tag:crantastic.org,2005:/packages/serrsBayesLatest activity for serrsBayes2019-04-29T11:42:53Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/877952019-04-29T11:42:53Z2019-04-29T11:42:53ZserrsBayes was upgraded to version 0.4-0<a href="/packages/serrsBayes">serrsBayes</a> was <span class="action">upgraded</span> to version <a href="/packages/serrsBayes/versions/83473">0.4-0</a><br /><h3>Package description:</h3><p>Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/759712018-06-05T12:02:47Z2018-06-05T12:02:47ZserrsBayes was upgraded to version 0.3-13<a href="/packages/serrsBayes">serrsBayes</a> was <span class="action">upgraded</span> to version <a href="/packages/serrsBayes/versions/72496">0.3-13</a><br /><h3>Package description:</h3><p>Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/717082018-02-18T21:02:35Z2018-02-18T21:02:35ZserrsBayes was upgraded to version 0.3-12<a href="/packages/serrsBayes">serrsBayes</a> was <span class="action">upgraded</span> to version <a href="/packages/serrsBayes/versions/68487">0.3-12</a><br /><h3>Package description:</h3><p>Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/716472018-02-15T23:22:32Z2018-02-15T23:22:32ZserrsBayes was upgraded to version 0.3-11<a href="/packages/serrsBayes">serrsBayes</a> was <span class="action">upgraded</span> to version <a href="/packages/serrsBayes/versions/68427">0.3-11</a><br /><h3>Package description:</h3><p>Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/716142018-02-14T19:02:38Z2018-02-14T19:02:38ZserrsBayes was released<a href="/packages/serrsBayes">serrsBayes</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>Sequential Monte Carlo (SMC) algorithms for fitting a generalised additive mixed model (GAMM) to surface-enhanced resonance Raman spectroscopy (SERRS), using the method of Moores et al. (2016) <arXiv:1604.07299>. Multivariate observations of SERRS are highly collinear and lend themselves to a reduced-rank representation. The GAMM separates the SERRS signal into three components: a sequence of Lorentzian, Gaussian, or pseudo-Voigt peaks; a smoothly-varying baseline; and additive white noise. The parameters of each component of the model are estimated iteratively using SMC. The posterior distributions of the parameters given the observed spectra are represented as a population of weighted particles.</p>crantastic.org