Showing 113 of total 113 results (show query)

sbgraves237

Ecdat:Data Sets for Econometrics

Data sets for econometrics, including political science.

Maintained by Spencer Graves. Last updated 4 months ago.

6.8 match 2 stars 7.25 score 740 scripts 3 dependents

alanarnholt

BSDA:Basic Statistics and Data Analysis

Data sets for book "Basic Statistics and Data Analysis" by Larry J. Kitchens.

Maintained by Alan T. Arnholt. Last updated 2 years ago.

3.4 match 7 stars 9.11 score 1.3k scripts 6 dependents

peterkdunn

GLMsData:Generalized Linear Model Data Sets

Data sets from the book Generalized Linear Models with Examples in R by Dunn and Smyth.

Maintained by Peter K. Dunn. Last updated 3 years ago.

9.1 match 2.61 score 220 scripts

alanarnholt

PASWR:Probability and Statistics with R

Functions and data sets for the text Probability and Statistics with R.

Maintained by Alan T. Arnholt. Last updated 3 years ago.

4.5 match 2 stars 4.70 score 241 scripts

alanarnholt

PASWR2:Probability and Statistics with R, Second Edition

Functions and data sets for the text Probability and Statistics with R, Second Edition.

Maintained by Alan T. Arnholt. Last updated 3 years ago.

4.5 match 1 stars 4.24 score 260 scripts

arbrazzale

cond:Approximate Conditional Inference for Logistic and Loglinear Models

Higher order likelihood-based inference for logistic and loglinear models.

Maintained by Alessandra R. Brazzale. Last updated 7 years ago.

3.6 match 3.51 score 27 scripts 2 dependents

p0bs

p0bservations:Assorted Functions and Observations by P0bs

Provides assorted functions by p0bs.

Maintained by Robin Penfold. Last updated 4 months ago.

1.8 match 2.30 score 1 scripts

cran

ADLP:Accident and Development Period Adjusted Linear Pools for Actuarial Stochastic Reserving

Loss reserving generally focuses on identifying a single model that can generate superior predictive performance. However, different loss reserving models specialise in capturing different aspects of loss data. This is recognised in practice in the sense that results from different models are often considered, and sometimes combined. For instance, actuaries may take a weighted average of the prediction outcomes from various loss reserving models, often based on subjective assessments. This package allows for the use of a systematic framework to objectively combine (i.e. ensemble) multiple stochastic loss reserving models such that the strengths offered by different models can be utilised effectively. Our framework is developed in Avanzi et al. (2023). Firstly, our criteria model combination considers the full distributional properties of the ensemble and not just the central estimate - which is of particular importance in the reserving context. Secondly, our framework is that it is tailored for the features inherent to reserving data. These include, for instance, accident, development, calendar, and claim maturity effects. Crucially, the relative importance and scarcity of data across accident periods renders the problem distinct from the traditional ensemble techniques in statistical learning. Our framework is illustrated with a complex synthetic dataset. In the results, the optimised ensemble outperforms both (i) traditional model selection strategies, and (ii) an equally weighted ensemble. In particular, the improvement occurs not only with central estimates but also relevant quantiles, such as the 75th percentile of reserves (typically of interest to both insurers and regulators). Reference: Avanzi B, Li Y, Wong B, Xian A (2023) "Ensemble distributional forecasting for insurance loss reserving" <doi:10.48550/arXiv.2206.08541>.

Maintained by Yanfeng Li. Last updated 11 months ago.

0.8 match 2.70 score

zangt2

obcost:Obesity Cost Database

This database contains necessary data relevant to medical costs on obesity throughout the United States. This database, in form of an R package, could output necessary data frames relevant to obesity costs, where the clients could easily manipulate the output using difference parameters, e.g. relative risks for each illnesses. This package contributes to parts of our published journal named "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis". Please use the following citation for the journal: Woods Thomas, Tatjana Miljkovic (2022) "Modeling the Economic Cost of Obesity Risk and Its Relation to the Health Insurance Premium in the United States: A State Level Analysis" <doi:10.3390/risks10100197>. The database is composed of the following main tables: 1. Relative_Risks: (constant) Relative risks for a given disease group with a risk factor of obesity; 2. Disease_Cost: (obesity_cost_disease) Supplementary output with all variables related to individual disease groups in a given state and year; 3. Full_Cost: (obesity_cost_full) Complete output with all variables used to make cost calculations, as well as cost calculations in a given state and year; 4. National_Summary: (obesity_cost_national_summary) National summary cost calculations in a given year. Three functions are included to assist users in calling and adjusting the mentioned tables and they are data_load(), data_produce(), and rel_risk_fun().

Maintained by Tianyue Zang. Last updated 2 years ago.

0.8 match 1.70 score