"PyMVPA is a Python module intended to ease pattern classification analyses of large datasets. In the neuroimaging contexts such analysis techniques are also known as decoding or MVPA analysis. PyMVPA provides high-level abstraction of typical processing steps and a number of implementations of some popular algorithms. While it is not limited to the neuroimaging domain, it is eminently suited for such datasets. PyMVPA is truly free software (in every respect) and additionally requires nothing but free-software to run."
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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.
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.
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.
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.
Adherence is defined as "the extent to which a person’s behavior coincides with medical or health
advice", which is very important, for both clinical researchers and physicians, to identify the treatment
effect of a specific medication(s).
The main goal of the Computer Assisted Search for Epidemics (CASE) project is to develop a reliable system that generates warnings when the number of reported cases of a particular infectious disease reaches a level that indicates an unusual or unexpected rate. The system is currently in use at the Swedish Institute for Infectious Disease Control (SMI). It performs daily surveillance using data obtained from the database to which all notifiable diseases are reported in Sweden.
EGADSS (Evidence-based Guideline and Decision Support System) is an open source tool that is designed to work in conjunction with primary care Electronic Medical Record (EMR) systems to provide patient specific point of care reminders in order to aid physicians provide high quality care. EGADSS is designed as a stand alone system that would respond to requests from existing Electronic Medical Records such as Wolf, Med Access, and MedOffIS to provide patient specific clinical guidance based on its internal collection of guidelines.
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."