Search Results for "example"

The CausalImpact repository houses an R package that implements causal inference in time series using Bayesian structural time series models. Its goal is to estimate the effect of an intervention (e.g. a marketing campaign, policy change) on a time series outcome by predicting what would have happened in a counterfactual “no intervention” world. The package requires as input a response time series plus one or more control (covariate) time series that are assumed unaffected by the...

Statistics101 is an Integrated Development Environment (IDE) that uses a simple, powerful language called “Resampling Stats” to develop Monte Carlo programs to analyze and solve statistical problems. The original Resampling Stats language and computer program were developed by Dr. Julian Simon (https://www.juliansimon.com/) and Peter Bruce (https://www.scientificamerican.com/author/peter-bruce/) as a new way to teach Statistics to social science students. Of course, social science students...

...SWire can be interrogated on a local network and it accepts AJAX queries, thus enabling the development of web applications which provide the user with graphical user interfaces. For example, a web application running on a browser can interact with Stata on a local network; this application could be a data entry form or an automatic report. By providing a communication protocol, SWire makes it possible to connect Stata with many other applications for data exchange and basic interaction, thereby extending the capabilities of Stata. ...

...This information can be used for: - obtaining the knowledge which applications are most frequently used in the network - providing the users some basic statistics about their Internet connection usage (for example for which kinds of applications their connection is used the most) - creating scientific profiles of traffic generated by different applications or different groups of applications - creating a traffic generator, to imitate traffic generated by particular applications, or to imitate the real traffic in the network - implementing smart assessment of QoS in the network at the users' level and in the core of the network - obtaining precise data needed to train Machine Learning Algorithms - many more cases :-)

...Main object is a sparse bit string (Bits) that efficiently scales up to 2^63 bits normally compressed as forest has duplicated substrings. Bits objects support reading bit, byte, short, int, or long (Java primitives) at any bit index in 64 bit range. Example: instead of building a class to hold a header and then data, represent all of that as Bits, subranges of them, and ints for sizes of its parts. Expansion ability for other kinds of compression, since Bits is a Java interface. Main functions on bits are substring, concat, number of 0 or 1 bits, and number of bits (size). All those operations can be done millions of times per second regardless of size because the AVL forest reuses existing branches recursively. ...

IterInt is a numerical integration package that implements iterated numerical integration methods. Iterated methods can be used to compute low dimensional (less than ten, for example) integration problems to get very accurate results.

libfgen is a library that implements an efficient and customizable genetic algorithm (GA). It also provides particle swarm optimization (PSO) functionality and an interface for real-valued function minimization or model fitting. It is written in C, but can also be compiled with a C++ compiler. Both Linux and Windows are supported.

Statastizard(c)2012 Nick Emblow is a program designed to guide its user into the right statistical test, perform the test, and report the results in a meaningful way. The idea of this is that the user won't actually need to know that they are using a mann-whitney U over a t-test, for example - it just tests for normality and homoscedastic of variance, and decides which test to use, then reports the results. Why? Because time after time, people come to me with some test that they've done that they think "proves" something - when in reality they've either not done a comparative test to start with (ex. a correlation), failed to test for confounding factors (such as normality etc) and as a result, failed to appropriately identify even the right test to do.