update vignettes and docs

Author: adw96

DESCRIPTION

@@ -7,7 +7,7 @@ Authors@R: as.person(c(
   ))
 Description: tinyvamp estimates the model described in Clausen & Willis, 2022. Notably, our model allows relative abundances to lie on the boundary of the simplex. We present a stable algorithm for computing parameter estimates, asymptotically valid procedures for inference in this nonstandard problem, and examples of the utility of the method. Our approach can be used to select or compare experimental protocols, design experiments with appropriate control data, analyze mixed-specimen samples, and remove across-sample contamination.
 URL: https://github.com/statdivlab/tinyvamp, https://statdivlab.github.io/tinyvamp/
-License: BSD_3_clause + file LICENSE
+License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
 Roxygen: list(markdown = TRUE)

LICENSE

@@ -1,35 +1,2 @@
-=======
-License
-=======
-
-Files: tinyvamp/*/*.R
-Copyright: 2022, The Regents of the University of Washington.
-License: BSD-3-Clause
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are
-met:
-
-    Redistributions of source code must retain the above copyright
-    notice, this list of conditions and the following disclaimer.
-
-    Redistributions in binary form must reproduce the above copyright
-    notice, this list of conditions and the following disclaimer in
-    the documentation and/or other materials provided with the
-    distribution.
-
-    Neither the name of the University of Washington nor the names of its
-    contributors may be used to endorse or promote products derived
-    from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+YEAR: 2023
+COPYRIGHT HOLDER: tinyvamp authors

