effCont

<p>Families to model effectiveness distributions with continuous support. Currently implemented
families are:</p><table><tr><td>
  <code><a rd-options="" href="/link/effCont_norm?package=simIReff&version=1.0" data-mini-rdoc="simIReff::effCont_norm">effCont_norm</a></code> </td>
<td> Truncated Normal. </td>
</tr><tr><td>
  <code><a rd-options="" href="/link/effCont_beta?package=simIReff&version=1.0" data-mini-rdoc="simIReff::effCont_beta">effCont_beta</a></code> </td>
<td> Beta. </td>
</tr><tr><td>
  <code><a rd-options="" href="/link/effCont_nks?package=simIReff&version=1.0" data-mini-rdoc="simIReff::effCont_nks">effCont_nks</a></code> </td>
<td> Truncated Kernel-smoothed with Gaussian kernel. </td>
</tr></table>

Provides tools for the stochastic simulation of effectiveness scores to mitigate
data-related limitations of Information Retrieval evaluation research, as described in Urbano and
Nagler (2018) <doi:10.1145/3209978.3210043>. These tools include: fitting, selection and plotting
distributions to model system effectiveness, transformation towards a prespecified expected value,
proxy to fitting of copula models based on these distributions, and simulation of new evaluation
data from these distributions and copula models.

Juli<c3><a1>n Urbano

simIReff

Stochastic Simulation for Information Retrieval Evaluation:
Effectiveness Scores

Thomas Nagler

effCont function

<p>Families to model effectiveness distributions with continuous support. Currently implemented
families are:</p><table><tr><td>
  <code><a rd-options='' href='effCont_norm'>effCont_norm</a></code> </td>
<td> Truncated Normal. </td>
</tr><tr><td>
  <code><a rd-options='' href='effCont_beta'>effCont_beta</a></code> </td>
<td> Beta. </td>
</tr><tr><td>
  <code><a rd-options='' href='effCont_nks'>effCont_nks</a></code> </td>
<td> Truncated Kernel-smoothed with Gaussian kernel. </td>
</tr></table>

effCont: Continuous Effectiveness Distributions

Description

Arguments

See Also

`effCont_norm`	Truncated Normal.
`effCont_beta`	Beta.
`effCont_nks`	Truncated Kernel-smoothed with Gaussian kernel.