from a CSV file created with DataStatix; descriptive statistics from every data (some more kind of statistics to come); graphs from every data.

Datamash Heckert gnu.small.png
Datamash is a command-line program which performs basic numeric, textual and statistical operations on input textual data files.

it is designed to be portable and reliable, and aid researchers to easily automate analysis pipelines, without writing code or even short scripts.

KNIME
KNIME [naim] is a user-friendly graphical workbench for the entire analysis process: data access, data transformation, initial investigation, powerful predictive analytics, visualisation and reporting. The open integration platform provides over 1000 modules (nodes), including those of the KNIME community and its extensive partner network.

MLPACK
MLPACK is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and flexibility for expert users. MLPACK contains the following algorithms: Collaborative Filtering, Density Estimation Trees, Euclidean Minimum Spanning Trees, Fast Exact Max-Kernel Search (FastMKS), Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), Kernel Principal Component Analysis (KPCA), K-Means Clustering, Least-Angle Regression (LARS/LASSO), Local Coordinate Coding, Locality-Sensitive Hashing (LSH), Logistic regression, Naive Bayes Classifier, Neighbourhood Components Analysis (NCA), Non-negative Matrix Factorization (NMF), Principal Components Analysis (PCA), Independent component analysis (ICA), Rank-Approximate Nearest Neighbor (RANN), Simple Least-Squares Linear Regression (and Ridge Regression), Sparse Coding, Tree-based Neighbor Search (all-k-nearest-neighbors, all-k-furthest-neighbors), Tree-based Range Search.

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