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cran
clv:Cluster Validation Techniques
Package contains most of the popular internal and external cluster validation methods ready to use for the most of the outputs produced by functions coming from package "cluster". Package contains also functions and examples of usage for cluster stability approach that might be applied to algorithms implemented in "cluster" package as well as user defined clustering algorithms.
Maintained by Lukasz Nieweglowski. Last updated 1 years ago.
81.3 match 1 stars 4.73 score 148 scripts 17 dependents
bachmannpatrick
CLVTools:Tools for Customer Lifetime Value Estimation
A set of state-of-the-art probabilistic modeling approaches to derive estimates of individual customer lifetime values (CLV). Commonly, probabilistic approaches focus on modelling 3 processes, i.e. individuals' attrition, transaction, and spending process. Latent customer attrition models, which are also known as "buy-'til-you-die models", model the attrition as well as the transaction process. They are used to make inferences and predictions about transactional patterns of individual customers such as their future purchase behavior. Moreover, these models have also been used to predict individuals’ long-term engagement in activities such as playing an online game or posting to a social media platform. The spending process is usually modelled by a separate probabilistic model. Combining these results yields in lifetime values estimates for individual customers. This package includes fast and accurate implementations of various probabilistic models for non-contractual settings (e.g., grocery purchases or hotel visits). All implementations support time-invariant covariates, which can be used to control for e.g., socio-demographics. If such an extension has been proposed in literature, we further provide the possibility to control for time-varying covariates to control for e.g., seasonal patterns. Currently, the package includes the following latent attrition models to model individuals' attrition and transaction process: [1] Pareto/NBD model (Pareto/Negative-Binomial-Distribution), [2] the Extended Pareto/NBD model (Pareto/Negative-Binomial-Distribution with time-varying covariates), [3] the BG/NBD model (Beta-Gamma/Negative-Binomial-Distribution) and the [4] GGom/NBD (Gamma-Gompertz/Negative-Binomial-Distribution). Further, we provide an implementation of the Gamma/Gamma model to model the spending process of individuals.
Maintained by Patrick Bachmann. Last updated 3 months ago.
clvcustomer-lifetime-valuecustomer-relationship-managementopenblasgslcppopenmp
33.2 match 55 stars 6.47 score 12 scripts
joshuaulrich
TTR:Technical Trading Rules
A collection of over 50 technical indicators for creating technical trading rules. The package also provides fast implementations of common rolling-window functions, and several volatility calculations.
Maintained by Joshua Ulrich. Last updated 1 years ago.
algorithmic-tradingfinancetechnical-analysis
3.3 match 338 stars 15.11 score 2.8k scripts 359 dependents
evigneau
ClustVarLV:Clustering of Variables Around Latent Variables
Functions for the clustering of variables around Latent Variables, for 2-way or 3-way data. Each cluster of variables, which may be defined as a local or directional cluster, is associated with a latent variable. External variables measured on the same observations or/and additional information on the variables can be taken into account. A "noise" cluster or sparse latent variables can also be defined.
Maintained by Evelyne Vigneau. Last updated 3 years ago.
14.3 match 2.62 score 21 scripts