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The 'DiagnosisMed' R Package

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DiagnosisMed is a package to analyze data from diagnostic test accuracy evaluating health conditions. It is being built to be used by health professionals. This package is able to estimate sensitivity and specificity from categorical and continuous test results including some evaluations of indeterminate results, or compare different categorical tests, and estimate reasonble cut-offs of tests and display it in a way commonly used by health professionals. No graphical interface is avalible yet. Partners are most welcome.

The 'epi' R Package

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The Epi package is mainly focused on "classical" chronic disease epidemiology. The package has grown out of the course Statistical Practice in Epidemiology using R.

OMERO

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OMERO is client-server software for visualisation, management and analysis of biological microscope images.

From the microscope to publication, OMERO handles all your images in a secure central repository. You can view, organise, analyse and share your data from anywhere you have internet access. Work with your images from a desktop app (Windows, Mac or Linux), from the web or from 3rd party software.

The 'epibasix' R Package

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This package contains elementary tools for analysis of common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity).

Appropriate print and summary statements are also written to facilitate interpretation wherever possible.

This package is a work in progress, so any comments or suggestions would be appreciated. Source code is commented throughout to facilitate modification. The target audience includes graduate students in various epi/biostatistics courses.

The 'epicalc' R Package

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Functions making R easy for epidemiological calculation.

Datasets from Dbase (.dbf), Stata (.dta), SPSS(.sav), EpiInfo(.rec) and Comma separated value (.csv) formats as well as R data frames can be processed to do make several epidemiological calculations.

rxncon

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The complexity of cellular networks is an outstanding challenge for documentation, visualisation and mathematical modelling. In this project, we develop a new way to describe these networks that minimises the combinatorial complexity and allows an automatic visualisation and export of mathematical (ODE/rulebased) models.

Features:

  • Automatic visualiztion with Cytoscape.
  • Automatic generation of rule based models for BioNetGen.
  • Storage of biological facts that can be used for modelling.

PyEPL

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Your rating: None Average: 2.6 (7 votes)

PyEPL (the Python Experiment-Programming Library) is a library for coding psychology experiments in Python. It supports presentation of both visual and auditory stimuli, and supports both manual (keyboard/joystick) and sound (microphone) input as responses.

The 'epiR' R Package

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A package for analysing epidemiological data. Contains functions for directly and indirectly adjusting measures of disease frequency, quantifying measures of association on the basis of single or multiple strata of count data presented in a contingency table, and computing confidence intervals around incidence risk and incidence rate estimates. Miscellaneous functions for use in meta-analysis, diagnostic test interpretation, and sample size calculations.

ADDIS

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ADDIS is a software developed within the Dutch Escher-project for managing and analyzing clinical trial information.

ADDIS is a proof-of-concept system that allows us to simultaneously discover the possibilities of and the requirements on a database of structured clinical trials data. The automated discovery and (meta-)analysis of trial data, as well as benefit-risk assessment is supported.

ADDIS comes with two built-in examples:

The R Project for Statistical Computing

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Your rating: None Average: 2.7 (3 votes)

R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R."

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