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rstudio

shiny:Web Application Framework for R

Makes it incredibly easy to build interactive web applications with R. Automatic "reactive" binding between inputs and outputs and extensive prebuilt widgets make it possible to build beautiful, responsive, and powerful applications with minimal effort.

Maintained by Winston Chang. Last updated 5 days ago.

reactiverstudioshinyweb-appweb-development

5.5k stars 21.31 score 108k scripts 1.8k dependents

enricoschumann

NMOF:Numerical Methods and Optimization in Finance

Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). The package provides implementations of optimisation heuristics (Differential Evolution, Genetic Algorithms, Particle Swarm Optimisation, Simulated Annealing and Threshold Accepting), and other optimisation tools, such as grid search and greedy search. There are also functions for the valuation of financial instruments such as bonds and options, for portfolio selection and functions that help with stochastic simulations.

Maintained by Enrico Schumann. Last updated 2 days ago.

black-scholesdifferential-evolutiongenetic-algorithmgrid-searchheuristicsimplied-volatilitylocal-searchoptimizationparticle-swarm-optimizationsimulated-annealingthreshold-accepting

37 stars 9.27 score 101 scripts 3 dependents

tilmandavies

sparr:Spatial and Spatiotemporal Relative Risk

Provides functions to estimate kernel-smoothed spatial and spatio-temporal densities and relative risk functions, and perform subsequent inference. Methodological details can be found in the accompanying tutorial: Davies et al. (2018) <DOI:10.1002/sim.7577>.

Maintained by Tilman M. Davies. Last updated 11 days ago.

8 stars 7.38 score 112 scripts 8 dependents

luisbenavides

SNSchart:Sequential Normal Scores in Statistical Process Management

The methods discussed in this package are new non-parametric methods based on sequential normal scores 'SNS' (Conover et al (2017) <doi:10.1080/07474946.2017.1360091>), designed for sequences of observations, usually time series data, which may occur singly or in batches, and may be univariate or multivariate. These methods are designed to detect changes in the process, which may occur as changes in location (mean or median), changes in scale (standard deviation, or variance), or other changes of interest in the distribution of the observations, over the time observed. They usually apply to large data sets, so computations need to be simple enough to be done in a reasonable time on a computer, and easily updated as each new observation (or batch of observations) becomes available. Some examples and more detail in 'SNS' is presented in the work by Conover et al (2019) <arXiv:1901.04443>.

Maintained by Luis Benavides. Last updated 4 years ago.

3.70 score 1 scripts

cran

GCPM:Generalized Credit Portfolio Model

Analyze the default risk of credit portfolios. Commonly known models, like CreditRisk+ or the CreditMetrics model are implemented in their very basic settings. The portfolio loss distribution can be achieved either by simulation or analytically in case of the classic CreditRisk+ model. Models are only implemented to respect losses caused by defaults, i.e. migration risk is not included. The package structure is kept flexible especially with respect to distributional assumptions in order to quantify the sensitivity of risk figures with respect to several assumptions. Therefore the package can be used to determine the credit risk of a given portfolio as well as to quantify model sensitivities.

Maintained by Kevin Jakob. Last updated 8 years ago.

cpp

1 stars 1.00 score

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