R/fastnnls.R

@@ -177,183 +177,3 @@ fast_nnls <- function(
 
   return(d)
 }
-
-fast_nnls_Matrix <- function(
-  ZTx,
-  ZTZ,
-  active = NULL,
-  unconstrained = NULL,
-  tolerance = 1e-10
-) {
-  # store number of covariates
-  p <- nrow(ZTZ)
-
-  if (length(intersect(active, unconstrained)) > 0) {
-    stop(
-      "A variable cannot be assigned to the unconstrained set and to the active set."
-    )
-  }
-  ### getting stuck in this initial loop for some reason! check it out!
-  if (!is.null(active)) {
-    #set d = 0 to start
-    d <- matrix(0, nrow = p, ncol = 1)
-    #check appropriateness of initial active set
-    s <- matrix(0, nrow = p, ncol = 1)
-    not_active <- !(1:p %in% active)
-    s_passive <- qr.solve(
-      ZTZ[not_active, not_active, drop = F],
-      ZTx[not_active, drop = F],
-      tol = 1e-14
-    )
-    s[not_active, ] <- s_passive
-
-    ### redefine not_active to excluded unconstrained variables (if included)
-    if (!is.null(unconstrained)) {
-      not_active <- !(1:p %in% union(active, unconstrained))
-      s_passive <- s[not_active]
-    }
-    #Enter inner loop
-    if (length(s_passive) > 0) {
-      while (ifelse(length(s_passive) > 0, min(s_passive) <= 0, FALSE)) {
-        alphas <- (-(d[not_active]) / (d[not_active] - s_passive))
-        alphas <- alphas[s_passive < 0]
-        alpha <- min(-alphas)
-
-        d_archive <- d
-        d <- d + alpha * (s - d)
-        stopifnot(sum(is.na(d)) == 0)
-
-        if (is.null(unconstrained)) {
-          active <- (1:p)[d == 0]
-          not_active <- !(1:p %in% active)
-        }
-        if (!is.null(unconstrained)) {
-          active <- (1:p)[(d == 0) & (!((1:p) %in% unconstrained))]
-          not_active <- !(1:p %in% active)
-        }
-
-        s <- matrix(0, nrow = p)
-        if (length(active) > 0) {
-          s_passive <- qr.solve(
-            ZTZ[not_active, not_active],
-            ZTx[not_active, ],
-            tol = 1e-14
-          )
-          s[not_active, ] <- s_passive
-        } else {
-          s <- s_passive <- qr.solve(ZTZ, ZTx, tol = 1e-14)
-        }
-
-        if (!is.null(unconstrained)) {
-          not_active <- !(1:p %in% union(active, unconstrained))
-          s_passive <- s[not_active]
-        }
-      }
-    }
-    d <- s
-    active <- (1:p)[d == 0]
-
-    w <- ZTx - ZTZ %*% d
-  }
-  if (is.null(active)) {
-    if (is.null(unconstrained)) {
-      active <- 1:p
-      d <- matrix(0, nrow = p, ncol = 1)
-      w <- ZTx
-    } else {
-      active <- (1:p)[!(1:p %in% unconstrained)]
-      d <- matrix(0, nrow = p, ncol = 1)
-      d[unconstrained] <-
-        qr.solve(
-          ZTZ[unconstrained, unconstrained],
-          ZTx[unconstrained, ],
-          tol = 1e-14
-        )
-      w <- (ZTx - ZTZ %*% d)
-    }
-  }
-
-  # counter <- 0
-  #Enter main loop - ???? are loop conditions correct ???
-  while ((ifelse(length(active) > 0, max(w[active]), -1) > tolerance)) {
-    # counter <- counter + 1
-    # print(counter)
-
-    to_remove <- which.max(w[active])
-    # print(to_remove)
-    active <- active[-to_remove]
-
-    if (length(active) > 0) {
-      s <- matrix(0, nrow = p)
-      not_active <- !(1:p %in% active)
-
-      s_passive <- qr.solve(
-        ZTZ[not_active, not_active],
-        ZTx[not_active, ],
-        tol = 1e-14
-      )
-      s[not_active, ] <- s_passive
-    } else {
-      s <- s_passive <- qr.solve(ZTZ, ZTx, tol = 1e-14)
-    }
-
-    ### redefine not_active to excluded unconstrained variables (if included)
-    if (!is.null(unconstrained)) {
-      not_active <- !(1:p %in% union(active, unconstrained))
-      s_passive <- s[not_active]
-    }
-
-    #Enter inner loop
-    if (length(s_passive) > 0) {
-      while (ifelse(length(s_passive) > 0, min(s_passive) <= 0, FALSE)) {
-        alphas <- (-(d[not_active]) / (d[not_active] - s_passive))
-        alphas <- alphas[s_passive < 0]
-        alpha <- min(-alphas)
-
-        d_archive <- d
-        d <- d + alpha * (s - d)
-        stopifnot(sum(is.na(d)) == 0)
-
-        if (is.null(unconstrained)) {
-          active <- (1:p)[d == 0]
-          not_active <- !(1:p %in% active)
-        }
-        if (!is.null(unconstrained)) {
-          active <- (1:p)[(d == 0) & (!((1:p) %in% unconstrained))]
-          not_active <- !(1:p %in% active)
-        }
-
-        s <- matrix(0, nrow = p)
-        if (length(active) > 0) {
-          s_passive <- qr.solve(
-            ZTZ[not_active, not_active],
-            ZTx[not_active, ],
-            tol = 1e-14
-          )
-          s[not_active, ] <- s_passive
-        } else {
-          s <- s_passive <- qr.solve(ZTZ, ZTx, tol = 1e-14)
-        }
-
-        ### redefine not_active to excluded unconstrained variables (if included)
-        if (!is.null(unconstrained)) {
-          not_active <- !(1:p %in% union(active, unconstrained))
-          s_passive <- s[not_active]
-        }
-      }
-    }
-
-    d <- s
-
-    stopifnot(sum(is.na(d)) == 0)
-
-    w <- (ZTx - ZTZ %*% d)
-
-    # counter <- counter + 1
-
-    # print(counter)
-    # stopifnot(counter<13)
-  }
-
-  return(d)
-}

R/logsum.R

@@ -35,42 +35,3 @@ sum_of_logs <- function(x, y = NULL) {
 }
 
 
-diff_of_logs <- function(larger, smaller) {
-  return(larger + log(1 - exp(smaller - larger)))
-}
-
-
-signed_log_sum <- function(log_magnitudes, signs) {
-  # if no nonzero summands, return zero
-  if (sum(signs != 0) == 0) {
-    return(c(0, 0))
-  }
-
-  # if no negative summands, return sum of positive summands
-  if (sum(signs == -1) == 0) {
-    return(c(sum_of_logs(log_magnitudes[signs == 1]), 1))
-  }
-
-  # if no positive summands, return sum of negative summands (with sign -1)
-  if (sum(signs == 1) == 0) {
-    return(c(sum_of_logs(log_magnitudes[signs == -1]), -1))
-  }
-
-  # otherwise sum positive and negative magnitudes separately
-  positives <- log_magnitudes[signs == 1]
-  negatives <- log_magnitudes[signs == -1]
-
-  pos_logsum <- sum_of_logs(positives)
-  neg_logsum <- sum_of_logs(negatives)
-
-  # if positive magnitude equals negative magnitude, return zero
-  if (pos_logsum == neg_logsum) {
-    return(c(0, 0))
-  }
-
-  # otherwise return difference of positive and negative terms with appropriate sign
-  return(c(
-    diff_of_logs(max(pos_logsum, neg_logsum), min(pos_logsum, neg_logsum)),
-    ifelse(pos_logsum > neg_logsum, 1, -1)
-  ))
-}